Duplicate from eaedk/agri-tech-fastapi

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.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/
+.vscode/
+assets/crop_recommandation2.pkl

Dockerfile

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+FROM python:3.9
+#-slim
+
+WORKDIR /app 
+
+COPY requirements.txt ./ 
+
+RUN pip install -r requirements.txt 
+
+EXPOSE 7860
+
+COPY . .
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: Agri Tech Fastapi
+emoji: 🪴
+colorFrom: orange
+colorTo: green
+sdk: docker
+pinned: false
+license: mit
+duplicated_from: eaedk/agri-tech-fastapi
+---
+
+Here is the link to directly access the API: [here](https://eaedk-agri-tech-fastapi.hf.space).
+Access the documentation [here](https://eaedk-agri-tech-fastapi.hf.space/docs).
+
+To direcly access your API hosted on HuggingFace you should use the URL follow this format : `https://<USERNAME>-<SPACENAME>.hf.space/` 
+
+In my case it is : https://eaedk-agri-tech-fastapi.hf.space/ 
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

assets/ml/crop_recommandation2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:90c49b603c04d5dec6dc0be582b59dc5cd4334ca2e97364b78be91bf314bd19a
+size 1787986

main.py

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+from fastapi import FastAPI
+import uvicorn
+from typing import List, Literal, Optional
+from pydantic import BaseModel
+import pandas as pd
+import pickle
+import os
+import json
+import logging
+
+# logger
+logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)
+
+# Util Functions & Classes
+def loading(fp):
+    with open(fp, "rb") as f:
+        data = pickle.load(f)
+
+    print(f"INFO: Loaded data : {data}")
+    return data
+
+
+def predict(df, endpoint="simple"):
+    """Take a dataframe as input and use it to make predictions"""
+
+    print(
+        f"[Info] 'predict' function has been called through the endpoint '{endpoint}'.\n"
+    )
+    
+    logging.info(f" \n{df.to_markdown()}")
+
+    # scaling
+    scaled_df = scaler.transform(df)
+    logging.info(f"     Scaler output is of type {type(scaled_df)}")
+
+    # prediction
+    prediction = model.predict_proba(scaled_df)
+    print(f"INFO: Prediction output: {prediction}")
+
+    # Formatting of the prediction
+    ## extract highest proba
+    highest_proba = prediction.max(axis=1)
+    print(f"INFO: Highest probabilities : {highest_proba}")
+
+    ## extract indexes of the highest proba
+    highest_proba_idx = prediction.argmax(axis=1)
+    print(f"INFO: Highest probability indexes : {highest_proba_idx}")
+
+    ## Maching prediction with classes
+    predicted_classes = [labels[i] for i in highest_proba_idx]
+    print(f"INFO: Predicted classes : {predicted_classes}")
+    # prediction[:, highest_proba_idx]
+
+    # save in df
+    df["predicted proba"] = highest_proba
+    df["predicted label"] = predicted_classes
+
+    print(f"INFO: dataframe filled with prediction\n{df.to_markdown()}\n")
+
+    # parsing prediction
+    # parsed = json.loads(df.to_json(orient="index")) # or
+    parsed = df.to_dict("records")
+
+    return parsed
+
+
+## INPUT MODELING
+class Land(BaseModel):
+    """Modeling of one input data in a type-restricted dictionary-like format
+
+    column_name : variable type # strictly respect the name in the dataframe header.
+
+    eg.:
+    =========
+    customer_age : int
+    gender : Literal['male', 'female', 'other']
+    """
+
+    N: float
+    P: float
+    K: float
+    temperature: float
+    humidity: float
+    ph: float
+    rainfall: float
+
+
+class Lands(BaseModel):
+    inputs: List[Land]
+
+    def return_list_of_dict(
+        cls,
+    ):
+        # return [land.dict() for land in cls.inputs]
+        return [i.dict() for i in cls.inputs]
+
+
+# API Config
+app = FastAPI(title="Agri-Tech API",
+              description="This is a ML API for classification of crop to plant on a land regarding some features")
+
+# ML Config
+ml_objects = loading(fp=os.path.join("assets", "ml", "crop_recommandation2.pkl"))
+## Extract the ml components
+model = ml_objects["model"]
+scaler = ml_objects["scaler"].set_output(transform="pandas")
+labels = ml_objects["labels"]
+
+
+# Endpoints
+@app.get("/")
+def root():
+    return {"Description": " This is a ML API for classification of crop to plant on a land regarding some features.",
+            "Documentation": "Go to the docs: https://eaedk-agri-tech-fastapi.hf.space/docs"}
+
+
+@app.get("/checkup")
+def test(a: Optional[int], b: int):
+    return {"a": a, "b": b}
+
+
+## ML endpoint
+@app.post("/predict")
+def make_prediction(
+    N: float,
+    P: float,
+    K: float,
+    temperature: float,
+    humidity: float,
+    ph: float,
+    rainfall: float,
+):
+    """Make prediction with the passed data"""
+
+    df = pd.DataFrame(
+        {
+            "N": [N],
+            "P": [P],
+            "K": [K],
+            "temperature": [temperature],
+            "humidity": [humidity],
+            "ph": [ph],
+            "rainfall": [rainfall],
+        }
+    )
+
+    parsed = predict(df=df)  # df.to_dict('records')
+
+    return {
+        "output": parsed,
+    }
+
+
+@app.post("/predict_multi")
+def make_multi_prediction(multi_lands: Lands):
+    """Make prediction with the passed data"""
+    print(f"Mutiple inputs passed: {multi_lands}\n")
+    df = pd.DataFrame(multi_lands.return_list_of_dict())
+
+    parsed = predict(df=df, endpoint="multi inputs")  # df.to_dict('records')
+
+    return {
+        "output": parsed,
+        "author": "Stella Archar",
+        "api_version": ";)",
+    }
+
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", reload=True)

requirements.txt

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+anyio==3.6.2
+black==23.3.0
+certifi==2023.5.7
+click==8.1.3
+dnspython==2.3.0
+email-validator==2.0.0.post2
+fastapi==0.95.2
+h11==0.14.0
+httpcore==0.17.1
+httptools==0.5.0
+httpx==0.24.1
+idna==3.4
+itsdangerous==2.1.2
+Jinja2==3.1.2
+joblib==1.2.0
+MarkupSafe==2.1.2
+mypy-extensions==1.0.0
+numpy==1.24.3
+orjson==3.8.12
+packaging==23.1
+pandas==2.0.1
+pathspec==0.11.1
+platformdirs==3.5.1
+pydantic==1.10.7
+python-dateutil==2.8.2
+python-dotenv==1.0.0
+python-multipart==0.0.6
+pytz==2023.3
+PyYAML==6.0
+scikit-learn==1.2.2
+scipy==1.10.1
+six==1.16.0
+sniffio==1.3.0
+starlette==0.27.0
+tabulate==0.9.0
+threadpoolctl==3.1.0
+tomli==2.0.1
+typing_extensions==4.5.0
+tzdata==2023.3
+ujson==5.7.0
+uvicorn==0.22.0
+uvloop==0.17.0
+watchfiles==0.19.0
+websockets==11.0.3