Update app.py

app.py

@@ -1,144 +1,144 @@
-import os
-import gradio as gr
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from model import GDPPredictor
-
-# Initialize the predictor
-predictor = GDPPredictor()
-
-# Check if we have models saved
-model_file = 'gdp_models.pkl'
-data_file = 'Consolidated.csv'
-
-# Initialize and train models if needed
-if os.path.exists(model_file):
-    print("Loading pre-trained models...")
-    predictor.load_models(model_file)
-    predictor.load_data(data_file)
-else:
-    print("Training new models...")
-    predictor.load_data(data_file)
-    predictor.train_models()
-    predictor.save_models(model_file)
-
-# Get latest GDP for reference
-latest_year, latest_gdp = predictor.get_latest_gdp()
-
-# Create a dictionary of all features and their default values (current values)
-feature_info = predictor.get_feature_info()
-
-# Create sliders for each feature, organized by category
-def create_feature_inputs():
-    inputs = []
-    
-    # For each category
-    for category, features in feature_info.items():
-        # Add a label for the category
-        inputs.append(gr.Markdown(f"## {category}"))
-        
-        # Add sliders for each feature in this category
-        for feature_name, (min_val, max_val, mean_val, current_val) in features.items():
-            # Adjust slider range to be a bit wider than historical data
-            slider_min = min_val * 0.9
-            slider_max = max_val * 1.1
-            
-            # Create a slider for this feature
-            slider = gr.Slider(
-                minimum=slider_min,
-                maximum=slider_max,
-                value=current_val,  # Default to current value
-                step=(slider_max - slider_min) / 100,  # 100 steps across range
-                label=feature_name
-            )
-            inputs.append(slider)
-    
-    return inputs
-
-def predict(*feature_values):
-    # Get all input features as flat list
-    flat_inputs = list(feature_values)
-    
-    # Map features to values
-    feature_names = []
-    for category, features in feature_info.items():
-        for feature_name in features:
-            feature_names.append(feature_name)
-    
-    # Create input dictionary
-    input_dict = {feature_names[i]: flat_inputs[i] for i in range(len(feature_names))}
-    
-    # Make prediction
-    try:
-        predictions = predictor.predict_gdp(input_dict)
-        
-        # Get ensemble prediction and calculate change
-        ensemble_pred = predictions['Ensemble']
-        change = ensemble_pred - latest_gdp
-        pct_change = (change / latest_gdp) * 100
-        
-        # Format results
-        result_text = f"# GDP Prediction Results\n\n"
-        result_text += f"## Primary Prediction\n"
-        result_text += f"**Ensemble Model:** {ensemble_pred:.2f} USD billion\n\n"
-        result_text += f"## Comparison with {latest_year} GDP ({latest_gdp:.2f} USD billion)\n"
-        result_text += f"**Change:** {change:.2f} USD billion ({pct_change:.2f}%)\n\n"
-        result_text += f"## All Model Predictions\n"
-        
-        # Add all individual model predictions
-        for name, pred in sorted(predictions.items(), key=lambda x: x[1], reverse=True):
-            if name != 'Ensemble':
-                result_text += f"- **{name}:** {pred:.2f} USD billion\n"
-        
-        # Create visualization
-        fig, ax = plt.subplots(figsize=(10, 6))
-        
-        # Get last 10 years data
-        df = predictor.cleaned_df
-        last_years = df.sort_values('Year').tail(10)
-        ax.plot(last_years['Year'], last_years[predictor.target], 'o-', linewidth=2, label='Historical GDP')
-        
-        # Add prediction point
-        pred_year = latest_year + 1
-        ax.scatter([pred_year], [predictions['Ensemble']], color='green', s=150, label='Prediction')
-        
-        # Format plot
-        ax.set_title('GDP Prediction', fontsize=14)
-        ax.set_xlabel('Year', fontsize=12)
-        ax.set_ylabel('Real GDP (USD billion)', fontsize=12)
-        ax.grid(True, alpha=0.3)
-        ax.legend()
-        
-        return result_text, fig
-    
-    except Exception as e:
-        return f"Error making prediction: {str(e)}", None
-
-# Create the interface
-with gr.Blocks(title="GDP Predictor") as demo:
-    gr.Markdown("# GDP Prediction Model")
-    gr.Markdown(f"""
-    This application predicts GDP based on various economic indicators. The current dataset contains data up to the year {latest_year}.
-    
-    Adjust the sliders below to see how changes in different economic indicators might affect GDP.
-    The default values are set to the most recent values from the dataset.
-    """)
-    
-    with gr.Row():
-        with gr.Column(scale=2):
-            # Create input sliders from feature info
-            inputs = create_feature_inputs()
-        
-        with gr.Column(scale=3):
-            # Output components
-            prediction_text = gr.Markdown("Adjust sliders and click 'Predict' to see results")
-            prediction_plot = gr.Plot(label="GDP Prediction Visualization")
-    
-    # Predict button
-    predict_btn = gr.Button("Predict GDP")
-    predict_btn.click(fn=predict, inputs=inputs, outputs=[prediction_text, prediction_plot])
-
-# Launch the app
-if __name__ == "__main__":
+import os
+import gradio as gr
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from model import GDPPredictor
+
+# Initialize the predictor
+predictor = GDPPredictor()
+
+# Check if we have models saved
+model_file = 'gdp_models.pkl'
+data_file = 'Consolidated.csv'
+
+# Initialize and train models if needed
+if os.path.exists(model_file):
+    print("Loading pre-trained models...")
