Update app.py

app.py

@@ -4,25 +4,27 @@ import joblib
 import ee  
 import geemap  
 
-# Earth Engine Authentication (Replace with your actual authentication)  
+# Authenticate 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  
+# Load model and farm data
 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  
+# Function to calculate NDRE
 def calculate_ndre(coordinates, start_date, end_date):  
     try:  
+        # Convert start_date and end_date to strings
+        start_date_str = start_date.strftime('%Y-%m-%d')
+        end_date_str = end_date.strftime('%Y-%m-%d')
+
         roi = ee.Geometry.Point(coordinates)  
         imageCollection = ee.ImageCollection('COPERNICUS/S2_SR') \
             .filterBounds(roi) \
-            .filterDate(start_date, end_date) \
+            .filterDate(start_date_str, end_date_str) \
             .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))  
             
         def ndre(image):  
@@ -44,16 +46,15 @@ def calculate_ndre(coordinates, start_date, end_date):
         st.error(f"Error calculating NDRE: {e}")  
         return None  
 
-# Streamlit UI  
+# Streamlit User Interface
 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")  
 
+# Handling Farm Data Selection and NDRE Calculation
 selected_farm_data = farm_data[farm_data['Farm'] == selected_farm]  
 coordinates = (selected_farm_data['longitude'].iloc[0], selected_farm_data['latitude'].iloc[0])  
 
@@ -67,37 +68,33 @@ if st.button('نمایش نقشه NDRE'):
         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.addLayer(ee.Image(NDRE), vis_params, 'NDRE')  
         Map.to_streamlit(height=500)  
     else:
         st.error("Unable to calculate NDRE.")
 
+# Making Predictions Using the Loaded Model
 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
+        'Age': [farm_age],  
+        'Variety': [farm_variety],  
+        'NDRE': [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"Brix: {prediction[0][0]}")  
     st.write(f"Pol: {prediction[0][1]}")  
     st.write(f"Purity: {prediction[0][2]}")  
-    st.write(f"RS: {prediction[0][3]}")
+    st.write(f"RS: {prediction[0][3]}")