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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) | |
| 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') | |
| # Load farm data | |
| farm_data = pd.read_csv('Farm_NDRE_TimeSeries.csv') | |
| farm_names = farm_data['Farm'].tolist() | |
| # Function to calculate NDRE | |
| def calculate_ndre(coordinates, start_date, end_date): | |
| try: | |
| roi = ee.Geometry.Point(coordinates) | |
| imageCollection = ee.ImageCollection('COPERNICUS/S2_SR') \ | |
| .filterBounds(roi) \ | |
| .filterDate(start_date, end_date) \ | |
| .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)) | |
| 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')) | |
| ndre_image = imageCollection.map(ndre).median().select('NDRE') | |
| ndre_value = ndre_image.reduceRegion( | |
| reducer=ee.Reducer.first(), | |
| geometry=roi, | |
| scale=10 | |
| ).getInfo() | |
| return ndre_value.get('NDRE') if ndre_value else None | |
| 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") | |
| 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('نمایش نقشه NDRE'): | |
| NDRE = calculate_ndre(coordinates, start_date, end_date) | |
| if NDRE is not None: | |
| st.session_state.ndre_value = NDRE # Store NDRE in session state | |
| st.write(f'شاخص NDRE: {NDRE}') | |
| Map = geemap.Map() | |
| Map.centerObject(ee.Geometry.Point(coordinates), 12) | |
| vis_params = {'min': 0, 'max': 1, 'palette': ['blue', 'green', 'yellow', 'red']} | |
| Map.addLayer(NDRE, vis_params, 'NDRE') | |
| Map.to_streamlit(height=500) | |
| else: | |
| st.error("Unable to calculate NDRE.") | |
| if st.button("Predict"): | |
| # Retrieve NDRE value from session state, default to 0 if not set | |
| ndre_value = st.session_state.get('ndre_value', 0) | |
| # Prepare the user input DataFrame | |
| user_input = pd.DataFrame({ | |
| 'Age': [farm_age], # Rename to match model's expected feature | |
| 'Variety': [farm_variety], # Rename to match model's expected feature | |
| 'NDRE': [ndre_value] # Use the retrieved NDRE value | |
| }) | |
| # Additional features: calculate DayOfYear and Month from the start date | |
| if start_date: | |
| day_of_year = start_date.timetuple().tm_yday | |
| month = start_date.month | |
| user_input['DayOfYear'] = [day_of_year] | |
| user_input['Month'] = [month] | |
| # Reorder the columns to match the order expected by the model | |
| user_input = user_input[['Age', 'DayOfYear', 'Month', 'Variety', 'NDRE']] | |
| # Make predictions | |
| 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]}") |