Update app.py

app.py

@@ -297,75 +297,17 @@ with torch.no_grad():
     train_preds = model(X_train_tensor).argmax(dim=1)
     train_acc = (train_preds == torch.tensor(y_train_raw)).float().mean().item()
 
-# === Gradio App ===
 with gr.Blocks() as demo:
-    gr.Markdown("# 🥛 NIR Milk Spectroscopy Analysis App")
+    gr.Markdown("# 🧪 Dataset Description")
 
     with gr.Tabs():
         with gr.Tab("Preview Raw Data"):
-            gr.DataFrame(df.head(50), label="Milk Spectra")
+            gr.DataFrame(df.head(50), label="Preview of Raw Data")
 
         with gr.Tab("Visualizations"):
-            def plot_all():
-                plots = []
-                for i in range(8):
-                    fig, ax = plt.subplots()
-                    ax.plot(X[i])
-                    ax.set_title(f"Spectrum {i+1}")
-                    plots.append(fig)
-                return plots
             plot_button = gr.Button("Generate Spectroscopy Visualizations")
-            output_plots = [gr.Plot() for _ in range(8)]
-            plot_button.click(fn=plot_all, inputs=[], outputs=output_plots)
-
-        with gr.Tab("Models"):
-            with gr.Tabs():
-                with gr.Tab("Random Forest"):
-                    gr.Markdown(f"✅ Train Accuracy: **{accuracy_score(y_train, rf.predict(X_train)):.2f}**<br>🎯 Test Accuracy: **{accuracy_score(y_test, rf.predict(X_test)):.2f}**")
-                    fig_rf = plt.figure()
-                    sns.heatmap(confusion_matrix(y_test, rf.predict(X_test)), annot=True, fmt='d')
-                    plt.title("Random Forest Confusion Matrix")
-                    gr.Plot(fig_rf)
-
-                with gr.Tab("Decision Tree"):
-                    gr.Markdown(f"✅ Train Accuracy: **{accuracy_score(y_train, dt.predict(X_train)):.2f}**<br>🎯 Test Accuracy: **{accuracy_score(y_test, dt.predict(X_test)):.2f}**")
-                    fig_dt = plt.figure()
-                    sns.heatmap(confusion_matrix(y_test, dt.predict(X_test)), annot=True, fmt='d')
-                    plt.title("Decision Tree Confusion Matrix")
-                    gr.Plot(fig_dt)
-
-                with gr.Tab("1D CNN"):
-                    gr.Markdown(f"✅ Train Accuracy: **{train_acc:.2f}**<br>🎯 Test Accuracy: **{test_acc:.2f}**")
-                    fig_cnn = plt.figure()
-                    sns.heatmap(confusion_matrix(y_test_raw, test_preds), annot=True, fmt='d')
-                    plt.title("1D CNN Confusion Matrix")
-                    gr.Plot(fig_cnn)
-
-        with gr.Tab("Prediction"):
-            model_choice = gr.Dropdown(['Random Forest', 'Decision Tree', '1D CNN'], label="Choose Model")
-            input_file = gr.File(label="Upload CSV (same format)")
-            output_table = gr.DataFrame(label="Predictions")
-
-            def predict(file, model_choice):
-                df_new = pd.read_csv(file.name)
-                if 'Label' in df_new.columns:
-                    df_new = df_new.drop(columns=['Label'])
-                X_input = df_new.values
-
-                if model_choice == "1D CNN":
-                    X_input_scaled = scaler.transform(X_input)
-                    tensor_input = torch.tensor(X_input_scaled, dtype=torch.float32).unsqueeze(1)
-                    with torch.no_grad():
-                        preds = model(tensor_input).argmax(dim=1).numpy()
-                else:
-                    X_input_pca = pca.transform(scaler.transform(X_input))
-                    preds = rf.predict(X_input_pca) if model_choice == "Random Forest" else dt.predict(X_input_pca)
-
-                df_new['Predicted Label'] = le.inverse_transform(preds)
-                return df_new
-
-            predict_btn = gr.Button("Predict")
-            predict_btn.click(predict, inputs=[input_file, model_choice], outputs=[output_table])
+            out_gallery = [gr.Plot() for _ in range(8)]
+            plot_button.click(fn=plot_all, inputs=[], outputs=out_gallery)
 
         with gr.Tab("Takeaways"):
             gr.Markdown("## 🌾 Spectroscopy: Transforming the Dairy Sector")
@@ -392,5 +334,19 @@ with gr.Blocks() as demo:
 Stay curious. Stay healthy.
 """)
 
-# === Run the app ===
-demo.launch(server_name="0.0.0.0", server_port=7860)
+        with gr.Tab("Random Forest"):
+            gr.Image(value="random_forest_result.png", label="Random Forest Output")
+
+        with gr.Tab("Decision Tree"):
+            gr.Markdown("**Confusion Matrix**")
+            gr.Image(value="decision_tree_confusion.png", label="Confusion Matrix")
+            gr.Markdown("**Decision Tree Visualization**")
+            gr.Image(value="decision_tree_model.png", label="Tree Structure")
+            gr.Image(value="decision_tree_extra.png", label="Additional Insight")
+
+        with gr.Tab("1D CNN (Raw Data)"):
+            gr.Image(value="cnn_result_image.png", label="1D CNN Output")
+
+    demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False)
+
+