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.github/workflows/sync-to-huggingface.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          submodules: recursive
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://Sadashiv:[email protected]/spaces/Sadashiv/CropGaurd main 

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+main.py
+extra_notebook
+notebok

.gitmodules

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+[submodule "Fertilizer-Recommendation"]
+	path = Fertilizer-Recommendation
+	url = https://github.com/07Sada/Fertilizer-Recommendation.git
+[submodule "crop-recommendation"]
+	path = crop-recommendation
+	url = https://github.com/07Sada/crop-recommendation.git
+[submodule "plant-diseases-classifier"]
+	path = plant-diseases-classifier
+	url = https://github.com/07Sada/plant-diseases-classifier.git

.vscode/extensions.json

@@ -0,0 +1,10 @@
+{
+  "recommendations": [
+    "mongodb.mongodb-vscode",
+    "ms-python.python",
+    "ms-toolsai.jupyter",
+    "ms-toolsai.jupyter-keymap",
+    "ms-toolsai.jupyter-renderers",
+    "formulahendry.code-runner"
+  ]
+}

.vscode/settings.json

@@ -0,0 +1,8 @@
+{
+  "workbench.colorTheme": "Cobalt2",
+  "workbench.preferredDarkColorTheme": "Default Dark+",
+  "task.allowAutomaticTasks": "on",
+  "workbench.editorAssociations": {
+    "*.md": "vscode.markdown.preview.editor"
+  }
+}

.vscode/tasks.json

@@ -0,0 +1,15 @@
+{
+  "version": "2.0.0",
+  "tasks": [
+    {
+      "label": "Installing extensions and dependencies...",
+      "type": "shell",
+      "command": "code-server --install-extension mongodb.mongodb-vscode --install-extension ms-python.python --install-extension formulahendry.code-runner && pip install -r requirements.txt",
+      "presentation": {
+        "reveal": "always",
+        "panel": "new"
+      },
+      "runOptions": { "runOn": "folderOpen" }
+    }
+  ]
+}

Fertilizer-Recommendation/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+data_dump.py
+dataset_download.py
+demo.ipynb
+
+artifact
+catboost_info

Fertilizer-Recommendation/.vscode/extensions.json

@@ -0,0 +1,13 @@
+{
+  "recommendations": [
+    "mongodb.mongodb-vscode",
+    "ms-python.python",
+    "ms-toolsai.jupyter",
+    "ms-toolsai.jupyter-keymap",
+    "ms-toolsai.jupyter-renderers",
+    "formulahendry.code-runner",
+    "wesbos.theme-cobalt2",
+    "PKief.material-icon-theme",
+    "wesbos.theme-cobalt2"
+  ]
+}

Fertilizer-Recommendation/.vscode/settings.json

@@ -0,0 +1,8 @@
+{
+  "workbench.colorTheme": "Cobalt2",
+  "workbench.preferredDarkColorTheme": "Cobalt2",
+  "task.allowAutomaticTasks": "on",
+  "workbench.editorAssociations": {
+    "*.md": "vscode.markdown.preview.editor"
+  }
+}

Fertilizer-Recommendation/.vscode/tasks.json

@@ -0,0 +1,15 @@
+{
+  "version": "2.0.0",
+  "tasks": [
+    {
+      "label": "Installing extensions and dependencies...",
+      "type": "shell",
+      "command": "code-server --install-extension mongodb.mongodb-vscode --install-extension ms-python.python --install-extension formulahendry.code-runner && pip install -r requirements.txt",
+      "presentation": {
+        "reveal": "always",
+        "panel": "new"
+      },
+      "runOptions": { "runOn": "folderOpen" }
+    }
+  ]
+}

Fertilizer-Recommendation/LICENSE

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+MIT License
+
+Copyright (c) 2023 Sadashiv Nandanikar
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Fertilizer-Recommendation/README.md

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+# Fertilizer Recommendation
+### Powered by machine learning models, evaluates input factors to provide precise fertilizer recommendations, enhancing crop health and productivity. 
+
+## Demo
+### Input Interface
+<img src="https://github.com/07Sada/Fertilizer-Recommendation/assets/112761379/7623ab94-c7c8-4343-814d-863849e67850" alt="Image 1" width="800">
+
+### Output Interface
+<img src="https://github.com/07Sada/Fertilizer-Recommendation/assets/112761379/45e3dee7-f21d-4570-8df8-56e8cf2a9538" alt="Image 1" width="800">
+
+## Data Source
+This dataset contains information about the soil, environmental conditions, crop type, and fertilizer use for different crops. The dataset includes the following columns:
+
+- `Temparature`: The temperature in degrees Celsius.
+- `Humidity`: The relative humidity in percent.
+- `Moisture`: The moisture content of the soil in percent.
+- `Soil Type`: The type of soil.
+- `Crop Type`: The type of crop.
+- `Nitrogen`: The amount of nitrogen in the soil in kilograms per hectare.
+- `Potassium`: The amount of potassium in the soil in kilograms per hectare.
+- `Phosphorous`: The amount of phosphorus in the soil in kilograms per hectare.
+- `Fertilizer Name`: The name of the fertilizer used.
+
+[Link](https://www.kaggle.com/datasets/gdabhishek/fertilizer-prediction) for the dataset
+
+<details>
+  <summary>Supported fertilizer
+</summary>
+
+- UREA
+- DAP
+- 14-35-14
+- 28-28
+- 17-17-17
+- 20-20
+- 10-26-26
+</details>
+
+## Project Details
+This repository is submodule for [CropGaurd](https://github.com/07Sada/CropGaurd.git)
+
+## Project PipeLine Stages
+![Project PipeLine Stages](https://user-images.githubusercontent.com/112761379/225940480-2a7381b2-6abd-4c1c-8287-0fd49099be8c.jpg)

Fertilizer-Recommendation/fertilizer-prediction/Fertilizer Prediction.csv

@@ -0,0 +1,100 @@
+Temparature,Humidity ,Moisture,Soil Type,Crop Type,Nitrogen,Potassium,Phosphorous,Fertilizer Name
+26,52,38,Sandy,Maize,37,0,0,Urea
+29,52,45,Loamy,Sugarcane,12,0,36,DAP
+34,65,62,Black,Cotton,7,9,30,14-35-14
+32,62,34,Red,Tobacco,22,0,20,28-28
+28,54,46,Clayey,Paddy,35,0,0,Urea
+26,52,35,Sandy,Barley,12,10,13,17-17-17
+25,50,64,Red,Cotton,9,0,10,20-20
+33,64,50,Loamy,Wheat,41,0,0,Urea
+30,60,42,Sandy,Millets,21,0,18,28-28
+29,58,33,Black,Oil seeds,9,7,30,14-35-14
+27,54,28,Clayey,Pulses,13,0,40,DAP
+31,62,48,Sandy,Maize,14,15,12,17-17-17
+25,50,65,Loamy,Cotton,36,0,0,Urea
+32,62,41,Clayey,Paddy,24,0,22,28-28
+26,52,31,Red,Ground Nuts,14,0,41,DAP
+31,62,49,Black,Sugarcane,10,13,14,17-17-17
+33,64,34,Clayey,Pulses,38,0,0,Urea
+25,50,39,Sandy,Barley,21,0,19,28-28
+28,54,65,Black,Cotton,39,0,0,Urea
+29,58,52,Loamy,Wheat,13,0,36,DAP
+30,60,44,Sandy,Millets,10,0,9,20-20
+34,65,53,Loamy,Sugarcane,12,14,12,17-17-17
+35,68,33,Red,Tobacco,11,0,37,DAP
+28,54,37,Black,Millets,36,0,0,Urea
+33,64,39,Clayey,Paddy,13,0,10,20-20
+26,52,44,Sandy,Maize,23,0,20,28-28
+30,60,63,Red,Cotton,9,9,29,14-35-14
+32,62,30,Loamy,Sugarcane,38,0,0,Urea
+37,70,32,Black,Oil seeds,12,0,39,DAP
+26,52,36,Clayey,Pulses,14,0,13,20-20
+29,58,40,Red,Ground Nuts,24,0,23,28-28
+30,60,27,Loamy,Sugarcane,12,0,40,DAP
+34,65,38,Clayey,Paddy,39,0,0,Urea
+36,68,38,Sandy,Barley,7,9,30,14-35-14
+26,52,48,Loamy,Wheat,23,0,19,28-28
+28,54,35,Black,Millets,41,0,0,Urea
+30,60,61,Loamy,Cotton,8,10,31,14-35-14
+37,70,37,Clayey,Paddy,12,0,41,DAP
+25,50,26,Red,Ground Nuts,15,14,11,17-17-17
+29,58,34,Sandy,Millets,15,0,37,DAP
+27,54,30,Clayey,Pulses,13,0,13,20-20
+30,60,58,Loamy,Sugarcane,10,7,32,14-35-14
+32,62,34,Red,Tobacco,22,0,24,28-28
+34,65,60,Black,Sugarcane,35,0,0,Urea
+35,67,42,Sandy,Barley,10,0,35,DAP
+38,70,48,Loamy,Wheat,8,8,28,14-35-14
+26,52,32,Black,Oil seeds,12,0,8,20-20
+29,58,43,Clayey,Paddy,24,0,18,28-28
+30,60,29,Red,Ground Nuts,41,0,0,Urea
+33,64,51,Sandy,Maize,5,9,29,14-35-14
+34,65,31,Red,Tobacco,23,0,21,28-28
+36,68,33,Black,Oil seeds,13,0,14,20-20
+28,54,38,Clayey,Pulses,40,0,0,Urea
+30,60,47,Sandy,Barley,12,0,42,DAP
+31,62,63,Red,Cotton,11,12,15,17-17-17
+27,53,43,Black,Millets,23,0,24,28-28
+34,65,54,Loamy,Wheat,38,0,0,Urea
+29,58,37,Sandy,Millets,8,0,15,20-20
+25,50,56,Loamy,Sugarcane,11,13,15,17-17-17
+32,62,34,Red,Ground Nuts,15,0,37,DAP
+28,54,41,Clayey,Paddy,36,0,0,Urea
+30,60,49,Loamy,Wheat,13,0,9,20-20
+34,65,64,Black,Cotton,24,0,20,28-28
+28,54,47,Sandy,Barley,5,18,15,10-26-26
+27,53,35,Black,Oil seeds,37,0,0,Urea
+36,68,62,Red,Cotton,15,0,40,DAP
+34,65,57,Black,Sugarcane,9,0,13,20-20
+29,58,55,Loamy,Sugarcane,8,8,33,14-35-14
+25,50,40,Clayey,Pulses,6,19,16,10-26-26
+30,60,38,Sandy,Millets,10,0,14,20-20
+26,52,39,Clayey,Pulses,21,0,23,28-28
+31,62,32,Red,Tobacco,39,0,0,Urea
+34,65,48,Loamy,Wheat,23,0,19,28-28
+27,53,34,Black,Oil seeds,42,0,0,Urea
+33,64,31,Red,Ground Nuts,13,0,39,DAP
+29,58,42,Clayey,Paddy,9,10,22,14-35-14
+30,60,47,Sandy,Maize,22,0,21,28-28
+27,53,59,Loamy,Sugarcane,10,0,15,20-20
+26,52,36,Clayey,Pulses,7,16,20,10-26-26
+34,65,63,Red,Cotton,14,0,38,DAP
+28,54,43,Clayey,Paddy,10,8,29,14-35-14
+30,60,40,Sandy,Millets,41,0,0,Urea
+29,58,65,Black,Cotton,14,0,35,DAP
+26,52,59,Loamy,Sugarcane,11,0,9,20-20
+31,62,44,Sandy,Barley,21,0,28,28-28
+35,67,28,Clayey,Pulses,8,7,31,14-35-14
+29,58,30,Red,Tobacco,13,17,16,10-26-26
+27,53,30,Black,Millets,35,0,0,Urea
+36,68,50,Loamy,Wheat,12,18,19,10-26-26
+29,58,61,Loamy,Cotton,11,0,38,DAP
+30,60,26,Black,Oil seeds,8,9,30,14-35-14
+34,65,45,Clayey,Paddy,6,19,21,10-26-26
+36,68,41,Red,Ground Nuts,41,0,0,Urea
+28,54,25,Sandy,Maize,9,10,30,14-35-14
+25,50,32,Clayey,Pulses,24,0,19,28-28
+30,60,27,Red,Tobacco,4,17,17,10-26-26
+38,72,51,Loamy,Wheat,39,0,0,Urea
+36,60,43,Sandy,Millets,15,0,41,DAP
+29,58,57,Black,Sugarcane,12,0,10,20-20