+    predictor.load_models(model_file)
+    predictor.load_data(data_file)
+else:
+    print("Training new models...")
+    predictor.load_data(data_file)
+    predictor.train_models()
+    predictor.save_models(model_file)
+
+# Get latest GDP for reference
+latest_year, latest_gdp = predictor.get_latest_gdp()
+
+# Create a dictionary of all features and their default values (current values)
+feature_info = predictor.get_feature_info()
+
+# Create sliders for each feature, organized by category
+def create_feature_inputs():
+    inputs = []
+    
+    # For each category
+    for category, features in feature_info.items():
+        # Add a label for the category
+        inputs.append(gr.Markdown(f"## {category}"))
+        
+        # Add sliders for each feature in this category
+        for feature_name, (min_val, max_val, mean_val, current_val) in features.items():
+            # Adjust slider range to be a bit wider than historical data
+            slider_min = min_val * 0.9
+            slider_max = max_val * 1.1
+            
+            # Create a slider for this feature
+            slider = gr.Slider(
+                minimum=slider_min,
+                maximum=slider_max,
+                value=current_val,  # Default to current value
+                step=(slider_max - slider_min) / 100,  # 100 steps across range
+                label=feature_name
+            )
+            inputs.append(slider)
+    
+    return inputs
+
+def predict(*feature_values):
+    # Get all input features as flat list
+    flat_inputs = list(feature_values)
+    
+    # Map features to values
+    feature_names = []
+    for category, features in feature_info.items():
+        for feature_name in features:
+            feature_names.append(feature_name)
+    
+    # Create input dictionary
+    input_dict = {feature_names[i]: flat_inputs[i] for i in range(len(feature_names))}
+    
+    # Make prediction
+    try:
+        predictions = predictor.predict_gdp(input_dict)
+        
+        # Get ensemble prediction and calculate change
+        ensemble_pred = predictions['Ensemble']
+        change = ensemble_pred - latest_gdp
+        pct_change = (change / latest_gdp) * 100
+        
+        # Format results
+        result_text = f"# GDP Prediction Results\n\n"
+        result_text += f"## Primary Prediction\n"
+        result_text += f"**Ensemble Model:** {ensemble_pred:.2f} USD billion\n\n"
+        result_text += f"## Comparison with {latest_year} GDP ({latest_gdp:.2f} USD billion)\n"
+        result_text += f"**Change:** {change:.2f} USD billion ({pct_change:.2f}%)\n\n"
+        result_text += f"## All Model Predictions\n"
+        
+        # Add all individual model predictions
+        for name, pred in sorted(predictions.items(), key=lambda x: x[1], reverse=True):
+            if name != 'Ensemble':
+                result_text += f"- **{name}:** {pred:.2f} USD billion\n"
+        
+        # Create visualization
+        fig, ax = plt.subplots(figsize=(10, 6))
+        
+        # Get last 10 years data
+        df = predictor.cleaned_df
+        last_years = df.sort_values('Year').tail(10)
+        ax.plot(last_years['Year'], last_years[predictor.target], 'o-', linewidth=2, label='Historical GDP')
+        
+        # Add prediction point
+        pred_year = latest_year + 1
+        ax.scatter([pred_year], [predictions['Ensemble']], color='green', s=150, label='Prediction')
+        
+        # Format plot
+        ax.set_title('GDP Prediction', fontsize=14)
+        ax.set_xlabel('Year', fontsize=12)
+        ax.set_ylabel('Real GDP (USD billion)', fontsize=12)
+        ax.grid(True, alpha=0.3)
+        ax.legend()
+        
+        return result_text, fig
+    
+    except Exception as e:
+        return f"Error making prediction: {str(e)}", None
+
+# Create the interface
+with gr.Blocks(title="GDP Predictor") as demo:
+    gr.Markdown("# GDP Prediction Model")
+    gr.Markdown(f"""
+    This application predicts GDP based on various economic indicators. The current dataset contains data up to the year {latest_year}.
+    
+    Adjust the sliders below to see how changes in different economic indicators might affect GDP.
+    The default values are set to the most recent values from the dataset.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            # Create input sliders from feature info
+            inputs = create_feature_inputs()
+        
+        with gr.Column(scale=3):
+            # Output components
+            prediction_text = gr.Markdown("Adjust sliders and click 'Predict' to see results")
+            prediction_plot = gr.Plot(label="GDP Prediction Visualization")
+    
+    # Predict button
+    predict_btn = gr.Button("Predict GDP")
+    predict_btn.click(fn=predict, inputs=inputs, outputs=[prediction_text, prediction_plot])
+
+# Launch the app
+if __name__ == "__main__":
     demo.launch()