Create app.py

app.py

@@ -0,0 +1,180 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Global variable to store history of attempts
+history = []
+
+def predict_house_price(area):
+    """Simple house price prediction based on area"""
+    # Using the simple formula: price = 0.1 * area (as per your slides)
+    price = 0.1 * area
+    return float(price)
+
+def calculate_sse(x, y, m, b):
+    """Calculate Sum of Squared Errors"""
+    y_predicted = m * x + b
+    sse = np.sum((y - y_predicted) ** 2)
+    return sse
+
+def plot_regression(data, m, b):
+    try:
+        df = data if isinstance(data, pd.DataFrame) else pd.read_csv(data)
+        df['X'] = pd.to_numeric(df['X'])
+        df['Y'] = pd.to_numeric(df['Y'])
+        
+        sse = calculate_sse(df['X'], df['Y'], m, b)
+        
+        history.append({
+            'm': m,
+            'b': b,
+            'sse': sse,
+            'color': plt.cm.rainbow(len(history) % 10 / 10)
+        })
+        
+        fig = plt.figure(figsize=(15, 6))
+        
+        # First subplot - Regression lines
+        ax1 = fig.add_subplot(121)
+        ax1.scatter(df['X'], df['Y'], color='black', alpha=0.5, label='Data points')
+        
+        for i, attempt in enumerate(history):
+            x_range = np.linspace(df['X'].min(), df['X'].max(), 100)
+            y_line = attempt['m'] * x_range + attempt['b']
+            label = f"m={attempt['m']:.1f}, b={attempt['b']:.1f}"
+            ax1.plot(x_range, y_line, color=attempt['color'], linewidth=2, 
+                    label=f"Try {i+1}: {label}")
+        
+        ax1.set_xlabel('X')
+        ax1.set_ylabel('Y')
+        ax1.set_title('Linear Regression Attempts')
+        ax1.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
+        
+        # Second subplot - SSE values
+        ax2 = fig.add_subplot(122)
+        attempts = range(1, len(history) + 1)
+        sse_values = [attempt['sse'] for attempt in history]
+        colors = [attempt['color'] for attempt in history]
+        
+        ax2.scatter(attempts, sse_values, c=colors)
+        ax2.plot(attempts, sse_values, 'gray', alpha=0.3)
+        
+        for i, (attempt, sse) in enumerate(zip(attempts, sse_values)):
+            label = f"m={history[i]['m']:.1f}\nb={history[i]['b']:.1f}"
+            ax2.annotate(label, (attempt, sse), 
+                        xytext=(5, 5), textcoords='offset points')
+        
+        ax2.set_xlabel('Attempt Number')
+        ax2.set_ylabel('Sum of Squared Errors')
+        ax2.set_title('SSE for Each Attempt')
+        ax2.grid(True, alpha=0.3)
+        
+        plt.tight_layout()
+        plt.close()
+        return fig
+        
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
+
+def clear_history():
+    history.clear()
+    return None
+
+# Create the Gradio interface with tabs
+with gr.Blocks() as app:
+    gr.Markdown("# Linear Regression Learning Tools")
+    
+    with gr.Tabs():
+        # First Tab - House Price Prediction
+        with gr.TabItem("House Price Predictor"):
+            gr.Markdown("""
+            # House Price Predictor
+            Enter the area of the house (in m²) to predict its price.
+            Based on the simple model: Price = 0.1 × Area
+            """)
+            
+            with gr.Row():
+                area_input = gr.Number(
+                    label="House Area (m²)",
+                    value=100
+                )
+                price_output = gr.Number(
+                    label="Predicted Price ($M)",
+                    value=None
+                )
+            
+            predict_button = gr.Button("Predict Price")
+            predict_button.click(
+                fn=predict_house_price,
+                inputs=area_input,
+                outputs=price_output
+            )
+            
+            # Example table
+            gr.Markdown("""
+            ### Example Data Points:
+            | Area (m²) | Price ($M) |
+            |-----------|------------|
+            | 100       | 10         |
+            | 200       | 20         |
+            | 300       | 30         |
+            | 400       | 40         |
+            | 500       | 50         |
+            """)
+
+        # Second Tab - Regression Playground
+        with gr.TabItem("Regression Playground"):
+            gr.Markdown("""
+            # Linear Regression Playground
+            Explore how slope (m) and intercept (b) affect the line of best fit:
+            - Enter data points in the table
+            - Adjust the sliders to fit the line
+            - Click Submit to see your attempt
+            """)
+            
+            with gr.Row():
+                data_input = gr.Dataframe(
+                    headers=["X", "Y"],
+                    datatype=["number", "number"],
+                    row_count=5,
+                    col_count=2,
+                    label="Dataset",
+                    interactive=True,
+                    value=[[100, 10],
+                          [200, 20],
+                          [300, 30],
+                          [400, 40],
+                          [500, 50]]
+                )
+                
+                with gr.Column():
+                    m_slider = gr.Slider(
+                        minimum=-10,
+                        maximum=10,
+                        value=1.0,
+                        step=0.1,
+                        label="Slope (m)",
+                    )
+
+                    b_slider = gr.Slider(
+                        minimum=-10,
+                        maximum=10,
+                        value=0.0,
+                        step=0.1,
+                        label="Intercept (b)",
+                    )
+                    
+                    submit_button = gr.Button("Submit")
+                    clear_button = gr.Button("Clear History")
+            
+            plot_output = gr.Plot()
+            
+            # Set up the event handlers
+            inputs = [data_input, m_slider, b_slider]
+            clear_button.click(fn=clear_history, inputs=None, outputs=plot_output)
+            submit_button.click(fn=plot_regression, inputs=inputs, outputs=plot_output)
+
+if __name__ == "__main__":
+    app.launch(show_api=False)