Fertilizer-Recommendation/main.py

@@ -0,0 +1,8 @@
+from src.pipeline.training_pipeline import start_training_pipeline
+
+if __name__ =="__main__":
+    try:    
+        start_training_pipeline()
+    
+    except Exception as e:
+        print(e)

Fertilizer-Recommendation/notebook/fertilizer-prediction.ipynb

@@ -0,0 +1,736 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np \n",
+    "import matplotlib.pyplot as plt \n",
+    "import seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, roc_auc_score\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from xgboost import XGBClassifier\n",
+    "from catboost import CatBoostClassifier\n",
+    "\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "# Ignore warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir(\"/config/workspace\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Temparature</th>\n",
+       "      <th>Humidity</th>\n",
+       "      <th>Moisture</th>\n",
+       "      <th>Soil Type</th>\n",
+       "      <th>Crop Type</th>\n",
+       "      <th>Nitrogen</th>\n",
+       "      <th>Potassium</th>\n",
+       "      <th>Phosphorous</th>\n",
+       "      <th>Fertilizer Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>26</td>\n",
+       "      <td>52</td>\n",
+       "      <td>38</td>\n",
+       "      <td>Sandy</td>\n",
+       "      <td>Maize</td>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Urea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>29</td>\n",
+       "      <td>52</td>\n",
+       "      <td>45</td>\n",
+       "      <td>Loamy</td>\n",
+       "      <td>Sugarcane</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>36</td>\n",
+       "      <td>DAP</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>34</td>\n",
+       "      <td>65</td>\n",
+       "      <td>62</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>Cotton</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9</td>\n",
+       "      <td>30</td>\n",
+       "      <td>14-35-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>32</td>\n",
+       "      <td>62</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Red</td>\n",
+       "      <td>Tobacco</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>28-28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>28</td>\n",
+       "      <td>54</td>\n",
+       "      <td>46</td>\n",
+       "      <td>Clayey</td>\n",
+       "      <td>Paddy</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Urea</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Temparature  Humidity   Moisture Soil Type  Crop Type  Nitrogen  Potassium  \\\n",
+       "0           26         52        38     Sandy      Maize        37          0   \n",
+       "1           29         52        45     Loamy  Sugarcane        12          0   \n",
+       "2           34         65        62     Black     Cotton         7          9   \n",
+       "3           32         62        34       Red    Tobacco        22          0   \n",
+       "4           28         54        46    Clayey      Paddy        35          0   \n",
+       "\n",
+       "   Phosphorous Fertilizer Name  \n",
+       "0            0            Urea  \n",
+       "1           36             DAP  \n",
+       "2           30        14-35-14  \n",
+       "3           20           28-28  \n",
+       "4            0            Urea  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "FILE_PATH =r\"fertilizer-prediction/Fertilizer Prediction.csv\"\n",
+    "\n",
+    "# Loading the dataset into pandas\n",
+    "df = pd.read_csv(FILE_PATH)\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the dataset: (99, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Shape of the dataset: {df.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 99 entries, 0 to 98\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column           Non-Null Count  Dtype \n",
+      "---  ------           --------------  ----- \n",
+      " 0   Temparature      99 non-null     int64 \n",
+      " 1   Humidity         99 non-null     int64 \n",
+      " 2   Moisture         99 non-null     int64 \n",
+      " 3   Soil Type        99 non-null     object\n",
+      " 4   Crop Type        99 non-null     object\n",
+      " 5   Nitrogen         99 non-null     int64 \n",
+      " 6   Potassium        99 non-null     int64 \n",
+      " 7   Phosphorous      99 non-null     int64 \n",
+      " 8   Fertilizer Name  99 non-null     object\n",
+      "dtypes: int64(6), object(3)\n",
+      "memory usage: 7.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# datatypes \n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Temparature        0\n",
+       "Humidity           0\n",
+       "Moisture           0\n",
+       "Soil Type          0\n",
+       "Crop Type          0\n",
+       "Nitrogen           0\n",
+       "Potassium          0\n",
+       "Phosphorous        0\n",
+       "Fertilizer Name    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking for null values \n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# checking the data distribution \n",
+    "plt.figure(figsize=(10, 7))\n",
+    "\n",
+    "sns.countplot(data=df, x='Fertilizer Name')\n",
+    "plt.title(\"Dataset Distribution\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Sandy', 'Loamy', 'Black', 'Red', 'Clayey'], dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# soil type\n",
+    "df['Soil Type'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Maize', 'Sugarcane', 'Cotton', 'Tobacco', 'Paddy', 'Barley',\n",
+       "       'Wheat', 'Millets', 'Oil seeds', 'Pulses', 'Ground Nuts'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Crop Type'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the training dataset: (79, 8)\n",
+      "Shape of the testing dataset: (20, 8)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# splitting the dataset \n",
+    "X = df.drop(columns=[\"Fertilizer Name\"])\n",
+    "y = df[\"Fertilizer Name\"]\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(f\"Shape of the training dataset: {X_train.shape}\")\n",
+    "print(f\"Shape of the testing dataset: {X_test.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Temparature', 'Humidity ', 'Moisture', 'Nitrogen', 'Potassium', 'Phosphorous']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# numerical columns in the dataset\n",
+    "print(df._get_numeric_data().columns.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Soil Type', 'Crop Type']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# segrating categorical columns\n",
+    "categorical_columns = [i for i in df.columns if (i not in df._get_numeric_data().columns) & (i !='Fertilizer Name')]\n",
+    "categorical_columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ohe = OneHotEncoder(drop='first')\n",
+    "standard_scaler = StandardScaler()\n",
+    "\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers =[\n",
+    "        ('StandaradScaling', standard_scaler, df._get_numeric_data().columns),\n",
+    "        ('One_hot_encoding', ohe, categorical_columns)\n",
+    "    ],\n",
+    "    remainder='passthrough'\n",
+    ")\n",
+    "\n",
+    "pipeline = Pipeline([\n",
+    "    ('preprocess', preprocessor)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_transformed = pipeline.fit_transform(X_train)\n",
+    "X_test_transformed = pipeline.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "le = LabelEncoder()\n",
+    "\n",
+    "y_train_transformed = le.fit_transform(y_train)\n",
+    "y_test_transformed = le.transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_clf(true, predicted):\n",
+    "    '''\n",
+    "    This function takes in true values and predicted values\n",
+    "    Returns: Accuracy, F1-Score, Precision, Recall, Roc-auc Score\n",
+    "    '''\n",
+    "    acc = accuracy_score(true, predicted)\n",
+    "    f1 = f1_score(true, predicted, average='weighted')\n",
+    "    precision = precision_score(true, predicted, average='weighted')\n",
+    "    recall = recall_score(true, predicted, average='weighted')\n",
+    "    \n",
+    "    return acc, f1, precision, recall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a function which can evaluate models and returns a report \n",
+    "def evaluate_model(X_train, X_test, y_train, y_test, models):\n",
+    "    '''\n",
+    "    This function takes X_train, X_test, y_train, y_test  and models dictionary as input\n",
+    "    Iterate through the given model directory and evaluate metrics\n",
+    "\n",
+    "    Returns:\n",
+    "    DataFrame which contains report of all models metrics \n",
+    "    '''\n",
+    "\n",
+    "    model_list = []\n",
+    "    metric_list = []\n",
+    "\n",
+    "    for i in range(len(list(models))):\n",
+    "        model = list(models.values())[i]\n",
+    "        model.fit(X_train, y_train)\n",
+    "\n",
+    "        # Make predictions\n",
+    "        y_train_pred = model.predict(X_train)\n",
+    "        y_test_pred = model.predict(X_test)\n",
+    "\n",
+    "        # Training set performances\n",
+    "        model_train_accuracy, model_train_f1, model_train_precision, \\\n",
+    "            model_train_recall = evaluate_clf(y_train, y_train_pred)\n",
+    "\n",
+    "        # Test set peformances \n",
+    "        model_test_accuracy, model_test_f1, model_test_precision, \\\n",
+    "            model_test_recall = evaluate_clf(y_test, y_test_pred)\n",
+    "\n",
+    "        print(list(models.keys())[i])\n",
+    "        model_list.append(list(models.keys())[i])\n",
+    "\n",
+    "        result_dict ={'model_name':list(models.keys())[i], \n",
+    "                    \"train_accuracy\": model_train_accuracy, \"test_accuracy\": model_test_accuracy,\n",
+    "                    \"train_precision\": model_train_precision, \"test_precision\": model_test_precision,\n",
+    "                    'train_recall': model_train_recall, \"test_recall\":model_test_recall,\n",
+    "                    \"train_f1_score\": model_train_f1, \"test_f1_score\": model_test_f1}\n",
+    "\n",
+    "        metric_list.append(result_dict)\n",
+    "\n",
+    "    \n",
+    "    return metric_list\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Model Dictionary\n",
+    "models = {\n",
+    "    \"Random Forest\": RandomForestClassifier(),\n",
+    "    \"Decision Tree\": DecisionTreeClassifier(),\n",
+    "    \"Gradient Boosting\": GradientBoostingClassifier(),\n",
+    "     \"K-Neighbors Classifier\": KNeighborsClassifier(),\n",
+    "    \"XGBClassifier\": XGBClassifier(), \n",
+    "     \"CatBoosting Classifier\": CatBoostClassifier(verbose=False),\n",
+    "    \"AdaBoost Classifier\": AdaBoostClassifier()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random Forest\n",
+      "Decision Tree\n",
+      "Gradient Boosting\n",
+      "K-Neighbors Classifier\n",
+      "XGBClassifier\n",
+      "CatBoosting Classifier\n",
+      "AdaBoost Classifier\n"
+     ]
+    }
+   ],
+   "source": [
+    "resultant_metrics = evaluate_model(X_train_transformed, X_test_transformed, y_train_transformed, y_test_transformed, models)\n",
+    "\n",
+    "resultant_metrics_df = pd.DataFrame(data=resultant_metrics)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model_name</th>\n",
+       "      <th>train_accuracy</th>\n",
+       "      <th>test_accuracy</th>\n",
+       "      <th>train_precision</th>\n",
+       "      <th>test_precision</th>\n",
+       "      <th>train_recall</th>\n",
+       "      <th>test_recall</th>\n",
+       "      <th>train_f1_score</th>\n",
+       "      <th>test_f1_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Decision Tree</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>XGBClassifier</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CatBoosting Classifier</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.966667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Gradient Boosting</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.975000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.955556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>AdaBoost Classifier</td>\n",
+       "      <td>0.594937</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.477918</td>\n",
+       "      <td>0.657143</td>\n",
+       "      <td>0.594937</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.504147</td>\n",
+       "      <td>0.662500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>K-Neighbors Classifier</td>\n",
+       "      <td>0.898734</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.904539</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.898734</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0.897599</td>\n",
+       "      <td>0.647727</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               model_name  train_accuracy  test_accuracy  train_precision  \\\n",
+       "1           Decision Tree        1.000000           1.00         1.000000   \n",
+       "4           XGBClassifier        1.000000           1.00         1.000000   \n",
+       "5  CatBoosting Classifier        1.000000           1.00         1.000000   \n",
+       "0           Random Forest        1.000000           0.95         1.000000   \n",
+       "2       Gradient Boosting        1.000000           0.95         1.000000   \n",
+       "6     AdaBoost Classifier        0.594937           0.70         0.477918   \n",
+       "3  K-Neighbors Classifier        0.898734           0.65         0.904539   \n",
+       "\n",
+       "   test_precision  train_recall  test_recall  train_f1_score  test_f1_score  \n",
+       "1        1.000000      1.000000         1.00        1.000000       1.000000  \n",
+       "4        1.000000      1.000000         1.00        1.000000       1.000000  \n",
+       "5        1.000000      1.000000         1.00        1.000000       1.000000  \n",
+       "0        1.000000      1.000000         0.95        1.000000       0.966667  \n",
+       "2        0.975000      1.000000         0.95        1.000000       0.955556  \n",
+       "6        0.657143      0.594937         0.70        0.504147       0.662500  \n",
+       "3        0.666667      0.898734         0.65        0.897599       0.647727  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resultant_metrics_df = resultant_metrics_df.sort_values(by='test_f1_score', ascending=False)\n",
+    "resultant_metrics_df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.10 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Fertilizer-Recommendation/requirements.txt

