Update app.py

app.py

@@ -113,42 +113,36 @@ def forecast_solar_energy(historical_data):
 # Read and preprocess CSV data for prediction
 def preprocess_csv_for_prediction(csv_file):
     try:
-        # Load the dataset from CSV
+        # 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 list(data.columns) != expected_columns:
-            return f"Error: Expected columns {expected_columns}, but got {list(data.columns)}"
+        if not all(col in data.columns for col in expected_columns):
+            return f"Error: Missing expected columns. Required: {expected_columns}"
 
-        # Ensure there are at least 43 rows for prediction
-        if data.shape[0] < 43:
-            return "Error: CSV must have at least 43 rows of data for prediction"
+        # Select the relevant columns (ensure all 43 features are included)
+        last_row = data.tail(1)[expected_columns]  # Use only the last row of data
 
-        # Select the relevant columns for the LSTM model
-        relevant_columns = ['Irradiance(W/m^2)', 'Panel_Efficiency', 'Wind_Speed(km/h)', 
-                            'Cloud_Cover(%)', 'temperature (°f)']
-        last_43_days = data.tail(43)[relevant_columns]
-
-        # Handle missing values (replace NaNs with 0)
-        if last_43_days.isnull().any().any():
-            last_43_days.fillna(0, inplace=True)
+        # Fill missing values
+        last_row.fillna(0, inplace=True)
 
-        # Convert to numpy array and reshape for LSTM input
-        data_array = last_43_days.values.astype(np.float32)
-        sequence = np.reshape(data_array, (1, 43, len(relevant_columns)))
+        # 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, 
-                                panel_length, panel_width, irradiance, electricity_rate, historical_data_csv):
+                               panel_length, panel_width, irradiance, electricity_rate, historical_data_csv):
     try:
         # Process the uploaded image
         if image is None: