Update src/streamlit_app.py

src/streamlit_app.py

@@ -11,6 +11,7 @@ from database import insert_analysis_result
 from dotenv import load_dotenv
 
 load_dotenv()
+
 # Backend API Key Configuration
 GEMINI_API_KEY = os.getenv("GEMINI_KEY")
 
@@ -22,30 +23,49 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
+# Enhanced logging configuration
 logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(message)s",
+    level=logging.DEBUG,  # Changed to DEBUG for more detailed logs
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
     handlers=[
-        logging.StreamHandler()
+        logging.StreamHandler(),
+        logging.FileHandler('app.log', mode='a')  # Also log to file
     ]
 )
 logger = logging.getLogger(__name__)
 
-
 def configure_gemini():
     """Configure Gemini API with backend key"""
+    logger.info("Starting Gemini API configuration...")
+    
     if not GEMINI_API_KEY:
-        st.error("GEMINI_KEY not found in environment variables")
+        error_msg = "GEMINI_KEY not found in environment variables"
+        logger.error(error_msg)
+        st.error(error_msg)
         return False
+    
+    logger.info(f"API Key found, length: {len(GEMINI_API_KEY)}")
+    logger.debug(f"API Key starts with: {GEMINI_API_KEY[:10]}..." if len(GEMINI_API_KEY) > 10 else "API Key too short")
+    
     try:
         genai.configure(api_key=GEMINI_API_KEY)
+        logger.info("Gemini API configured successfully")
+        
+        # Test API connection
+        logger.info("Testing API connection...")
+        models = list(genai.list_models())
+        logger.info(f"Available models: {[model.name for model in models]}")
+        
         return True
     except Exception as e:
-        st.error(f"Failed to configure Gemini API: {str(e)}")
+        error_msg = f"Failed to configure Gemini API: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        st.error(error_msg)
         return False
 
 # Enhanced system prompt with timestamp-based improvements
 SYSTEM_PROMPT = f"""{os.getenv("SYS_PROMPT")}"""
+logger.info(f"System prompt loaded, length: {len(SYSTEM_PROMPT) if SYSTEM_PROMPT else 0}")
 
 def analyze_video_and_generate_script(
         video_bytes,
@@ -58,14 +78,23 @@ def analyze_video_and_generate_script(
     """
     Analyze video and generate direct response script variations
     """
+    logger.info(f"Starting video analysis for: {video_name}")
+    logger.info(f"Video size: {len(video_bytes)} bytes")
+    
     try:
         # Save uploaded video to temporary file
+        logger.info("Creating temporary file...")
         with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(video_name)[1]) as tmp_file:
             tmp_file.write(video_bytes)
             tmp_file_path = tmp_file.name
         
+        logger.info(f"Temporary file created: {tmp_file_path}")
+        logger.info(f"File size on disk: {os.path.getsize(tmp_file_path)} bytes")
+        
         # Configure Gemini
+        logger.info("Configuring Gemini API...")
         if not configure_gemini():
+            logger.error("Gemini configuration failed")
             return None
         
         # Show upload progress
@@ -74,23 +103,64 @@ def analyze_video_and_generate_script(
         
         upload_status.text("Uploading video to Google AI...")
         upload_progress.progress(20)
+        logger.info("Starting file upload to Gemini...")
         
-        # Upload video to Gemini
-        video_file_obj = genai.upload_file(tmp_file_path)
-        upload_progress.progress(40)
+        try:
+            # Upload video to Gemini
+            video_file_obj = genai.upload_file(tmp_file_path)
+            logger.info(f"File uploaded successfully. File URI: {video_file_obj.uri}")
+            logger.info(f"File state: {video_file_obj.state.name}")
+            upload_progress.progress(40)
+            
+        except Exception as upload_error:
+            error_msg = f"File upload failed: {str(upload_error)}"
+            logger.error(error_msg, exc_info=True)
+            upload_status.error(error_msg)
+            return None
         
