Graph update

app.py

@@ -29,17 +29,47 @@ def predict_and_plot(dirty, wait, lastyear, usa):
     # Predicting on test set for comparison
     y_pred = model.predict(X_test)
 
-    # Plotting actual vs predicted values
-    plt.figure(figsize=(8, 6))
-    plt.scatter(range(len(y_test)), y_test, color='blue', label='Actual Values', alpha=0.6)
-    plt.scatter(range(len(y_pred)), y_pred, color='red', label='Predicted Values', alpha=0.6)
-    plt.title('Actual vs Predicted Values')
-    plt.xlabel('Sample Index')
-    plt.ylabel('Value')
-    plt.legend()
-    plt.grid(True)
-
-    # Save plot to a file and display
+    # Creating subplots for each variable and showing predicted value
+    fig, axs = plt.subplots(2, 2, figsize=(12, 10))
+    
+    # Plot dirty variable distribution with predicted value
+    axs[0, 0].hist(X_test[:, 0], bins=30, color='gray', alpha=0.5, label='Dirty Distribution')
+    axs[0, 0].axvline(dirty, color='orange', linestyle='--', label='Input Value (Dirty)')
+    axs[0, 0].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value')
+    axs[0, 0].set_title('Distribution of Dirty')
+    axs[0, 0].set_xlabel('Dirty')
+    axs[0, 0].set_ylabel('Frequency')
+    axs[0, 0].legend()
+
+    # Plot wait variable distribution with predicted value
+    axs[0, 1].hist(X_test[:, 1], bins=30, color='gray', alpha=0.5, label='Wait Distribution')
+    axs[0, 1].axvline(wait, color='orange', linestyle='--', label='Input Value (Wait)')
+    axs[0, 1].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value')
+    axs[0, 1].set_title('Distribution of Wait')
+    axs[0, 1].set_xlabel('Wait')
+    axs[0, 1].set_ylabel('Frequency')
+    axs[0, 1].legend()
+
+    # Plot lastyear variable distribution with predicted value
+    axs[1, 0].hist(X_test[:, 2], bins=30, color='gray', alpha=0.5, label='Lastyear Distribution')
+    axs[1, 0].axvline(lastyear, color='orange', linestyle='--', label='Input Value (Lastyear)')
+    axs[1, 0].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value')
+    axs[1, 0].set_title('Distribution of Lastyear')
+    axs[1, 0].set_xlabel('Lastyear')
+    axs[1, 0].set_ylabel('Frequency')
+    axs[1, 0].legend()
+
+    # Plot usa variable distribution with predicted value
+    axs[1, 1].hist(X_test[:, 3], bins=30, color='gray', alpha=0.5, label='USA Distribution')
+    axs[1, 1].axvline(usa, color='orange', linestyle='--', label='Input Value (USA)')
+    axs[1, 1].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value')
+    axs[1, 1].set_title('Distribution of USA')
+    axs[1, 1].set_xlabel('USA')
+    axs[1, 1].set_ylabel('Frequency')
+    axs[1, 1].legend()
+
+    # Adjust layout and save the plot
+    plt.tight_layout()
     plt.savefig('output_plot.png')
     plt.close()
 
@@ -50,9 +80,9 @@ with gr.Blocks() as demo:
     gr.Markdown("# Logistic Regression Prediction")
 
     with gr.Row():
-        dirty_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="Dirty")
-        wait_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="Wait")
-        lastyear_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="Last Year")
+        dirty_slider = gr.Slider(minimum=0, maximum=6, step=0.01, label="Dirty")
+        wait_slider = gr.Slider(minimum=0, maximum=5.3, step=0.01, label="Wait")
+        lastyear_slider = gr.Slider(minimum=0, maximum=70, step=0.01, label="Last Year")
         usa_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="USA")
 
     predict_button = gr.Button("Predict")