Update app.py

app.py

@@ -34,6 +34,7 @@ def update_dropdown(file):
             return gr.update(choices=[], value=None)
         return gr.update(choices=list(df.columns), value=None)
     except Exception as e:
+        print(f"Error in update_dropdown: {e}")  # Debug logging
         return gr.update(choices=[], value=None)
 
 def analyze_file(file, label_col, n_clusters):
@@ -48,6 +49,7 @@ def analyze_file(file, label_col, n_clusters):
         else:
             return ("Unsupported file type. Please upload a CSV or XLSX file.", None, None, None, None, None)
     except Exception as e:
+        print(f"Error reading file: {e}")  # Debug logging
         return (f"Error reading file: {e}", None, None, None, None, None)
 
     if df.empty:
@@ -253,6 +255,7 @@ def predict_interactive(**kwargs):
 
 def create_interactive_inputs(file, label_col):
     if file is None or label_col is None:
+        print("No file or label column provided")  # Debug logging
         return []
     
     try:
@@ -261,9 +264,18 @@ def create_interactive_inputs(file, label_col):
         elif file.name.endswith('.xlsx'):
             df = pd.read_excel(file.name)
         else:
+            print("Unsupported file type")  # Debug logging
+            return []
+        
+        if df.empty or label_col not in df.columns:
+            print(f"Empty DataFrame or invalid label column: {label_col}")  # Debug logging
             return []
         
         X = df.drop(columns=[label_col])
+        if X.empty:
+            print("No features available after dropping label column")  # Debug logging
+            return []
+        
         components = []
         for col in X.columns:
             examples = X[col].dropna().sample(min(3, len(X[col].dropna()))).tolist()
@@ -272,8 +284,10 @@ def create_interactive_inputs(file, label_col):
             else:
                 unique_values = X[col].dropna().unique().tolist()
                 components.append(gr.Dropdown(label=f"{col} (e.g., {', '.join(map(str, examples))})", choices=unique_values, value=None))
+        print(f"Generated {len(components)} input components")  # Debug logging
         return components
-    except Exception:
+    except Exception as e:
+        print(f"Error in create_interactive_inputs: {e}")  # Debug logging
         return []
 
 with gr.Blocks() as demo:
@@ -328,13 +342,14 @@ with gr.Blocks() as demo:
             gr.Markdown("Enter values for each feature to get a prediction based on the trained model.")
             with gr.Column():
                 input_components = gr.State(value=[])
-                dynamic_inputs = gr.Column()
+                dynamic_inputs = gr.Column(visible=True)
                 predict_btn = gr.Button("Predict")
                 prediction_output = gr.Textbox(label="Prediction Result")
             
             def update_inputs(file, label_col):
+                print(f"Updating inputs with file: {file}, label_col: {label_col}")  # Debug logging
                 components = create_interactive_inputs(file, label_col)
-                return components, gr.Column.update(components=components)
+                return components, gr.Column.update(components=components, visible=True)
 
             file_input.change(
                 fn=update_inputs,
@@ -355,4 +370,4 @@ with gr.Blocks() as demo:
     analyze_btn.click(fn=analyze_file, inputs=[file_input, label_dropdown, clusters_slider],
                       outputs=[results_textbox, model_img_output, fi_output, kmeans_output, agg_output, diff_output])
 
-demo.launch()
+demo.launch(debug=True)  # Enable debug mode for more detailed error logging