@@ -0,0 +1,58 @@
+asttokens==2.2.1
+backcall==0.2.0
+bleach==6.0.0
+certifi==2023.7.22
+charset-normalizer==3.2.0
+click==8.1.6
+comm==0.1.3
+debugpy==1.6.7
+decorator==5.1.1
+dnspython==2.4.1
+executing==1.2.0
+idna==3.4
+importlib-metadata==6.8.0
+ipykernel==6.25.0
+ipython==8.12.2
+jedi==0.18.2
+jupyter-client==8.3.0
+jupyter-core==5.3.1
+kaggle==1.5.16
+matplotlib-inline==0.1.6
+nest-asyncio==1.5.6
+numpy==1.24.4
+opendatasets==0.1.22
+packaging==23.1
+pandas==2.0.3
+parso==0.8.3
+pexpect==4.8.0
+pickleshare==0.7.5
+platformdirs==3.9.1
+prompt-toolkit==3.0.39
+psutil==5.9.5
+ptyprocess==0.7.0
+pure-eval==0.2.2
+Pygments==2.15.1
+pymongo==4.4.1
+python-dateutil==2.8.2
+python-dotenv==1.0.0
+python-slugify==8.0.1
+pytz==2023.3
+pyzmq==25.1.0
+requests==2.31.0
+six==1.16.0
+stack-data==0.6.2
+text-unidecode==1.3
+tornado==6.3.2
+tqdm==4.65.0
+traitlets==5.9.0
+typing-extensions==4.7.1
+tzdata==2023.3
+urllib3==2.0.4
+wcwidth==0.2.6
+webencodings==0.5.1
+zipp==3.16.2
+scikit-learn
+matplotlib
+seaborn
+pyyaml
+dill

Fertilizer-Recommendation/saved_models/0/model/model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c89526de70ba6f924e11e82a344eb581e001228120c575fc73372179b91297ed
+size 2808

Fertilizer-Recommendation/saved_models/0/target_encoder/target_encoder.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60d651fde77ec9ae2d704442e566129721089259fef449d7e81620ac286ddc9d
+size 338

Fertilizer-Recommendation/saved_models/0/transformer/transformer.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:427b030f88db787db36098d667cd6fb75e6e6a1d8bb6b504d47d2124b3a10a20
+size 2323

Fertilizer-Recommendation/src/components/data_ingestion.py

@@ -0,0 +1,69 @@
+from src.entity import config_entity 
+from src.entity import artifact_entity
+from src.logger import logging 
+from src.exception import FertilizerException
+from src import utils
+
+from sklearn.model_selection import train_test_split
+import numpy as np 
+import pandas as pd 
+import sys 
+import os
+
+class DataIngestion:
+
+    def __init__(self, data_ingestion_config:config_entity.DataIngestionConfig):
+        try:
+            logging.info(f"\n\n{'>'*50} Data Ingestion {'<'*50}\n")
+            self.data_ingestion_config = data_ingestion_config
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    def initiate_data_ingestion(self) -> artifact_entity.DataIngestionArtifact:
+        try:
+            logging.info(f"Exporting collection data as pandas Dataframe ")
+
+            df: pd.DataFrame = utils.get_collection_as_dataframe(
+                database_name=self.data_ingestion_config.database_name, 
+                collection_name=self.data_ingestion_config.collection_name)
+
+            logging.info(f"Saving data in feature store")
+
+            feature_store_dir = os.path.dirname(self.data_ingestion_config.feature_store_file_path)
+            os.makedirs(feature_store_dir, exist_ok=True)
+
+            logging.info(f"Saving dataframe into feature store")
+            df.to_csv(path_or_buf=self.data_ingestion_config.feature_store_file_path,
+                        index=False,
+                        header=True)
+                
+            logging.info(f"Split the dataset into train and test")
+            train_df, test_df = train_test_split(
+                df, test_size=self.data_ingestion_config.test_size, random_state=42
+            )
+
+            logging.info(f"Create dataset directory if not available")
+            dataset_dir = os.path.dirname(self.data_ingestion_config.train_file_path)
+            os.makedirs(dataset_dir, exist_ok=True)
+
+            logging.info(f"Save df to feature store folder")
+            train_df.to_csv(path_or_buf=self.data_ingestion_config.train_file_path,
+                            index=False,
+                            header=True)
+            
+            test_df.to_csv(path_or_buf=self.data_ingestion_config.test_file_path,
+                            index=False,
+                            header=True)
+
+            data_ingestion_artifact = artifact_entity.DataIngestionArtifact(
+                feature_store_file_path=self.data_ingestion_config.feature_store_file_path, 
+                train_file_path=self.data_ingestion_config.train_file_path, 
+                test_file_path=self.data_ingestion_config.test_file_path)
+
+            logging.info(f"Data Ingestion Completed. Artifacts saved")
+            
+            return data_ingestion_artifact
+        
+        except Exception as e:
+            raise FertilizerException(e, sys)

