Update app.py

app.py

@@ -40,15 +40,31 @@ def clean_data(df):
     df = df.fillna(df.mean(numeric_only=True))
     return df
 
-def upload_file(file):
+def upload_file(file_path):
+    """
+    file_path comes in as a str because type="filepath".
+    We catch any error and return a tiny DataFrame with the message.
+    """
     global df_global
-    if file is None:
-        return pd.DataFrame({"Error": ["No file uploaded."]})
-    ext = os.path.splitext(file.name)[-1]
-    df = pd.read_csv(file.name) if ext == ".csv" else pd.read_excel(file.name)
-    df = clean_data(df)
-    df_global = df
-    return df.head()
+    try:
+        if not file_path:
+            raise ValueError("No file uploaded.")
+        ext = os.path.splitext(file_path)[-1].lower()
+        if ext == ".csv":
+            df = pd.read_csv(file_path)
+        elif ext in {".xls", ".xlsx"}:
+            df = pd.read_excel(file_path)
+        else:
+            raise ValueError(f"Unsupported extension: {ext}")
+
+        df = clean_data(df)
+        df_global = df
+        return df.head()
+
+    except Exception as e:
+        # Return a 1×1 DataFrame so Gradio won’t crash
+        return pd.DataFrame({"Error": [str(e)]})
+
 
 def format_analysis_report(raw_output, visuals):
     try:
@@ -391,37 +407,40 @@ def explainability(_):
 with gr.Blocks() as demo:
     gr.Markdown("## 📊 AI-Powered Data Analysis with Hyperparameter Optimization")
 
+    # ─────────────────────── Upload & Preview ───────────────────────
     with gr.Row():
         with gr.Column():
             file_input = gr.File(label="Upload CSV or Excel", type="filepath")
-            df_output = gr.DataFrame(label="Cleaned Data Preview")
-            file_input.change(fn=upload_file, inputs=file_input, outputs=df_output)
+            df_output  = gr.DataFrame(label="Cleaned Data Preview")
+            file_input.change(fn=upload_file, inputs=[file_input], outputs=[dff := df_output])
 
         with gr.Column():
-            insights_output = gr.HTML(label="Insights from SmolAgent")
-            visual_output = gr.Gallery(label="Visualizations (Auto-generated by Agent)", columns=2)
-            agent_btn = gr.Button("Run AI Agent (5 Insights + 5 Visualizations)")
+            insights_output = gr.HTML(label="Insights")
+            visual_output   = gr.Gallery(columns=2, label="Visuals")
+            agent_btn       = gr.Button("Run AI Agent")
 
+    # ─────────────────── Hyperopt + Trials ───────────────────
     with gr.Row():
-        train_btn = gr.Button("Train Model with Optuna + WandB")
-        metrics_output = gr.JSON(label="Performance Metrics")
-        trials_output = gr.DataFrame(label="Top 7 Hyperparameter Trials")
+        train_btn      = gr.Button("Train Model")
+        metrics_output = gr.JSON(label="Metrics")
+        trials_output  = gr.DataFrame(label="Top Trials")
 
+    # ─────────────────── Explainability ───────────────────
     with gr.Row():
         explain_btn = gr.Button("SHAP + LIME Explainability")
-        shap_img = gr.Image(label="SHAP Summary Plot")
-        lime_img = gr.Image(label="LIME Explanation")
-    
+        shap_img    = gr.Image(label="SHAP Summary")
+        lime_img    = gr.Image(label="LIME Explanation")
+
+    # ─────────────────── A/B Testing ───────────────────
     with gr.Row():
         ab_test_button = gr.Button("Run A/B Testing")
-        ab_summary = gr.HTML()
-        ab_results = gr.DataFrame()
-
-        
+        ab_summary     = gr.HTML(label="A/B Test Summary")
+        ab_results     = gr.DataFrame(label="A/B Test Results")
 
-    agent_btn.click(fn=analyze_data, inputs=[file_input], outputs=[insights_output, visual_output])
-    train_btn.click(fn=train_model, inputs=[file_input], outputs=[metrics_output, trials_output])
-    explain_btn.click(fn=explainability, inputs=[file_input], outputs=[shap_img, lime_img])
+    # ─────────────────── Hook callbacks ───────────────────
+    agent_btn.click(fn=analyze_data,  inputs=[file_input], outputs=[insights_output, visual_output])
+    train_btn.click(fn=train_model,   inputs=[file_input], outputs=[metrics_output, trials_output])
+    explain_btn.click(fn=explainability, inputs=[], outputs=[shap_img, lime_img])
     ab_test_button.click(fn=ab_test_models, inputs=[], outputs=[ab_summary, ab_results])
 
-demo.launch(debug=True)
+    demo.launch(debug=True)