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| import streamlit as st | |
| import pandas as pd | |
| import joblib | |
| import ee | |
| import geemap | |
| # Earth Engine Authentication (Replace with your actual authentication) | |
| # اعتبار سنجی و اتصال به Google Earth Engine | |
| service_account = 'earth-engine-service-account@ee-esmaeilkiani1387.iam.gserviceaccount.com' | |
| credentials = ee.ServiceAccountCredentials(service_account, 'ee-esmaeilkiani1387-1b2c5e812a1d.json') | |
| ee.Initialize(credentials) | |
| # Load pre-trained model | |
| model = joblib.load('updated_model.pkl') # Replace 'updated_model.pkl' with your actual model file | |
| # Load farm data | |
| farm_data = pd.read_csv('Farm_NDRE_TimeSeries.csv') # Replace 'Farm_NDRE_TimeSeries.csv' with your actual data file | |
| farm_names = farm_data['Farm_Name'].tolist() | |
| # Function to calculate NDRE | |
| def calculate_ndre(coordinates, start_date, end_date): | |
| try: | |
| # Define the Earth Engine region of interest (ROI) | |
| roi = ee.Geometry.Point(coordinates) | |
| # Define the image collection (replace with your actual collection ID and bands) | |
| imageCollection = ee.ImageCollection('COPERNICUS/S2_SR') \ | |
| .filterBounds(roi) \ | |
| .filterDate(start_date, end_date) \ | |
| .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)) #Cloud filtering | |
| # Function to compute NDRE | |
| def ndre(image): | |
| red_edge = image.select('B8A') | |
| red = image.select('B4') | |
| return image.addBands(red_edge.subtract(red).divide(red_edge.add(red)).rename('NDRE')) | |
| #Apply NDRE to all images in collection, and reduce to median | |
| ndre_image = imageCollection.map(ndre).median().select('NDRE') | |
| # Get NDRE value at the point | |
| ndre_value = ndre_image.reduceRegion( | |
| reducer=ee.Reducer.first(), | |
| geometry=roi, | |
| scale=10 | |
| ).getInfo() | |
| ndre_value = ndre_value.get('NDRE') #Extract from dictionary | |
| return ndre_value | |
| except Exception as e: | |
| st.error(f"Error calculating NDRE: {e}") | |
| return None | |
| # Streamlit UI | |
| st.title("Farm Parameter Prediction App") | |
| # User input | |
| selected_farm = st.selectbox("Select Farm", farm_names) | |
| farm_age = st.number_input("Farm Age (years)", min_value=0) | |
| farm_variety = st.text_input("Farm Variety") | |
| start_date = st.date_input("Start Date") | |
| end_date = st.date_input("End Date") | |
| #Find Coordinates based on Farm Name Selection | |
| selected_farm_data = farm_data[farm_data['Farm'] == selected_farm] | |
| coordinates = (selected_farm_data['longitude'].iloc[0], selected_farm_data['latitude'].iloc[0]) | |
| if st.button("Calculate NDRE and Show Map"): | |
| ndre_value = calculate_ndre(coordinates, start_date.strftime("%Y-%m-%d"), end_date.strftime("%Y-%m-%d")) | |
| if ndre_value is not None: | |
| st.write(f"NDRE Value: {ndre_value}") | |
| # Map Display (Replace with your actual map display logic) | |
| Map = geemap.Map() | |
| Map.centerObject(ee.Geometry.Point(coordinates), 12) | |
| # Add NDRE layer | |
| #This part requires more detailed specification of the image and display parameters. | |
| #Example (Adapt according to your NDRE image and color scheme) | |
| #vis_params = {'min': 0, 'max': 1, 'palette': ['blue', 'green', 'yellow', 'red']} | |
| #Map.addLayer(ndre_image, vis_params, 'NDRE') | |
| #Display Map | |
| Map.to_streamlit(height=500) | |
| if st.button("Predict"): | |
| user_input = pd.DataFrame({ | |
| 'Farm_Name': [selected_farm], | |
| 'Farm_Age': [farm_age], | |
| 'Farm_Variety': [farm_variety], | |
| 'NDRE': [ndre_value] if ndre_value is not None else [0] #Handle cases where ndre_value is None. Replace 0 with a more suitable default if needed. | |
| }) | |
| # Feature Engineering might be needed here depending on your model's input features | |
| prediction = model.predict(user_input) | |
| st.write("Predictions:") | |
| st.write(f"Brix: {prediction[0][0]}") #Assuming model outputs a list of lists | |
| st.write(f"Pol: {prediction[0][1]}") | |
| st.write(f"Purity: {prediction[0][2]}") | |
| st.write(f"RS: {prediction[0][3]}") | |