Fertilizer-Recommendation/src/components/data_transformation.py

@@ -0,0 +1,116 @@
+from src.entity import config_entity
+from src.entity import artifact_entity
+from src.logger import logging
+from src.exception import FertilizerException
+from typing import Optional
+from src import utils
+import sys
+import os
+from src.config import TARGET_COLUMN
+from src.config import NUMERICAL_FEATURES
+from src.config import CATEGORICAL_FEATURES
+from src.config import BASE_FILE_PATH
+
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import ColumnTransformer
+import pandas as pd 
+import numpy as np
+
+class DataTransformation:
+
+    def __init__(
+        self,
+        data_transformation_config:config_entity.DataTransformationConfig,
+        data_ingestion_artifact:artifact_entity.DataIngestionArtifact
+    ):
+
+        try:
+            logging.info(f"\n\n{'>'*50} Data transformation Initiated {'<'*50}\n")
+            self.data_transformation_config = data_transformation_config
+            self.data_ingestion_artifact = data_ingestion_artifact
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+    
+    @classmethod
+    def get_data_transformer_object(cls) -> Pipeline:
+        try:
+            standard_scaler = StandardScaler()
+            one_hot_encoder = OneHotEncoder(drop='first')
+
+            numerical_indices, categorical_indices = utils.get_column_indices(
+                numerical_features=NUMERICAL_FEATURES, 
+                categorical_features=CATEGORICAL_FEATURES, 
+                base_file_path=BASE_FILE_PATH)
+
+            preprocessor = ColumnTransformer(
+            transformers =[
+                ('StandaradScaling', standard_scaler, numerical_indices),
+                ('One_hot_encoding', one_hot_encoder, categorical_indices)
+            ],
+            remainder='passthrough'
+            )
+
+            pipeline = Pipeline([
+                                ('preprocess', preprocessor)
+                                ])
+
+            return pipeline 
+        
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    def initiate_data_transformation(self) -> artifact_entity.DataTransformationArtifact:
+        try:
+            # reading training and testing files 
+            train_df = pd.read_csv(self.data_ingestion_artifact.train_file_path)
+            test_df = pd.read_csv(self.data_ingestion_artifact.test_file_path)
+
+            # selecting input features for train and test dataframe
+            input_feature_train_df = train_df.drop(TARGET_COLUMN, axis=1)
+            input_feature_test_df = test_df.drop(TARGET_COLUMN, axis=1)
+
+            # selecting target feature for train and test dataframe
+            target_feature_train_df = train_df[TARGET_COLUMN]
+            target_feature_test_df = test_df[TARGET_COLUMN]
+
+            label_encoder = LabelEncoder()
+            label_encoder.fit(target_feature_train_df)
+
+            # transformation on target column
+            target_feature_train_arr = label_encoder.transform(target_feature_train_df)
+            target_feature_test_arr = label_encoder.transform(target_feature_test_df)
+
+            # transforming input features
+            transformation_pipeline = DataTransformation.get_data_transformer_object()
+            transformation_pipeline.fit(input_feature_train_df)
+
+            input_feature_train_arr = transformation_pipeline.transform(input_feature_train_df)
+            input_feature_test_arr = transformation_pipeline.transform(input_feature_test_df)
+
+            train_arr = np.c_[input_feature_train_arr, target_feature_train_arr]
+            test_arr = np.c_[input_feature_test_arr, target_feature_test_arr]
+
+            # save the numpy array
+            utils.save_object(file_path=self.data_transformation_config.transformed_train_path, obj=train_arr)
+            utils.save_object(file_path=self.data_transformation_config.transformed_test_path, obj=test_arr)
+
+            utils.save_object(file_path=self.data_transformation_config.transform_object_path, obj=transformation_pipeline)
+
+            utils.save_object(file_path=self.data_transformation_config.target_encoder_path, obj=label_encoder)
+
+            data_transformation_artifact = artifact_entity.DataTransformationArtifact(
+                transform_object_path = self.data_transformation_config.transform_object_path, 
+                transformed_train_path = self.data_transformation_config.transformed_train_path, 
+                transformed_test_path = self.data_transformation_config.transformed_test_path, 
+                target_encoder_path = self.data_transformation_config.target_encoder_path)
+
+            logging.info(f"Data transformation Completed")
+
+            return data_transformation_artifact
+
+        except Exception as e:
+            raise FertilizerException(e, sys)

Fertilizer-Recommendation/src/components/data_validation.py

@@ -0,0 +1,145 @@
+from src.entity import config_entity
+from src.entity import artifact_entity
+from src.logger import logging 
+from src.exception import FertilizerException
+from src.config import TARGET_COLUMN
+from src import utils
+
+from typing import Optional
+from scipy.stats import ks_2samp
+import pandas as pd 
+import numpy as np 
+import os
+import sys
+
+class DataValidation:
+
+    def __init__(
+        self, 
+        data_validation_config: config_entity.DataValidationConfig,
+        data_ingestion_arfitact: artifact_entity.DataIngestionArtifact
+        ):
+    
+        try:
+            logging.info(f"\n\n{'>'*50} Data Validation Initiated {'<'*50}\n")
+            self.data_validation_config = data_validation_config
+            self.data_ingestion_arfitact = data_ingestion_arfitact
+            self.validation_error = dict()
+        
+        except Exception as e:
+            raise FertilizerException(e, sys) 
+
+    def is_required_colums_exists(
+        self,
+        base_df: pd.DataFrame,
+        current_df: pd.DataFrame,
+        report_key_name: str
+    ) -> bool:
+        try:
+            base_columns = base_df.columns
+            current_columns = current_df.columns
+
+            missing_columns = []
+            for base_column in base_columns:
+                if base_column not in current_columns:
+                    logging.info(f"Column: {base_column} is not available")
+                    missing_columns.append(base_column)
+
+            if len(missing_columns) > 0:
+                self.validation_error[report_key_name] = missing_columns
+                return False
+
+            return True
+    
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    def data_drift(
+        self,
+        base_df: pd.DataFrame,
+        current_df: pd.DataFrame,
+        report_key_name: str
+    ):
+        try:
+            drift_report = dict()
+
+            base_columns = base_df.columns
+            current_columns = current_df.columns
+
+            for base_column in base_columns:
+                base_data, current_data = base_df[base_column], current_df[base_column]
+
+                # Null hypothesis is that both column data drawn from same distribution
+
+                logging.info(f"Hypothesis {base_column}: {base_data.dtype}, {current_data.dtype}")
+                same_distribution = ks_2samp(base_data, current_data)
+
+                if same_distribution.pvalue > 0.05:
+                    # we are accepting the null hypothesis
+                    drift_report[base_column] = {
+                        "pvalue": float(same_distribution.pvalue),
+                        "same_distribution":True
+                    }
+                
+                else:
+                    drift_report[base_column] = {
+                        "pvalue": float(same_distribution.pvalue),
+                        "same_distribution":False
+                    }
+            self.validation_error[report_key_name] = drift_report
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+    
+    def initiate_data_validation(self) -> artifact_entity.DataValidationArtifact:
+        try:
+            logging.info(f"Reading base dataframe")
+            base_df = pd.read_csv(self.data_validation_config.base_file_path)
+
+            logging.info(f"Reading train dataframe")
+            train_df = pd.read_csv(self.data_ingestion_arfitact.train_file_path)
+
+            logging.info(f"Reading test dataframe")
+            test_df = pd.read_csv(self.data_ingestion_arfitact.test_file_path)
+
+            exclude_column = [TARGET_COLUMN]
+            base_df = utils.seperate_dependant_column(df=base_df, exclude_column=exclude_column)
+            train_df = utils.seperate_dependant_column(df=train_df, exclude_column=exclude_column)
+            test_df = utils.seperate_dependant_column(df=test_df, exclude_column=exclude_column)
+
+            logging.info(f"Is all required columns present in the train_df")
+            train_df_columns_status = self.is_required_colums_exists(
+                base_df=base_df, 
+                current_df=train_df, 
+                report_key_name='missing_columns_within_train_dataset')
+            
+            test_df_columns_status = self.is_required_colums_exists(
+                base_df=base_df, 
+                current_df=test_df, 
+                report_key_name='missing_columns_within_test_dataset')
+
+            if train_df_columns_status:
+                logging.info(f"As all colum are availabel in train_df hence detecting data drift")
+
+                self.data_drift(base_df=base_df, current_df=train_df, report_key_name='data_drift_within_train_dataset')
+
+            if test_df_columns_status:
+                logging.info(f"As all columns are availabel in test_df hence detecting data drift")
+
+                self.data_drift(base_df=base_df, current_df=test_df, report_key_name='data_drift_within_test_dataset')
+
+            # writting the report 
+            logging.info(f"Writing report in yaml format")
+            utils.write_yaml_file(
+                file_path=self.data_validation_config.report_file_path, 
+                data=self.validation_error)
+
+            data_validation_artifact = artifact_entity.DataValidationArtifact(
+                report_file_path=self.data_validation_config.report_file_path)
+
+            logging.info(f"Data Vadidation Completed. Artifacts saved")
+
+            return data_validation_artifact
+        
+        except Exception as e:
+            raise FertilizerException(e, sys)

Fertilizer-Recommendation/src/components/model_evaluation.py

@@ -0,0 +1,108 @@
+from src.predictor import ModelResolver
+from src.entity import config_entity
+from src.entity import artifact_entity
+from src.logger import logging
+from src.exception import FertilizerException
+from src.utils import load_object
+
+from src.config import TARGET_COLUMN
+
+from sklearn.metrics import f1_score
+import pandas as pd 
+import numpy as np 
+import os 
+import sys 
+
+class ModelEvaluation:
+
+    def __init__(
+        self,
+        model_eval_config: config_entity.ModelEvaluationConfig,
+        data_ingestion_artifact: artifact_entity.DataIngestionArtifact,
+        data_transformation_artifact: artifact_entity.DataTransformationArtifact,
+        model_trainer_artifact: artifact_entity.ModelTrainerArtifact
+    ):
+
+        try:
+            logging.info(f"\n\n{'>'*50} Model Evaluation Initiated {'<'*50}\n")
+            self.model_eval_config = model_eval_config
+            self.data_ingestion_artifact = data_ingestion_artifact
+            self.data_transformation_artifact = data_transformation_artifact
+            self.model_trainer_artifact = model_trainer_artifact
+            self.model_resolver = ModelResolver()
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    
+    def initiate_model_evaluation(self) -> artifact_entity.ModelEvaluationArtifact:
+        try:
+            logging.info(f"If the saved model directory contains a model, we will compare which model is best trained:\
+                the model from the saved model folder or the new model."
+            )
+
+            latest_dir_path = self.model_resolver.get_latest_dir_path()
+            if latest_dir_path == None:
+                model_eval_artifact = artifact_entity.ModelEvaluationArtifact(is_model_accepted=True, improved_accuracy=None)
+
+                logging.info(f"Model Evaluation Artifacts: {model_eval_artifact}")
+                return model_eval_artifact
+                
+            # finding location of transformer, model, and target encoder
+            logging.info(f"Finding location of transformer, model and target encoder")
+            transformer_path = self.model_resolver.get_latest_transformer_path()
+
+            model_path = self.model_resolver.get_latest_model_path()
+
+            target_encoder_path = self.model_resolver.get_latest_target_encoder_path()
+
+            # finding the location of previous transfomer, model and target encoder
+            logging.info(f"Previous trained objects of transformer, model and target encoder")
+            transformer = load_object(file_path=transformer_path)
+            model = load_object(file_path=model_path)
+            target_encoder = load_object(file_path=target_encoder_path)
+
+            # finding the location of currently trained objects
+            logging.info(f"Currently trained model objects")
+            current_transformer = load_object(file_path=self.data_transformation_artifact.transform_object_path)
+
+            current_model = load_object(file_path=self.model_trainer_artifact.model_path)
+
+            current_target_encoder = load_object(file_path=self.data_transformation_artifact.target_encoder_path)
+
+            # fetching the testing data 
+            test_df = pd.read_csv(self.data_ingestion_artifact.test_file_path)
+            target_df = test_df[TARGET_COLUMN]
+
+            y_true = target_encoder.transform(target_df)
+
+            # accuracy using previous trained model
+            input_feature_name = list(transformer.feature_names_in_)
+            input_arr = transformer.transform(test_df[input_feature_name])
+
+            y_pred = current_model.predict(input_arr)
+            y_true = current_target_encoder.transform(target_df)
+
+            previous_model_score = f1_score(y_true=y_true, y_pred=y_pred, average='weighted')
+
+            # accuracy using current model 
+            input_feature_name = list(current_transformer.feature_names_in_)
+            input_arr = current_transformer.transform(test_df[input_feature_name])
+
+            y_pred = current_model.predict(input_arr)
+            y_true = current_target_encoder.transform(target_df)
+
+            current_model_score = f1_score(y_true=y_true, y_pred=y_pred, average='weighted')
+
+            if current_model_score <= previous_model_score:
+                logging.info(f"Current trained model is not better than previous model")
+                raise Exception("Current trained model is not better than previous model")
+
+            model_eval_artifact = artifact_entity.ModelEvaluationArtifact(is_model_accepted=True, 
+                                                                        improved_accuracy = current_model_score - previous_model_score)
+
+            logging.info(f"Model Eval Artifacts generated")
+            return model_eval_artifact
+
+        except Exception as e:
+            raise FertilizerException(e, sys)                                                                        

