Update app.py

app.py

@@ -115,30 +115,37 @@ def preprocess_csv_for_prediction(csv_file):
     try:
         # Load the dataset
         data = pd.read_csv(csv_file.name)
-
+        
         # Validate column names
         expected_columns = ['Month', 'Hour', 'Irradiance(W/m^2)', 'Latitude', 'Longitude', 
                             'Panel_Capacity(W)', 'Panel_Efficiency', 'Wind_Speed(km/h)', 
                             'Cloud_Cover(%)', 'temperature (°f)']
         if not all(col in data.columns for col in expected_columns):
             return f"Error: Missing expected columns. Required: {expected_columns}"
-
-        # Select the relevant columns (ensure all 43 features are included)
-        last_row = data.tail(1)[expected_columns]  # Use only the last row of data
-
-        # Fill missing values
-        last_row.fillna(0, inplace=True)
-
+        
+        # Ensure columns with string values like 'Month' are removed or processed
+        data = data[expected_columns]
+        
+        # Convert all non-numeric columns to numeric (handling errors where conversion is not possible)
+        data = data.apply(pd.to_numeric, errors='coerce')
+        
+        # Handle any missing values (NaNs)
+        data.fillna(0, inplace=True)
+        
+        # Select the relevant last row for prediction
+        last_row = data.tail(1)
+        
         # Reshape the data to match the model's input shape
         sequence = last_row.values.astype(np.float32)
         sequence = sequence.reshape((1, 1, 43))  # (batch_size, time_step, features)
-
+        
         return sequence
     except Exception as e:
         return f"Error in preprocessing CSV: {str(e)}"
 
 
 
+
 # Function to process image and forecast energy using CSV file
 # Function to process image and forecast energy using CSV file
 def process_image_and_forecast(image, min_area, pixel_area, clip_limit, tile_size, panel_type,