Fix: Correct indentation - all components inside Blocks context

app.py

@@ -667,6 +667,7 @@ def create_interface(initial_bot: RAGBot, use_inference_api: bool = False) -> gr
         initial_model_short = list(MODEL_MAP.keys())[0]
     
     # Create the Gradio interface with error handling
+    # CRITICAL: All components and event handlers must be INSIDE the with gr.Blocks() context
     try:
         with gr.Blocks(title="CGT-LLM-Beta RAG Chatbot") as demo:
             gr.Markdown("""
@@ -676,176 +677,168 @@ def create_interface(initial_bot: RAGBot, use_inference_api: bool = False) -> gr
             
             The chatbot uses a Retrieval-Augmented Generation (RAG) system to provide evidence-based answers from medical literature.
             """)
-    except Exception as blocks_error:
-        logger.error(f"Error creating Gradio Blocks: {blocks_error}", exc_info=True)
-        # Create a minimal fallback
-        with gr.Blocks(title="CGT-LLM-Beta RAG Chatbot") as demo:
-            gr.Markdown(f"# Error\n\nFailed to create interface: {blocks_error}")
-        return demo
-    
-    try:
-        
-        with gr.Row():
-            with gr.Column(scale=2):
-                question_input = gr.Textbox(
-                    label="Your Question",
-                    placeholder="e.g., What is Lynch Syndrome? What screening is recommended for BRCA1 carriers?",
-                    lines=3
-                )
-                
-                with gr.Row():
-                    model_dropdown = gr.Dropdown(
-                        choices=list(MODEL_MAP.keys()),
-                        value=initial_model_short,
-                        label="Select Model",
-                        info="Choose which LLM model to use for generating answers"
+            
+            with gr.Row():
+                with gr.Column(scale=2):
+                    question_input = gr.Textbox(
+                        label="Your Question",
+                        placeholder="e.g., What is Lynch Syndrome? What screening is recommended for BRCA1 carriers?",
+                        lines=3
                     )
                     
-                    education_dropdown = gr.Dropdown(
-                        choices=list(EDUCATION_LEVELS.keys()),
-                        value=list(EDUCATION_LEVELS.keys())[0],
-                        label="Education Level",
-                        info="Select your education level for personalized answers"
-                    )
-                
-                with gr.Accordion("Advanced Settings", open=False):
-                    k_slider = gr.Slider(
-                        minimum=1,
-                        maximum=10,
-                        value=5,
-                        step=1,
-                        label="Number of document chunks to retrieve (k)"
-                    )
-                    temperature_slider = gr.Slider(
-                        minimum=0.1,
-                        maximum=1.0,
-                        value=0.2,
-                        step=0.1,
-                        label="Temperature (lower = more focused)"
-                    )
-                    max_tokens_slider = gr.Slider(
-                        minimum=128,
-                        maximum=1024,
-                        value=512,
-                        step=128,
-                        label="Max Tokens (lower = faster responses)"
-                    )
-                
-                submit_btn = gr.Button("Ask Question", variant="primary", size="lg")
-                
-            with gr.Column(scale=3):
-                answer_output = gr.Textbox(
-                    label="Answer",
-                    lines=20,
-                    interactive=False,
-                    elem_classes=["answer-box"]
-                )
+                    with gr.Row():
+                        model_dropdown = gr.Dropdown(
+                            choices=list(MODEL_MAP.keys()),
+                            value=initial_model_short,
+                            label="Select Model",
+                            info="Choose which LLM model to use for generating answers"
+                        )
+                        
+                        education_dropdown = gr.Dropdown(
+                            choices=list(EDUCATION_LEVELS.keys()),
+                            value=list(EDUCATION_LEVELS.keys())[0],
+                            label="Education Level",
+                            info="Select your education level for personalized answers"
+                        )
+                    
+                    with gr.Accordion("Advanced Settings", open=False):
+                        k_slider = gr.Slider(
+                            minimum=1,
+                            maximum=10,
+                            value=5,
+                            step=1,
+                            label="Number of document chunks to retrieve (k)"
+                        )
+                        temperature_slider = gr.Slider(
+                            minimum=0.1,
+                            maximum=1.0,
+                            value=0.2,
+                            step=0.1,
+                            label="Temperature (lower = more focused)"
+                        )
+                        max_tokens_slider = gr.Slider(
+                            minimum=128,
+                            maximum=1024,
+                            value=512,
+                            step=128,
+                            label="Max Tokens (lower = faster responses)"
+                        )
+                    
+                    submit_btn = gr.Button("Ask Question", variant="primary", size="lg")
                 
-                with gr.Row():
-                    flesch_output = gr.Textbox(
-                        label="Flesch-Kincaid Grade Level",
-                        value="N/A",
+                with gr.Column(scale=3):
+                    answer_output = gr.Textbox(
+                        label="Answer",
+                        lines=20,
                         interactive=False,
-                        scale=1
+                        elem_classes=["answer-box"]
                     )
                     
-                    similarity_output = gr.Textbox(
-                        label="Similarity Scores",
-                        value="",
-                        interactive=False,
-                        scale=1
-                    )
+                    with gr.Row():
+                        flesch_output = gr.Textbox(
+                            label="Flesch-Kincaid Grade Level",
+                            value="N/A",
+                            interactive=False,
+                            scale=1
+                        )
+                        
+                        similarity_output = gr.Textbox(
+                            label="Similarity Scores",
+                            value="",
+                            interactive=False,
+                            scale=1
+                        )
+                        
+                        category_output = gr.Textbox(
+                            label="Question Category",
+                            value="",
+                            interactive=False,
+                            scale=1
+                        )
                     