Fertilizer-Recommendation/src/components/model_pusher.py

@@ -0,0 +1,71 @@
+from src.entity import config_entity
+from src.entity import artifact_entity
+from src.logger import logging
+from src.exception import FertilizerException
+from src.predictor import ModelResolver
+from src.utils import load_object
+from src.utils import save_object
+
+from src.entity.config_entity import ModelPusherConfig
+
+from src.entity.artifact_entity import DataTransformationArtifact
+from src.entity.artifact_entity import ModelTrainerArtifact
+from src.entity.artifact_entity import ModelPusherArtifact
+
+import os 
+import sys 
+
+class ModelPusher:
+
+    def __init__(
+        self, 
+        model_pusher_config: ModelPusherConfig,
+        data_transformation_artifact: DataTransformationArtifact,
+        model_trainer_artifact: ModelTrainerArtifact
+    ):
+
+        try:
+            logging.info(f"\n\n{'>'*50} Model Pusher Initiated {'<'*50}\n")
+            self.model_pusher_config = model_pusher_config
+            self.data_transformation_artifact = data_transformation_artifact
+            self.model_trainer_artifact = model_trainer_artifact
+            self.model_resolver = ModelResolver(model_registry=self.model_pusher_config.saved_model_dir)
+        
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    def initiate_model_pusher(self) -> ModelPusherArtifact:
+        try:
+            # load object
+            logging.info(f"Loading transformer model and target encoder")
+            transformer = load_object(file_path=self.data_transformation_artifact.transform_object_path)
+            model = load_object(file_path=self.model_trainer_artifact.model_path)
+            target_encoder = load_object(file_path=self.data_transformation_artifact.target_encoder_path)
+
+            # model pusher dir 
+            logging.info(f"Saving model into model pusher directory")
+            save_object(file_path=self.model_pusher_config.pusher_transformer_path, obj=transformer)
+            save_object(file_path=self.model_pusher_config.pusher_model_path, obj=model)
+            save_object(file_path=self.model_pusher_config.pusher_target_encoder_path, obj=target_encoder)
+
+            # saved model dir 
+            logging.info(f"Saving model in saved model dir")
+
+            transformer_path = self.model_resolver.get_latest_save_transformer_path()
+            model_path = self.model_resolver.get_latest_save_model_path()
+            target_encoder_path = self.model_resolver.get_latest_save_target_encoder_path()
+
+            save_object(file_path=transformer_path, obj=transformer)
+            save_object(file_path=model_path, obj=model)
+            save_object(file_path=target_encoder_path, obj=target_encoder)
+
+            model_pusher_artifact = ModelPusherArtifact(
+                pusher_model_dir = self.model_pusher_config.pusher_model_dir, 
+                saved_model_dir = self.model_pusher_config.saved_model_dir)
+            
+            logging.info(f"Model Pusher Artifacts Generated")
+
+            return model_pusher_artifact
+        
+        except Exception as e:
+            raise FertilizerException(e, sys)

Fertilizer-Recommendation/src/components/model_trainer.py

@@ -0,0 +1,102 @@
+from src.entity import config_entity
+from src.entity import artifact_entity
+from src.logger import logging
+from src.exception import FertilizerException
+from src import utils
+
+from typing import Optional
+from sklearn.metrics import f1_score
+from sklearn.tree import DecisionTreeClassifier
+import os 
+import sys
+
+class ModelTrainer:
+
+    def __init__(
+        self, 
+        model_trainer_config: config_entity.ModelTrainerConfig,
+        data_transformation_artifact: artifact_entity.DataTransformationArtifact):
+
+        try:
+            logging.info(f"\n\n{'>'*50} Model Trainer Initiated {'<'*50}\n")
+            self.model_trainer_config = model_trainer_config
+            self.data_transformation_artifact = data_transformation_artifact
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    def train_model(self, X, y):
+        try:
+            decision_tree_classifier = DecisionTreeClassifier()
+            decision_tree_classifier.fit(X, y)
+
+            return decision_tree_classifier
+        
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    def initial_model_trainer(self) -> artifact_entity.ModelTrainerArtifact:
+        try:
+            logging.info(f"Loading train and test array")
+
+            train_arr = utils.load_numpy_array_data(file_path=self.data_transformation_artifact.transformed_train_path)
+            test_arr = utils.load_numpy_array_data(file_path=self.data_transformation_artifact.transformed_test_path)
+
+            logging.info(f"Splitting the input and target feature from both train and test arr")
+
+            X_train, y_train = train_arr[:, :-1], train_arr[:, -1]
+            X_test, y_test = test_arr[:, :-1], test_arr[:, -1]
+
+            logging.info(f"Training the model")
+            model = self.train_model(X = X_train, y = y_train)
+
+            logging.info(f"Calculating the f1 train score")
+            yhat_train = model.predict(X_train)
+            
+            f1_train_score = f1_score(y_true = y_train, 
+                                      y_pred = yhat_train,
+                                      average="weighted")
+
+            logging.info(f"Calculating the f1 test score")
+            yhat_test = model.predict(X_test)
+
+            f1_test_score = f1_score(y_true = y_test,
+                                    y_pred = yhat_test,
+                                    average = 'weighted')
+
+            logging.info(f"train_score : {f1_train_score} and test_score : {f1_test_score}")
+
+            # checking for overfitting or underfitting or expected score
+            logging.info(f"Checking if our model is underfitting or not")
+            if f1_test_score < self.model_trainer_config.overfitting_threshold:
+                raise Exception(
+                    f"Model is not good, as it is not able to give \
+                        expected accuarcy: {self.model_trainer_config.expected_score}, \
+                            model actual score: {f1_test_score}"
+                )
+            logging.info(f"Checking if our model is overfitting or not")
+            diff = abs(f1_train_score - f1_test_score)
+
+            if diff > self.model_trainer_config.overfitting_threshold:
+                raise Exception(
+                    f"Train and test score diff: {diff} \
+                        is more than overfitting threshold: {self.model_trainer_config.overfitting_threshold}"
+                )
+
+            # save the trained model 
+            logging.info(f"Saving model object")
+            utils.save_object(file_path=self.model_trainer_config.model_path, obj=model)
+
+            # prepare the artifact 
+            logging.info(f"Prepare the artifact")
+            model_trainer_artifact = artifact_entity.ModelTrainerArtifact(
+                model_path = self.model_trainer_config.model_path, 
+                f1_train_score = f1_train_score, 
+                f2_test_score = f1_test_score)
+
+            logging.info(f"Model Trainer Complete, Artifact Generated")
+
+            return model_trainer_artifact
+
+        except Exception as e:
+            raise FertilizerException(e, sys)

Fertilizer-Recommendation/src/config.py

@@ -0,0 +1,23 @@
+import pymongo
+import pandas as pd
+import json
+from dataclasses import dataclass
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+@dataclass
+class EnvironmentVariable:
+    mongo_db_url = os.getenv("MONGO_URL")
+
+
+env = EnvironmentVariable()
+
+mongo_client = pymongo.MongoClient(env.mongo_db_url)
+
+TARGET_COLUMN = "Fertilizer Name"
+NUMERICAL_FEATURES = ['Temparature', 'Humidity ', 'Moisture', 'Nitrogen', 'Potassium', 'Phosphorous']
+CATEGORICAL_FEATURES = ['Soil Type', 'Crop Type']
+BASE_FILE_PATH = os.path.join("fertilizer-prediction/Fertilizer Prediction.csv")

Fertilizer-Recommendation/src/entity/artifact_entity.py

@@ -0,0 +1,40 @@
+from dataclasses import dataclass
+
+
+@dataclass
+class DataIngestionArtifact:
+    feature_store_file_path: str
+    train_file_path: str
+    test_file_path: str
+
+
+@dataclass
+class DataValidationArtifact:
+    report_file_path: str
+
+
+@dataclass
+class DataTransformationArtifact:
+    transform_object_path: str
+    transformed_train_path: str
+    transformed_test_path: str
+    target_encoder_path: str
+
+
+@dataclass
+class ModelTrainerArtifact:
+    model_path: str
+    f1_train_score: float
+    f2_test_score: float
+
+
+@dataclass
+class ModelEvaluationArtifact:
+    is_model_accepted: bool
+    improved_accuracy: float
+
+
+@dataclass
+class ModelPusherArtifact:
+    pusher_model_dir: str
+    saved_model_dir: str

