Update app.py

app.py

@@ -5,56 +5,45 @@ 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  
+model = joblib.load('updated_model.pkl')  
 
 # Load farm data  
-farm_data = pd.read_csv('Farm_NDRE_TimeSeries.csv')  # Replace 'Farm_NDRE_TimeSeries.csv' with your actual data file  
+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:  
-        # 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  
+            .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))  
             
-        # 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  
+        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")  
 
@@ -65,44 +54,27 @@ 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])  
 
-
-# Inside the 'نمایش نقشه NDRE' button's code block
 if st.button('نمایش نقشه NDRE'):
-    # Call the function and store the returned value in 'NDRE'
     NDRE = calculate_ndre(coordinates, start_date, end_date)
-    st.write(f'شاخص NDRE: {NDRE}') 
-        # Map Display (Replace with your actual map display logic)  
+    if NDRE is not None:
+        st.session_state.ndre_value = NDRE
+        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')  
-
-        #Display Map  
         Map.to_streamlit(height=500)  
+    else:
+        st.error("Unable to calculate NDRE.")
 
 if st.button("Predict"):  
+    ndre_value = st.session_state.get('ndre_value', 0)  # Default to 0 if not calculated
     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]}")
-
-
-
+        'Farm_Variety': [farm_variety],