-                    category_output = gr.Textbox(
-                        label="Question Category",
-                        value="",
+                    sources_output = gr.Textbox(
+                        label="Source Documents (with Chunk Text)",
+                        lines=15,
                         interactive=False,
-                        scale=1
+                        info="Shows the retrieved document chunks with full text. File paths are shown for easy access."
                     )
-                
-                sources_output = gr.Textbox(
-                    label="Source Documents (with Chunk Text)",
-                    lines=15,
-                    interactive=False,
-                    info="Shows the retrieved document chunks with full text. File paths are shown for easy access."
-                )
-        
-        # Example questions - all questions from the results CSV (scrollable)
-        gr.Markdown("### 💡 Example Questions")
-        gr.Markdown(f"Select a question below to use it in the chatbot ({len(EXAMPLE_QUESTIONS)} questions - scrollable dropdown):")
-        
-        # Use Dropdown which is naturally scrollable with many options
-        example_questions_dropdown = gr.Dropdown(
-            choices=EXAMPLE_QUESTIONS,
-            label="Example Questions",
-            value=None,
-            info="Open the dropdown and scroll through all questions. Select one to use it.",
-            interactive=True,
-            container=True,
-            scale=1
-        )
-        
-        # Update question input when dropdown selection changes
-        def update_question_from_dropdown(selected_question):
-            return selected_question if selected_question else ""
-        
-        example_questions_dropdown.change(
-            fn=update_question_from_dropdown,
-            inputs=example_questions_dropdown,
-            outputs=question_input
-        )
-        
-        # Footer
-        gr.Markdown("""
-        ---
-        **Note:** This chatbot provides informational answers based on medical literature. 
-        It is not a substitute for professional medical advice, diagnosis, or treatment. 
-        Always consult with qualified healthcare providers for medical decisions.
-        """)
-        
-        # Connect the submit button
-        def process_with_education_level(question, model, education, k, temp, max_tok):
-            education_key = EDUCATION_LEVELS[education]
-            return interface.process_question(question, model, education_key, k, temp, max_tok)
-        
-        submit_btn.click(
-            fn=process_with_education_level,
-            inputs=[
-                question_input,
-                model_dropdown,
-                education_dropdown,
-                k_slider,
-                temperature_slider,
-                max_tokens_slider
-            ],
-            outputs=[
-                answer_output,
-                flesch_output,
-                sources_output,
-                similarity_output,
-                category_output
-            ]
-        )
-        
-        # Also allow Enter key to submit
-        question_input.submit(
-            fn=process_with_education_level,
-            inputs=[
-                question_input,
-                model_dropdown,
-                education_dropdown,
-                k_slider,
-                temperature_slider,
-                max_tokens_slider
-            ],
-            outputs=[
-                answer_output,
-                flesch_output,
-                sources_output,
-                similarity_output,
-                category_output
-            ]
-        )
+            
+            # Example questions - all questions from the results CSV (scrollable)
+            gr.Markdown("### 💡 Example Questions")
+            gr.Markdown(f"Select a question below to use it in the chatbot ({len(EXAMPLE_QUESTIONS)} questions - scrollable dropdown):")
+            
+            # Use Dropdown which is naturally scrollable with many options
+            example_questions_dropdown = gr.Dropdown(
+                choices=EXAMPLE_QUESTIONS,
+                label="Example Questions",
+                value=None,
+                info="Open the dropdown and scroll through all questions. Select one to use it.",
+                interactive=True,
+                container=True,
+                scale=1
+            )
+            
+            # Update question input when dropdown selection changes
+            def update_question_from_dropdown(selected_question):
+                return selected_question if selected_question else ""
+            
+            example_questions_dropdown.change(
+                fn=update_question_from_dropdown,
+                inputs=example_questions_dropdown,
+                outputs=question_input
+            )
+            
+            # Footer
+            gr.Markdown("""
+            ---
+            **Note:** This chatbot provides informational answers based on medical literature. 
+            It is not a substitute for professional medical advice, diagnosis, or treatment. 
+            Always consult with qualified healthcare providers for medical decisions.
+            """)
+            
+            # Connect the submit button
+            def process_with_education_level(question, model, education, k, temp, max_tok):
+                education_key = EDUCATION_LEVELS[education]
+                return interface.process_question(question, model, education_key, k, temp, max_tok)
+            
+            submit_btn.click(
+                fn=process_with_education_level,
+                inputs=[
+                    question_input,
+                    model_dropdown,
+                    education_dropdown,
+                    k_slider,
+                    temperature_slider,
+                    max_tokens_slider
+                ],
+                outputs=[
+                    answer_output,
+                    flesch_output,
+                    sources_output,
+                    similarity_output,
+                    category_output
+                ]
+            )
+            
+            # Also allow Enter key to submit
+            question_input.submit(
+                fn=process_with_education_level,
+                inputs=[
+                    question_input,
+                    model_dropdown,
+                    education_dropdown,
+                    k_slider,
+                    temperature_slider,
+                    max_tokens_slider
+                ],
+                outputs=[
+                    answer_output,
+                    flesch_output,
+                    sources_output,
+                    similarity_output,
+                    category_output
+                ]
+            )
     except Exception as interface_error:
         logger.error(f"Error setting up Gradio interface components: {interface_error}", exc_info=True)
         import traceback