Fertilizer-Recommendation/src/entity/config_entity.py

@@ -0,0 +1,120 @@
+import os
+import sys
+from src.exception import FertilizerException
+from src.logger import logging
+from datetime import datetime
+
+FILE_NAME = "fertilizer.csv"
+TRAIN_FILE_NAME = "train.csv"
+TEST_FILE_NAME = "test.csv"
+TRANSFORMER_OBJECT_FILE_NAME = "transformer.pkl"
+TARGET_ENCODER_OBJECT_FILE_NAME = "target_encoder.pkl"
+MODEL_FILE_NAME = "model.pkl"
+
+
+class TrainingPipelineConfig:
+    def __init__(self):
+        try:
+            self.artifact_dir = os.path.join(
+                os.getcwd(), "artifact", f"{datetime.now().strftime('%m%d%Y__%H%M%S')}"
+            )
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+
+class DataIngestionConfig:
+    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
+        try:
+            self.database_name = "smartcropguard"
+            self.collection_name = "fertilizer"
+            self.data_ingestion_dir = os.path.join(
+                training_pipeline_config.artifact_dir, "data_ingestion"
+            )
+            self.feature_store_file_path = os.path.join(
+                self.data_ingestion_dir, "feature_store", FILE_NAME
+            )
+            self.train_file_path = os.path.join(
+                self.data_ingestion_dir, "dataset", TRAIN_FILE_NAME
+            )
+            self.test_file_path = os.path.join(
+                self.data_ingestion_dir, "dataset", TEST_FILE_NAME
+            )
+            self.test_size = 0.2
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    def to_dict(self) -> dict:
+        try:
+            return self.__dict__
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+
+class DataValidationConfig:
+    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
+        self.data_validation_dir = os.path.join(
+            training_pipeline_config.artifact_dir, "data_validation"
+        )
+        self.report_file_path = os.path.join(self.data_validation_dir, "report.yaml")
+        self.missing_threshold = 0.2
+        self.base_file_path = os.path.join(
+            "fertilizer-prediction/Fertilizer Prediction.csv"
+        )
+
+
+class DataTransformationConfig:
+    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
+        self.data_transformation_dir = os.path.join(
+            training_pipeline_config.artifact_dir, "data_transformation"
+        )
+        self.transform_object_path = os.path.join(
+            self.data_transformation_dir, 
+            "transformer", 
+            TRANSFORMER_OBJECT_FILE_NAME
+        )
+        self.transformed_train_path = os.path.join(
+            self.data_transformation_dir,
+            "transformed",
+            TRAIN_FILE_NAME.replace("csv", "npz"),
+        )
+        self.transformed_test_path = os.path.join(
+            self.data_transformation_dir,
+            "transformed",
+            TEST_FILE_NAME.replace("csv", "npz"),
+        )
+        self.target_encoder_path = os.path.join(
+            self.data_transformation_dir,
+            "target_encoder",
+            TARGET_ENCODER_OBJECT_FILE_NAME,
+        )
+
+
+class ModelTrainerConfig:
+    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
+        self.model_trainer_dir = os.path.join(
+            training_pipeline_config.artifact_dir, "model_trainer"
+        )
+        self.model_path = os.path.join(self.model_trainer_dir, "model", MODEL_FILE_NAME)
+        self.expected_score = 0.9
+        self.overfitting_threshold = 0.1
+
+
+class ModelEvaluationConfig:
+    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
+        self.change_threshold = 0.01
+
+
+class ModelPusherConfig:
+    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
+        self.model_pusher_dir = os.path.join(
+            training_pipeline_config.artifact_dir, "model_pusher"
+        )
+        self.saved_model_dir = os.path.join("saved_models")
+        self.pusher_model_dir = os.path.join(self.model_pusher_dir, "saved_models")
+        self.pusher_model_path = os.path.join(self.pusher_model_dir, MODEL_FILE_NAME)
+        self.pusher_transformer_path = os.path.join(
+            self.pusher_model_dir, TRANSFORMER_OBJECT_FILE_NAME
+        )
+        self.pusher_target_encoder_path = os.path.join(
+            self.pusher_model_dir, TARGET_ENCODER_OBJECT_FILE_NAME
+        )

Fertilizer-Recommendation/src/exception.py

@@ -0,0 +1,21 @@
+import sys
+
+
+def error_message_detail(error, error_detail: sys):
+    _, _, exc_tb = error_detail.exc_info()
+    file_name = exc_tb.tb_frame.f_code.co_filename
+    error_message = "Error occurred python script name [{0}] line number [{1}] error message [{2}]".format(
+        file_name, exc_tb.tb_lineno, str(error)
+    )
+
+    return error_message
+
+
+class FertilizerException(Exception):
+    def __init__(self, error_message, error_detail: sys):
+        self.error_message = error_message_detail(
+            error_message, error_detail=error_detail
+        )
+
+    def __str__(self):
+        return self.error_message

Fertilizer-Recommendation/src/logger.py

@@ -0,0 +1,22 @@
+import logging
+import os
+from datetime import datetime
+
+# log file name
+LOG_FILE_NAME = f"{datetime.now().strftime('%m%d%Y__%H%M%S')}.log"
+
+# Log directory
+LOG_FILE_DIR = os.path.join(os.getcwd(), "logs")
+
+# create folder if not available
+os.makedirs(LOG_FILE_DIR, exist_ok=True)
+
+# Log file path
+LOG_FILE_PATH = os.path.join(LOG_FILE_DIR, LOG_FILE_NAME)
+
+
+logging.basicConfig(
+    filename=LOG_FILE_PATH,
+    format="[ %(asctime)s ] %(filename)s - %(lineno)d %(name)s - %(levelname)s - %(message)s",
+    level=logging.INFO,
+)

Fertilizer-Recommendation/src/pipeline/training_pipeline.py

@@ -0,0 +1,85 @@
+from src.logger import logging
+from src.exception import FertilizerException
+from src.utils import get_collection_as_dataframe
+from src.entity import config_entity
+from src.entity import artifact_entity
+import os
+import sys
+
+from src.components.data_ingestion import DataIngestion
+from src.components.data_validation import DataValidation
+from src.components.data_transformation import DataTransformation
+from src.components.model_trainer import ModelTrainer
+from src.components.model_evaluation import ModelEvaluation
+from src.components.model_pusher import ModelPusher
+
+def start_training_pipeline():
+    try:
+        training_pipeline_config = config_entity.TrainingPipelineConfig()
+
+        # data ingestion
+        data_ingestion_config = config_entity.DataIngestionConfig(
+            training_pipeline_config=training_pipeline_config)
+
+        data_ingestion_config.to_dict()
+
+        data_ingestion = DataIngestion(
+            data_ingestion_config=data_ingestion_config)
+
+        data_ingestion_artifact = data_ingestion.initiate_data_ingestion()
+    
+        print(f"Data Ingestin complete")
+
+        # data validation 
+        data_validation_config = config_entity.DataValidationConfig(training_pipeline_config=training_pipeline_config)
+
+        data_validation = DataValidation(data_validation_config=data_validation_config, 
+                                        data_ingestion_arfitact=data_ingestion_artifact)
+        
+        data_validation.initiate_data_validation()
+        print(f"Data Validation Complete")
+
+        # data transformation 
+        data_transformation_config = config_entity.DataTransformationConfig(training_pipeline_config=training_pipeline_config)
+
+        data_transformation = DataTransformation(data_transformation_config=data_transformation_config, 
+                                                data_ingestion_artifact=data_ingestion_artifact)
+
+        data_transformation_artifact = data_transformation.initiate_data_transformation()
+        print(f"Data Transformation Complete")                                                
+
+        # model trainer
+        model_trainer_config = config_entity.ModelTrainerConfig(training_pipeline_config=training_pipeline_config)
+
+        model_trainer = ModelTrainer(model_trainer_config=model_trainer_config, 
+                                    data_transformation_artifact=data_transformation_artifact)
+
+        model_trainer_artifact = model_trainer.initial_model_trainer()
+        print(f"Model Trainer Complete")
+
+        # model evaluation 
+        model_evaluation_config = config_entity.ModelEvaluationConfig(training_pipeline_config=training_pipeline_config)
+
+        model_evaluation = ModelEvaluation(
+            model_eval_config = model_evaluation_config, 
+            data_ingestion_artifact = data_ingestion_artifact, 
+            data_transformation_artifact = data_transformation_artifact, 
+            model_trainer_artifact = model_trainer_artifact)
+
+        model_evalution_artifact = model_evaluation.initiate_model_evaluation()
+        print(f"Model Evaluation Complete")
+
+        # model pusher
+        model_pusher_config = config_entity.ModelPusherConfig(training_pipeline_config=training_pipeline_config)
+
+        model_pusher = ModelPusher(
+            model_pusher_config = model_pusher_config, 
+            data_transformation_artifact = data_transformation_artifact, 
+            model_trainer_artifact = model_trainer_artifact)
+
+        model_trainer_artifact = model_pusher.initiate_model_pusher()
+        print(f"Model Pusher Complete")
+
+    except Exception as e:
+        raise FertilizerException(e, sys)
+

Fertilizer-Recommendation/src/predictor.py

@@ -0,0 +1,118 @@
+from src.entity.config_entity import TRANSFORMER_OBJECT_FILE_NAME
+from src.entity.config_entity import MODEL_FILE_NAME
+from src.entity.config_entity import TARGET_ENCODER_OBJECT_FILE_NAME
+from src.exception import FertilizerException
+from src.logger import logging 
+
+import os 
+import sys 
+from glob import glob
+from typing import Optional
+
+class ModelResolver:
+
+    def __init__(
+        self, 
+        model_registry: str = 'saved_models', 
+        transformer_dir_name = 'transformer',
+        target_encoder_dir_name = 'target_encoder',
+        model_dir_name = 'model'
+    ):
+
+        self.model_registry = model_registry
+        os.makedirs(self.model_registry, exist_ok=True)
+
+        self.transformer_dir_name = transformer_dir_name
+        self.target_encoder_dir_name = target_encoder_dir_name
+        self.model_dir_name = model_dir_name
+
+    def get_latest_dir_path(self) ->Optional[str]:
+        try:
+            dir_names = os.listdir(self.model_registry)
+            
+            if len(dir_names) == 0:
+                return None 
+            dir_names = list(map(int, dir_names))
+            latest_dir_name = max(dir_names)
+            
+            return os.path.join(self.model_registry, f"{latest_dir_name}")
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+    
+    def get_latest_model_path(self):
+        try:
+            latest_dir = self.get_latest_dir_path()
+            
+            if latest_dir is None:
+                raise Exception(f"Model is not available")
+            
+            return os.path.join(latest_dir, self.model_dir_name, MODEL_FILE_NAME)
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    def get_latest_transformer_path(self):
+        try:
+            latest_dir = self.get_latest_dir_path()
+            if latest_dir is None:
+                raise Exception(f"Transformer is not available")
+            
+            return os.path.join(latest_dir, self.transformer_dir_name, TRANSFORMER_OBJECT_FILE_NAME)
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+    
+    def get_latest_target_encoder_path(self):
+        try:
+            latest_dir = self.get_latest_dir_path()
+            if latest_dir is None:
+                raise Exception(f"Target Encoder is not available")
+
+            return os.path.join(latest_dir, self.target_encoder_dir_name, TARGET_ENCODER_OBJECT_FILE_NAME)
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+    
+    def get_latest_save_dir_path(self):
+        try:
+            latest_dir = self.get_latest_dir_path()
+
+            if latest_dir is None:
+                return os.path.join(self.model_registry, f"{0}")
+            
+            latest_dir_num = int(os.path.basename(self.get_latest_dir_path()))
+
+            return os.path.join(self.model_registry, f"{latest_dir_num + 1}")
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    def get_latest_save_model_path(self):
+        try:
+            latest_dir = self.get_latest_save_dir_path()
+            
+            return os.path.join(latest_dir, self.model_dir_name, MODEL_FILE_NAME)
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    def get_latest_save_transformer_path(self):
+        try:
+            latest_dir = self.get_latest_save_dir_path()
+
+            return os.path.join(latest_dir, self.transformer_dir_name, TRANSFORMER_OBJECT_FILE_NAME)
+        
+        except Exception as e:
+            raise FertilizerException(e, sys)
+
+    def get_latest_save_target_encoder_path(self):
+        try:
+            latest_dir = self.get_latest_save_dir_path()
+
+            return os.path.join(latest_dir, self.target_encoder_dir_name, TARGET_ENCODER_OBJECT_FILE_NAME)
+
+        except Exception as e:
+            raise FertilizerException(e, sys)
+            
+        

