Update app.py

app.py

@@ -1,32 +1,115 @@
 import gradio as gr
 import pickle
+import pandas as pd
+from datetime import datetime
+import joblib # Import joblib
+
+# Load the trained model and necessary data structures
+try:
+    # Load the trained model
+    best_model = joblib.load('best_model.joblib')
+
+    # Recreate unique_canteen_info and training_columns based on the original data structure
+    # In a real deployment, these should be saved during training and loaded here.
+    # For this example, we will create dummy data structures based on the assumption of 10 canteens.
+    canteen_ids = [f'C00{i+1}' for i in range(10)]
+    canteen_names = [
+        'VIT University Main Canteen', 'SRM Campus Canteen', 'Anna University Mess',
+        'IIT Madras Hostel Mess', 'Sangeetha Veg Restaurant', 'Murugan Idli Shop',
+        'Adyar Ananda Bhavan (A2B)', 'The Marina Café', 'Buhari Hotel Canteen',
+        'Crescent College Cafeteria'
+    ]
+    unique_canteen_info = pd.DataFrame({'canteen_id': canteen_ids, 'canteen_name': canteen_names})
+
+    # Create a dummy DataFrame with all possible categories to get the column structure for one-hot encoding
+    dummy_data_for_cols = pd.DataFrame(columns=['canteen_id', 'canteen_name'])
+    for cid in canteen_ids:
+        for cname in canteen_names:
+             dummy_data_for_cols = pd.concat([dummy_data_for_cols, pd.DataFrame({'canteen_id': [cid], 'canteen_name': [cname]})], ignore_index=True)
+
+    dummy_encoded_for_cols = pd.get_dummies(dummy_data_for_cols, columns=['canteen_id', 'canteen_name'], drop_first=True)
+    training_columns = dummy_encoded_for_cols.columns.tolist()
+
+
+except FileNotFoundError:
+    best_model = None
+    unique_canteen_info = None
+    training_columns = None
+    print("Error: best_model.joblib not found. Model loading failed.")
+except Exception as e:
+    best_model = None
+    unique_canteen_info = None
+    training_columns = None
+    print(f"An error occurred during model loading: {e}")
+
+
+def predict_surplus(day, month, year):
+    """Predicts surplus units for all canteens for a given date."""
+    if best_model is None or unique_canteen_info is None or training_columns is None:
+        return "Model or necessary data not loaded. Cannot make predictions."
 
-def load_and_display_data():
-    """Loads the pickled data and formats it for display."""
     try:
-        with open('canteen_surplus_data.pkl', 'rb') as f:
-            data = pickle.load(f)
-        # Format the dictionary for better display in Gradio
-        formatted_output = ""
-        for canteen_id, info in data.items():
-            formatted_output += f"Canteen ID: {canteen_id}\n"
-            formatted_output += f"  Canteen Name: {info['canteen_name']}\n"
-            formatted_output += f"  Predicted Surplus: {info['predicted_surplus']}\n"
-            formatted_output += f"  NGO Requirement: {info['ngo_requirement']}\n"
-            formatted_output += "-" * 20 + "\n"
-        return formatted_output
-    except FileNotFoundError:
-        return "Error: canteen_surplus_data.pkl not found."
-    except Exception as e:
-        return f"An error occurred: {e}"
-
-# Create a Gradio interface to display the data
+        prediction_date = datetime(year, month, day)
+    except ValueError:
+        return "Invalid date provided. Please enter valid day, month, and year."
+
+    # Create prediction DataFrame
+    prediction_df = unique_canteen_info.copy()
+    prediction_df['year'] = prediction_date.year
+    prediction_df['month'] = prediction_date.month
+    prediction_df['day'] = prediction_date.day
+    prediction_df['day_of_week'] = prediction_date.weekday() + 1 # Monday is 0, so add 1 to match the original data
+    prediction_df['day_of_year'] = prediction_date.timetuple().tm_yday
+
+    # One-hot encode and align columns with training data
+    prediction_encoded = pd.get_dummies(prediction_df, columns=['canteen_id', 'canteen_name'], drop_first=True)
+
+    for col in training_columns:
+        if col not in prediction_encoded.columns:
+            prediction_encoded[col] = False
+
+    # Ensure the order of columns matches the training data features (excluding the target)
+    # Recreate the feature columns list based on the training_columns plus the time features
+    feature_columns = ['day', 'month', 'year', 'day_of_week', 'day_of_year'] + training_columns
+    prediction_encoded = prediction_encoded[feature_columns]
+
+    # Make predictions
+    predicted_surplus_values = best_model.predict(prediction_encoded)
+
+    # Create a dictionary for output
+    output_data = {}
+    for i, row in unique_canteen_info.iterrows():
+        canteen_id = row['canteen_id']
+        canteen_name = row['canteen_name']
+        predicted_surplus = max(0, int(round(predicted_surplus_values[i]))) # Ensure non-negative integer
+
+        output_data[canteen_id] = {
+            'canteen_name': canteen_name,
+            'predicted_surplus': predicted_surplus
+        }
+
+    # Format the dictionary for better display in Gradio
+    formatted_output = "Predicted Surplus Units:\n\n"
+    for canteen_id, info in output_data.items():
+        formatted_output += f"Canteen ID: {canteen_id}\n"
+        formatted_output += f"  Canteen Name: {info['canteen_name']}\n"
+        formatted_output += f"  Predicted Surplus: {info['predicted_surplus']}\n"
+        formatted_output += "-" * 20 + "\n"
+
+    return formatted_output
+
+
+# Create a Gradio interface with inputs for day, month, and year
 iface = gr.Interface(
-    fn=load_and_display_data,
-    inputs=None,  # No input needed to display the data
+    fn=predict_surplus,
+    inputs=[
+        gr.Number(label="Day", precision=0),
+        gr.Number(label="Month", precision=0),
+        gr.Number(label="Year", precision=0)
+    ],
     outputs="text",
-    title="Canteen Surplus and NGO Requirements",
-    description="Displays the predicted surplus units and NGO requirements for each canteen."
+    title="Predict Canteen Surplus Units",
+    description="Enter a date (day, month, year) to predict the surplus units for each canteen."
 )
 
 iface.launch()