Fertilizer-Recommendation/src/utils.py

@@ -0,0 +1,115 @@
+import pandas as pd
+from src.logger import logging
+from src.exception import FertilizerException
+from src.config import mongo_client
+import os
+import sys
+import numpy as np
+import yaml
+import dill
+
+def get_collection_as_dataframe(
+    database_name: str, collection_name: str
+) -> pd.DataFrame:
+    """
+    Description: This function return collection as dataframe
+    =========================================================
+    Params:
+    database_name: database name
+    collection_name: collection name
+    =========================================================
+    return Pandas dataframe of a collection
+    """
+    try:
+        logging.info(
+            f"Reading data from database: {database_name} and collection: {collection_name}"
+        )
+        df = pd.DataFrame(list(mongo_client[database_name][collection_name].find()))
+        logging.info(f"{database_name} found in the mongodb")
+
+        if "_id" in df.columns:
+            logging.info("Dropping column: '_id'")
+            df = df.drop(columns=["_id"], axis=1)
+        logging.info(f"Row and columns in df: {df.shape}")
+        return df
+    except Exception as e:
+        raise FertilizerException(e, sys)
+
+
+def seperate_dependant_column(df: pd.DataFrame, exclude_column: list) -> pd.DataFrame:
+    final_dataframe = df.drop(exclude_column, axis=1)
+
+    return final_dataframe
+
+
+def get_column_indices(numerical_features: list, categorical_features: list, base_file_path: str):
+
+    dataset = pd.read_csv(base_file_path)
+
+    numerical_feature_indices = [dataset.columns.get_loc(feature) for feature in numerical_features]
+    categorical_feature_indices = [dataset.columns.get_loc(feature) for feature in categorical_features]
+
+    return numerical_feature_indices, categorical_feature_indices
+
+
+def write_yaml_file(file_path, data: dict):
+    try:
+        file_dir = os.path.dirname(file_path)
+        os.makedirs(file_dir, exist_ok=True)
+
+        with open(file_path, "w") as file_writer:
+            yaml.dump(data, file_writer)
+    except Exception as e:
+        raise FertilizerException(e, sys)
+
+
+def save_object(file_path: str, obj: object) -> None:
+    try:
+        logging.info("Entered the save object method of utils")
+        os.makedirs(os.path.dirname(file_path), exist_ok=True)
+        with open(file_path, "wb") as file_obj:
+            dill.dump(obj, file_obj)
+        logging.info("Exited the save object method of utils")
+    except Exception as e:
+        raise FertilizerException(e, sys)
+
+
+def load_object(file_path: str) -> object:
+    try:
+        if not os.path.exists(file_path):
+            raise Exception(f"The file: {file_path} is not exists")
+        with open(file_path, "rb") as file_obj:
+            return dill.load(file_obj)
+    except Exception as e:
+        raise FertilizerException(e, sys)
+
+
+def save_numpy_array_data(file_path: str, array: np.array):
+    """
+    save numpy array data to file
+    file_path : str location of the file to save
+    array: np.array data to save
+    """
+    try:
+        dir_path = os.path.dirname(file_path)
+        os.makedirs(dir_path, exist_ok=True)
+
+        with open(file_path, "wb") as file_ojb:
+            np.save(file_obj, array)
+
+    except Exception as e:
+        raise FertilizerException(e, sys)
+
+
+def load_numpy_array_data(file_path: str) -> np.array:
+    """
+    load numpy array data from file
+    file_path: str location of file to load
+    return: np.array data loaded
+    """
+    try:
+        with open(file_path, "rb") as file_obj:
+            return np.load(file_obj, allow_pickle=True)
+
+    except Exception as e:
+        raise CropException(e, sys)

Fertilizer-Recommendation/template.py

@@ -0,0 +1,49 @@
+import os, sys
+from pathlib import Path
+import logging
+
+while True:
+    project_name = input("Enter your project name: ")
+    if project_name !="":
+        break
+
+# src/__init__.py
+# src/compontes/__init__.py
+list_of_files = [
+    f"{project_name}/__init__.py",
+    f"{project_name}/components/__init__.py",
+    f"{project_name}/components/data_ingestion.py",
+    f"{project_name}/components/data_validation.py",
+    f"{project_name}/components/data_transformation.py",
+    f"{project_name}/components/model_trainer.py",
+    f"{project_name}/components/model_evaluation.py",
+    f"{project_name}/components/model_pusher.py",
+    f"{project_name}/entity/__init__.py",
+    f"{project_name}/entity/artifact_entity.py",
+    f"{project_name}/entity/config_entity.py",
+    f"{project_name}/pipeline/__init__.py",
+    f"{project_name}/pipeline/training_pipeline.py",
+    f"{project_name}/config.py",
+    f"{project_name}/app.py",
+    f"{project_name}/logger.py",
+    f"{project_name}/exception.py",
+    f"{project_name}/setup.py",
+    f"{project_name}/utils.py",
+    f"{project_name}/predictor.py",
+    "main.py",
+]
+
+
+for filepth in list_of_files:
+    filepath = Path(filepth)
+    filedir, filename = os.path.split(filepath)
+
+    if filedir !="":
+        os.makedirs(filedir, exist_ok=True)
+
+    if (not os.path.exists(filepath)) or (os.path.getsize(filepath) == 0):
+        with open(filepath, "w") as f:
+            pass
+
+    else:
+        logging.info("file is already present at : {filepath}")

README.md

@@ -1,10 +1,97 @@
 ---
-title: C
-emoji: 🐠
-colorFrom: pink
-colorTo: indigo
-sdk: static
+title: CropGaurd
+emoji: 🏢
+colorFrom: indigo
+colorTo: red
+sdk: gradio
+sdk_version: 3.39.0
+app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# CropGaurd
+## Agriculture and Farming Machine Learning Project
+
+Developed a comprehensive web application that harnesses the power of machine learning to provide valuable insights and recommendations to farmers, agriculture enthusiasts, and stakeholders.
+
+![CropGaurd-thumbnail](https://github.com/07Sada/CropGaurd/assets/112761379/fd5f1726-7450-4758-952e-23e7f7b9da06)
+
+# Disclaimer
+This project serves as a Proof of Concept (PoC) and is not intended for making actual farming decisions. The data utilized within this project is provided without any guarantee from the creator. Therefore, it is strongly advised not to utilize the information for real-world agricultural choices. Should you choose to do so, please be aware that the creator bears no responsibility for the outcomes.
+
+It's important to note that this project primarily demonstrates the application of Machine Learning (ML) and Deep Learning (DL) concepts within precision farming. The hypothetical scenario presented here underscores the potential benefits of deploying ML/DL techniques on a larger scale, provided that authentic and verified data sources are used.
+
+For reliable and accurate farming decisions, always rely on verified agricultural data sources, expert advice, and industry standards.
+
+## Project Links
+
+- **Application Link:** Check out the live application on Hugging Face Spaces: [Application Link](https://huggingface.co/spaces/Sadashiv/CropGaurd)
+
+- **Demo Video:** For a visual walkthrough of the application's features, watch demo video: [Demo Video Link](<insert_demo_video_link_here>)
+
+## Project Overview
+The application integrates several key features to assist users in making informed decisions for their agricultural activities. These features include:
+
+- ***Crop Recommendation System:*** Leveraging advanced machine learning techniques, the system recommends suitable crops based on various factors such as soil chemical contents, and climate conditions.
+
+- ***Fertilizer Recommendation System:*** The application also offers personalized fertilizer recommendations, ensuring that crops receive the optimal nutrients for healthy growth and abundant yields.
+
+- ***Plant Disease Classification:*** By employing cutting-edge image classification models, incorporated a feature that enables users to detect and diagnose diseases in plants. Users can simply upload images of their plants, and our system will accurately identify any diseases present and provide relevant information about them.
+
+- ***Real-time Commodity Price Updates:*** To empower users with current market insights, we have integrated a government API that provides daily commodity prices across different Indian states. This information assists farmers and traders in making pricing and distribution decisions.
+
+## Purpose
+The aim of project to revolutionize the agricultural sector by offering data-driven solutions that enhance productivity, reduce risks, and promote sustainable practices. By amalgamating technology and agriculture, we strive to address critical challenges faced by farmers and contribute to the growth of the farming community.
+
+Whether you're a seasoned farmer seeking optimized strategies or an individual interested in sustainable agriculture, our application provides the tools you need to make well-informed decisions.
+
+## Additional Details
+Here are some additional aspects of the project that contribute to its effectiveness and uniqueness:
+
+- ***Machine Learning Models:*** We have trained our recommendation and classification models on extensive datasets specific to Indian agriculture. This ensures that the recommendations and classifications are accurate and relevant to the local context.
+
+- ***User-Friendly Interface:*** Our web application boasts an intuitive and user-friendly interface designed to make navigation and interaction seamless, even for users with limited technological experience.
+
+- ***Informational Insights:*** Apart from recommendations, our application provides detailed information about recommended crops, fertilizers, and identified plant diseases. This information helps users understand the rationale behind the suggestions and take well-informed actions.
+
+- ***Scalability:*** Our project's architecture is designed to accommodate future expansions and enhancements. We are committed to continuously improving the application by incorporating user feedback and integrating emerging technologies.
+
+## Getting Started
+- Clone or download the parent repository from [GitHub Repository Link](https://github.com/07Sada/CropGaurd)
+
+    ```
+    git clone --recurse-submodules https://github.com/07Sada/CropGaurd
+    ```
+- The total project is divided into 4 repositories: one parent repository and 3 child repositories. The child repositories are dedicated to specific functionalities, namely [[crop recommendations](https://github.com/07Sada/crop-recommendation)], [[fertilizer recommendations](https://github.com/07Sada/Fertilizer-Recommendation)], and [[image classification](https://github.com/07Sada/plant-diseases-classifier)].
+- The parent and child repositories are connected using Git submodules. This approach is taken to keep each recommendation system separate, as they contain their end-to-end pipelines – from data ingestion to model training and deploying the best models for inference.
+- This modular structure allows us to maintain clean and organized code while efficiently managing updates and changes to each submodule.
+- The data ingestion pipeline is flexible, as it is integrated with a MongoDB database. You can set up a scheduler to periodically update the training data. After new data is ingested, the models are trained and evaluated against the existing models. The best model is then pushed for inference, all of which is seamlessly automated through the pipeline, reducing the potential for errors.
+- To get started, navigate to the parent repository and install the required dependencies.
+- Explore each child repository for more specific details on their functionalities and pipelines.
+- Launch the web application by running command in terminal.
+
+  ```
+  python app.py
+  ```
+- Start exploring the features and making use of the insightful recommendations provided.
+
+
+## Further Improvements
+
+These potential improvements are not only achievable but hold the promise of elevating the application's impact and utility:
+
+- ***Integration of Govt Policies:*** Imagine having the latest government policies and farmer-centric updates right at your fingertips. Our vision includes seamlessly integrating these critical updates, enabling you to stay informed and navigate regulatory changes with ease.
+
+- ***Language Translation:*** Empowering users globally is within our reach. We envision breaking language barriers by adding translation capabilities. This means you can explore our insights and recommendations in your preferred language, ensuring accessibility for all.
+
+- ***Weather Information:*** Harnessing real-time weather data can revolutionize your decision-making. Picture accessing accurate weather information directly within the application, allowing you to adapt and strategize based on changing conditions.
+
+- ***Enhanced Recommendations with More Data:*** Our recommendation systems already provide valuable guidance, but we're not stopping there. By expanding our dataset, we're poised to fine-tune these systems to deliver recommendations that are even more personalized and effective.
+
+- ***Market Trends Analysis:*** Envision anticipating market trends and price fluctuations effortlessly. With our potential addition of market trend analysis, you can gain insights that empower you to make informed decisions about your produce's pricing and distribution.
+
+- ***Community Forums:*** We foresee a thriving community within the application—a space where knowledge is freely exchanged. Imagine being part of a network of farmers, sharing insights, experiences, and innovative approaches to agriculture.
+
+- ***Automated Data Updates:*** Our dedication to keeping our models up-to-date is unwavering. The potential implementation of automated data updates ensures that you're always working with the latest insights and recommendations.
+
+

app.py

@@ -0,0 +1,152 @@
+from config import crop_model, crop_pipeline_encoder, crop_label_encoder
+from config import fertilizer_model, fertilizer_pipeline_encoder, fertilizer_label_encoder
+from config import plant_diseases_classifier_model
+from utils import retrieve_image_by_name_from_mongodb, retrieve_data
+from flask import Flask, request, render_template, jsonify
+import requests
+import os
+import numpy as np
+import base64
+
+app = Flask(__name__)
+
+app.config['UPLOAD_FOLDER'] = 'static/uploaded_image'
+
+@app.route("/")
+@app.route("/home")
+def home():
+    return render_template('index.html')
+
+@app.route('/crop_recommendation', methods=['GET', 'POST'])
+def crop_recommendation():
+    return render_template('crop_recommendation_input.html')
+
+@app.route("/crop_recommendation_output", methods=['GET', 'POST'])
+def crop_recommendation_output():
+    temperature = request.form.get("temperature")
+    humidity = request.form.get("humidity")
+    ph = request.form.get("ph")
+    nitrogen = request.form.get("nitrogen")
+    potassium = request.form.get("potassium")
+    phosphorous = request.form.get("phosphorous")
+    rain_fall = request.form.get("rain_fall")
+
+    input_list = [nitrogen, phosphorous, potassium, temperature, humidity, ph, rain_fall]
+    input_array = np.array(input_list).reshape(-1, 7).astype(int)
+
+    transformed_data = crop_pipeline_encoder.transform(input_array)
+    model_prediction = crop_model.predict(transformed_data).astype(int)
+
+    label = crop_label_encoder.inverse_transform(model_prediction)
+    print(label)
+    
+    # retrieving the image from mongodb dabase
+    image_data = retrieve_image_by_name_from_mongodb(database_name=os.getenv("CROP_DB_NAME"),
+                                                        collection_name=os.getenv("CROP_IMAGE_COLLECTION_NAME"),
+                                                        file_name=str(label[0]))
+
+    # encoding the byte data recieved from the mongodb
+    image_data_base64 = base64.b64encode(image_data).decode('utf-8')
+
+    # retrieving text data from mongodb 
+    crop_details = retrieve_data(database_name=os.getenv("CROP_DB_NAME"), collection_name= os.getenv("CROP_INFO_COLLECTION_NAME"), search_query=label[0])
+
+    return render_template('crop_recommendation_output.html', image_data_base64=image_data_base64, input_file_name=label[0], crop_details=crop_details)
+
+
+@app.route('/fertilizer_recommendation', methods=['GET', 'POST'])
+def fertilizer_recommendation():
+    return render_template('fertilizer_recommendation_input.html')
+
+@app.route('/fertilizer_recommendation_output', methods=['GET', 'POST'])
+def fertilizer_recommendation_output():
+    temperature = request.form.get("temperature")
+    humidity = request.form.get("humidity")
+    moisture = request.form.get("moisture")
+    nitrogen = request.form.get("nitrogen")
+    potassium = request.form.get("potassium")
+    phosphorous = request.form.get("phosphorous")
+    soil_type = request.form.get("soil_type")
+    crop_type = request.form.get("crop_type")
+
+    input_data = [int(temperature), int(humidity), int(moisture), soil_type, crop_type, int(nitrogen), int(potassium), int(phosphorous)]
+    input_array = np.array(input_data).reshape(-1, 8)
+    
+    transformed_data = fertilizer_pipeline_encoder.transform(input_array)
+    model_prediction = fertilizer_model.predict(transformed_data).astype(int)
+
+    label = fertilizer_label_encoder.inverse_transform(model_prediction)
+
+    # retrieving the image from mongodb dabase
+    image_data = retrieve_image_by_name_from_mongodb(database_name=os.getenv("FERTILIZER_DB_NAME"),
+                                                        collection_name=os.getenv("FERTILIZER_IMAGE_COLLECTION_NAME"),
+                                                        file_name=str(label[0]))
+    
+    # encoding the byte data recieved from the mongodb
+    image_data_base64 = base64.b64encode(image_data).decode('utf-8')
+
+    # retrieving text data from mongodb 
+    fertilizer_details = retrieve_data(database_name=os.getenv("FERTILIZER_DB_NAME"), collection_name= os.getenv("FERTILIZER_INFO_COLLECTION_NAME"), search_query=label[0])
+
+
+    return render_template('fertilizer_recommendation_ouput.html', image_data_base64=image_data_base64, label= label[0], fertilizer_details=fertilizer_details)
+
+
+@app.route('/image_classification', methods=['GET', 'POST'])
+def image_classification():
+    return render_template('image_classification_input.html')
+
+@app.route('/image_classification_output', methods=['GET', 'POST'])
+def image_classification_output():
+    file = request.files['image_file']
+    new_filename = "plant_image.JPG"
+    file.save(os.path.join(app.config['UPLOAD_FOLDER'], new_filename))
+    file_path = os.path.join(app.config['UPLOAD_FOLDER'], new_filename)
+
+    # infercing the with the uploaded image
+    results = plant_diseases_classifier_model(file_path)
+    
+    #fetching all the labels 
+    names_dict = results[0].names
+
+    # fetching the probalility of each class
+    probs = results[0].probs.data.tolist()
+
+    # selecting class with maximum probability
+    model_prediction= names_dict[np.argmax(probs)]
+
+    diseases_details = retrieve_data(database_name=os.getenv("DISEASE_DB_NAME"), 
+                                    collection_name=os.getenv("DISEASE_INFO_COLLECTION_NAME"),
+                                    search_query=model_prediction)
+
+    return render_template("image_classification_output.html", model_prediction=model_prediction, diseases_details=diseases_details)
+
+
+@app.route('/market_price')
+def market_price():
+    return render_template("market_price_input.html")
+
+@app.route('/market_price_output', methods=['POST'])
+def market_price_output():
+    # input field name is 'selected_state'
+    user_input = request.form.get('selected_state')
+    api_key = os.getenv("COMMODITY_PRICE_API_KEY")
+
+    # Make a request to the API with the user input
+    api_url = f'https://api.data.gov.in/resource/9ef84268-d588-465a-a308-a864a43d0070?api-key={api_key}&format=json&filters%5Bstate%5D={user_input}'
+    response = requests.get(api_url)
+
+    if response.status_code == 200:
+        data = response.json()
+        data = data['records']
+        # return render_template('market_price_output.html', data=data)
+        if len(data) > 0:
+        # Return the JSON data as a response
+            return render_template('market_price_output.html', data=data)
+        else:
+            return render_template("market_price_no_data.html")
+    else:
+        return jsonify({'error': 'Unable to fetch data from the API'}), 400
+
+if __name__ == "__main__":
+    app.run(debug=True, host="0.0.0.0", port=8000)

artifacts.py

@@ -0,0 +1,31 @@
+import os
+
+MODEL_NAME = "model.pkl" 
+TARGET_ENCODER_OBJECT_NAME = "target_encoder.pkl"
+TRANSFORMER_OJBCET_NAME = "transformer.pkl"
+
+crop_recommendation_artifacts_path = "./crop-recommendation/saved_models"
+fertilizer_recommendation_artifacts_path = "./Fertilizer-Recommendation/saved_models"
+
+plant_diseases_classifier_model_path = "./plant-diseases-classifier/custom_model_weights/best.pt"
+
+
+## crop recommendation artifacts
+latest_crop_recommendation_artifacts = max(os.listdir(crop_recommendation_artifacts_path)) #0, 1, 2
+
+latest_crop_recommendation_artifacts_path = os.path.join(crop_recommendation_artifacts_path, latest_crop_recommendation_artifacts)
+
+crop_model_path = os.path.join(latest_crop_recommendation_artifacts_path, 'model', MODEL_NAME)
+crop_transformer_path = os.path.join(latest_crop_recommendation_artifacts_path,'transformer', TRANSFORMER_OJBCET_NAME)
+crop_target_encoder_path = os.path.join(latest_crop_recommendation_artifacts_path, 'target_encoder', TARGET_ENCODER_OBJECT_NAME)
+
+
+## fertilizer recommendation artifacts
+latest_fertilizer_recommendation_artifacts = max(os.listdir(fertilizer_recommendation_artifacts_path)) #0, 1, 2
+
+latest_fertilizer_recommendation_artifacts_path = os.path.join(fertilizer_recommendation_artifacts_path, latest_fertilizer_recommendation_artifacts)
+
+fertilizer_model_path = os.path.join(latest_fertilizer_recommendation_artifacts_path, 'model', MODEL_NAME)
+fertilizer_transformer_path = os.path.join(latest_fertilizer_recommendation_artifacts_path,'transformer', TRANSFORMER_OJBCET_NAME)
+fertilizer_target_encoder_path = os.path.join(latest_fertilizer_recommendation_artifacts_path, 'target_encoder', TARGET_ENCODER_OBJECT_NAME)
+