         upload_status.text("Processing video...")
+        logger.info("Waiting for video processing...")
+        
+        processing_attempts = 0
+        max_processing_attempts = 30  # 1 minute timeout
+        
         while video_file_obj.state.name == "PROCESSING":
+            processing_attempts += 1
+            logger.debug(f"Processing attempt {processing_attempts}/{max_processing_attempts}")
+            
+            if processing_attempts > max_processing_attempts:
+                error_msg = "Video processing timed out after 1 minute"
+                logger.error(error_msg)
+                upload_status.error(error_msg)
+                return None
+            
             time.sleep(2)
-            video_file_obj = genai.get_file(video_file_obj.name)
-            upload_progress.progress(60)
+            try:
+                video_file_obj = genai.get_file(video_file_obj.name)
+                logger.debug(f"Processing state: {video_file_obj.state.name}")
+            except Exception as get_file_error:
+                logger.error(f"Error checking file status: {str(get_file_error)}", exc_info=True)
+                break
+                
+            upload_progress.progress(40 + (processing_attempts * 20 // max_processing_attempts))
+        
+        logger.info(f"Final file state: {video_file_obj.state.name}")
         
         if video_file_obj.state.name == "FAILED":
-            upload_status.error("Google AI file processing failed. Please try another video.")
+            error_msg = "Google AI file processing failed. Please try another video."
+            logger.error(error_msg)
+            upload_status.error(error_msg)
+            return None
+        
+        if video_file_obj.state.name != "ACTIVE":
+            error_msg = f"Unexpected file state: {video_file_obj.state.name}"
+            logger.error(error_msg)
+            upload_status.error(error_msg)
             return None
         
         upload_progress.progress(80)
         upload_status.text("Generating script variations...")
+        logger.info("Starting content generation...")
             
         # Build the enhanced user prompt
         user_prompt = f"""Analyze this reference video and generate 3 high-converting direct response video script variations with detailed timestamp-based improvements.
@@ -129,147 +199,241 @@ def analyze_video_and_generate_script(
 
         IMPORTANT: Return only valid JSON in the exact format specified in the system prompt. Analyze the video second-by-second for maximum detail."""
 
+        logger.info(f"User prompt length: {len(user_prompt)}")
+        logger.info(f"System prompt length: {len(SYSTEM_PROMPT) if SYSTEM_PROMPT else 0}")
+
         # Generate response
         try:
-            model = genai.GenerativeModel("gemini-2.0-flash")
-            response = model.generate_content([video_file_obj, user_prompt + "\n\n" + SYSTEM_PROMPT])
-        except Exception as e:
-            st.error(f"Error generating content with Gemini: {str(e)}")
+            logger.info("Creating GenerativeModel instance...")
+            model = genai.GenerativeModel("gemini-2.0-flash-exp")
+            logger.info("Model created successfully")
+            
+            logger.info("Generating content with video and prompts...")
+            full_prompt = user_prompt + "\n\n" + (SYSTEM_PROMPT or "")
+            logger.debug(f"Full prompt length: {len(full_prompt)}")
+            
+            response = model.generate_content([video_file_obj, full_prompt])
+            logger.info("Content generation completed successfully")
+            logger.debug(f"Response text length: {len(response.text) if hasattr(response, 'text') else 'No text attribute'}")
+            
+        except Exception as generation_error:
+            error_msg = f"Error generating content with Gemini: {str(generation_error)}"
+            logger.error(error_msg, exc_info=True)
+            upload_status.error(error_msg)
             return None
         
         upload_progress.progress(100)
         upload_status.success("Analysis complete!")
+        logger.info("Video analysis completed successfully")
         
         # Clean up temporary file
-        os.unlink(tmp_file_path)
+        try:
+            os.unlink(tmp_file_path)
+            logger.info(f"Temporary file deleted: {tmp_file_path}")
+        except Exception as cleanup_error:
+            logger.warning(f"Failed to delete temporary file: {str(cleanup_error)}")
         
         # Parse JSON response
+        logger.info("Parsing JSON response...")
         try:
+            if not hasattr(response, 'text'):
+                error_msg = "Response object has no text attribute"
+                logger.error(error_msg)
+                st.error(error_msg)
+                return None
+                
             response_text = response.text.strip()
+            logger.debug(f"Raw response text preview: {response_text[:500]}...")
+            
             if response_text.startswith('```json'):
                 response_text = response_text[7:-3]
+                logger.debug("Removed json code block markers")
             elif response_text.startswith('```'):
                 response_text = response_text[3:-3]
+                logger.debug("Removed generic code block markers")
+            
+            logger.debug(f"Cleaned response text preview: {response_text[:500]}...")
             
             json_response = json.loads(response_text)
+            logger.info("JSON parsing successful")
+            logger.debug(f"JSON keys: {list(json_response.keys()) if isinstance(json_response, dict) else 'Not a dict'}")
+            
             return json_response
             
-        except json.JSONDecodeError as e:
-            st.error(f"Error parsing AI response: {str(e)}")
+        except json.JSONDecodeError as json_error:
+            error_msg = f"Error parsing AI response as JSON: {str(json_error)}"
+            logger.error(error_msg)
+            logger.error(f"Response text that failed to parse: {response_text[:1000]}...")
+            st.error(error_msg)
+            st.text_area("Raw Response (for debugging):", response_text, height=200)
             return None
         
     except Exception as e:
-        st.error(f"Error processing video: {str(e)}")
+        error_msg = f"Unexpected error processing video: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        st.error(error_msg)
         return None
 
 def display_script_variations(json_data):
     """Display script variations in formatted tables"""
+    logger.info("Displaying script variations...")
+    
     if not json_data or "script_variations" not in json_data:
-        st.error("No script variations found in the response")
+        error_msg = "No script variations found in the response"
+        logger.error(error_msg)
+        logger.debug(f"JSON data keys: {list(json_data.keys()) if isinstance(json_data, dict) else 'Not a dict'}")
+        st.error(error_msg)
         return
     
-    for i, variation in enumerate(json_data["script_variations"], 1):
-        variation_name = variation.get("variation_name", f"Variation {i}")
-
-        st.markdown(f"### Variation {i}: {variation_name}")
-
-        #Convert script table to DataFrame for better display
-        script_data = variation.get("script_table")
-        if not script_data:
-            st.warning(f"No script data for {variation_name}")
-            continue
-
-        df = pd.DataFrame(script_data)
+    try:
+        variations = json_data["script_variations"]
+        logger.info(f"Found {len(variations)} script variations")
+        
+        for i, variation in enumerate(variations, 1):
+            variation_name = variation.get("variation_name", f"Variation {i}")
+            logger.debug(f"Processing variation {i}: {variation_name}")
 
-        # Rename columns for better display
-        df = df.rename(columns={
-            'timestamp': 'Timestamp',
-            'script_voiceover': 'Script / Voiceover',
-            'visual_direction': 'Visual Direction',
-            'psychological_trigger': 'Psychological Trigger',
-            'cta_action': 'CTA / Action'
-        })
+            st.markdown(f"### Variation {i}: {variation_name}")
 
-        st.table(df)
-        st.markdown("---")
+            #Convert script table to DataFrame for better display
+            script_data = variation.get("script_table")
+            if not script_data:
+                warning_msg = f"No script data for {variation_name}"
+                logger.warning(warning_msg)
+                st.warning(warning_msg)
+                continue
 
+            logger.debug(f"Script data for {variation_name}: {len(script_data)} rows")
+            
+            df = pd.DataFrame(script_data)
+
+            # Rename columns for better display
+            df = df.rename(columns={
+                'timestamp': 'Timestamp',
+                'script_voiceover': 'Script / Voiceover',
+                'visual_direction': 'Visual Direction',
+                'psychological_trigger': 'Psychological Trigger',
+                'cta_action': 'CTA / Action'
+            })
+
+            st.table(df)
+            st.markdown("---")
+            
+        logger.info("Script variations displayed successfully")
+        
+    except Exception as e:
+        error_msg = f"Error displaying script variations: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        st.error(error_msg)
 
 def display_video_analysis(json_data):
     """Display video analysis in tabular format"""
+    logger.info("Displaying video analysis...")
+    
     if not json_data or "video_analysis" not in json_data:
-        st.error("No video analysis found in the response")
+        error_msg = "No video analysis found in the response"
+        logger.error(error_msg)
+        st.error(error_msg)
         return
     
-    analysis = json_data["video_analysis"]
-
-    #Display general analysis
-    video_metrics = []
-    if isinstance(analysis, dict):
-        col1, col2 = st.columns(2)
-
-        with col1:
-            st.subheader("Effectiveness Factors")
-            st.write(analysis.get('effectiveness_factors', 'N/A'))
-
-            st.subheader("Target Audience")
-            st.write(analysis.get('target_audience', 'N/A'))
-
-        with col2:
-            st.subheader("Psychological Triggers")
-            st.write(analysis.get('psychological_triggers', 'N/A'))
-
-        video_metrics = analysis.get("video_metrics", [])
-
-    else:
-        st.warning("Unexpected format in video_analysis. Skipping metadata.")
-        if isinstance(analysis, list):
-            video_metrics = analysis
-
-    if video_metrics:
-        metrics_df = pd.DataFrame(video_metrics)
-        
-        # Rename columns for better display
-        column_mapping = {
-            'timestamp': 'Timestamp',
-            'element': 'Element',
-            'current_approach': 'Current Approach',
-            'effectiveness_score': 'Score',
-            'notes': 'Analysis Notes'
-        }
-        
-        metrics_df = metrics_df.rename(columns=column_mapping)
-        
-        st.dataframe(
-            metrics_df,
-            use_container_width=True,
-            hide_index=True,
-            column_config={
-                "Timestamp": st.column_config.TextColumn(width="small"),
-                "Element": st.column_config.TextColumn(width="medium"),
-                "Current Approach": st.column_config.TextColumn(width="large"),
-                "Score": st.column_config.TextColumn(width="small"),
-                "Analysis Notes": st.column_config.TextColumn(width="large")
+    try:
+        analysis = json_data["video_analysis"]
+        logger.debug(f"Video analysis type: {type(analysis)}")
+
+        #Display general analysis
+        video_metrics = []
+        if isinstance(analysis, dict):
+            col1, col2 = st.columns(2)
+
+            with col1:
+                st.subheader("Effectiveness Factors")
+                effectiveness = analysis.get('effectiveness_factors', 'N/A')
+                st.write(effectiveness)
+                logger.debug(f"Effectiveness factors: {effectiveness}")
+
+                st.subheader("Target Audience")
+                audience = analysis.get('target_audience', 'N/A')
+                st.write(audience)
+                logger.debug(f"Target audience: {audience}")
+
+            with col2:
+                st.subheader("Psychological Triggers")
+                triggers = analysis.get('psychological_triggers', 'N/A')
+                st.write(triggers)
+                logger.debug(f"Psychological triggers: {triggers}")
+
+            video_metrics = analysis.get("video_metrics", [])
+            logger.debug(f"Video metrics count: {len(video_metrics)}")
+
+        else:
+            warning_msg = "Unexpected format in video_analysis. Skipping metadata."
+            logger.warning(warning_msg)
+            st.warning(warning_msg)
+            if isinstance(analysis, list):
+                video_metrics = analysis
+
+        if video_metrics:
+            logger.info(f"Processing {len(video_metrics)} video metrics")
+            metrics_df = pd.DataFrame(video_metrics)
+            
+            # Rename columns for better display
+            column_mapping = {
+                'timestamp': 'Timestamp',
+                'element': 'Element',
+                'current_approach': 'Current Approach',
+                'effectiveness_score': 'Score',
+                'notes': 'Analysis Notes'
             }
-        )
-    else:
-        st.warning("No detailed video metrics available")
+            
+            metrics_df = metrics_df.rename(columns=column_mapping)
+            logger.debug(f"Metrics dataframe columns: {list(metrics_df.columns)}")
+            
+            st.dataframe(
+                metrics_df,
+                use_container_width=True,
+                hide_index=True,
+                column_config={
+                    "Timestamp": st.column_config.TextColumn(width="small"),
+                    "Element": st.column_config.TextColumn(width="medium"),
+                    "Current Approach": st.column_config.TextColumn(width="large"),
+                    "Score": st.column_config.TextColumn(width="small"),
+                    "Analysis Notes": st.column_config.TextColumn(width="large")
+                }
+            )
+        else:
+            warning_msg = "No detailed video metrics available"
+            logger.warning(warning_msg)
+            st.warning(warning_msg)
+            
+        logger.info("Video analysis displayed successfully")
+        
+    except Exception as e:
+        error_msg = f"Error displaying video analysis: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        st.error(error_msg)
 
 def display_timestamp_improvements(json_data):
     """Display timestamp-based improvements in tabular format"""
+    logger.info("Displaying timestamp improvements...")
+    
     improvements = json_data.get("timestamp_improvements")
 
     if improvements is None:
-        st.error("No timestamp improvements found in the response")
+        error_msg = "No timestamp improvements found in the response"
+        logger.error(error_msg)
+        st.error(error_msg)
         return
 
     if not improvements:
-        st.warning("No timestamp improvements available")
+        warning_msg = "No timestamp improvements available"
+        logger.warning(warning_msg)
+        st.warning(warning_msg)
         return
 
-    st.subheader("Timestamp-by-Timestamp Improvement Recommendations")
-    
-    improvements = json_data["timestamp_improvements"]
-    if improvements:
+    try:
+        st.subheader("Timestamp-by-Timestamp Improvement Recommendations")
+        logger.info(f"Processing {len(improvements)} improvement recommendations")
+        
         improvements_df = pd.DataFrame(improvements)
         
         # Rename columns for better display
@@ -283,6 +447,7 @@ def display_timestamp_improvements(json_data):
         }
         
         improvements_df = improvements_df.rename(columns=column_mapping)
+        logger.debug(f"Improvements dataframe columns: {list(improvements_df.columns)}")
         
         # Color code priority
         def color_priority(val):
@@ -303,220 +468,4 @@ def display_timestamp_improvements(json_data):
             column_config={
                 "Timestamp": st.column_config.TextColumn(width="small"),
                 "Current Element": st.column_config.TextColumn(width="medium"),
-                "Improvement Type": st.column_config.TextColumn(width="medium"),
-                "Recommended Change": st.column_config.TextColumn(width="large"),
-                "Expected Impact": st.column_config.TextColumn(width="medium"),
-                "Priority": st.column_config.TextColumn(width="small")
-            }
-        )
-    else:
-        st.warning("No timestamp improvements available")
-
-def create_csv_download(json_data):
-    """Create CSV content with all scripts combined"""
-    all_scripts_data = []
-    
-    # Combine all script variations into one dataset
-    for i, variation in enumerate(json_data.get("script_variations", []), 1):
-        variation_name = variation.get("variation_name", f"Variation {i}")
-        
-        for row in variation.get("script_table", []):
-            script_row = {
-                'Variation': variation_name,
-                'Timestamp': row.get('timestamp', ''),
-                'Script_Voiceover': row.get('script_voiceover', ''),
-                'Visual_Direction': row.get('visual_direction', ''),
-                'Psychological_Trigger': row.get('psychological_trigger', ''),
-                'CTA_Action': row.get('cta_action', '')
-            }
-            all_scripts_data.append(script_row)
-    
-    # Convert to DataFrame and then to CSV
-    if all_scripts_data:
-        df = pd.DataFrame(all_scripts_data)
-        return df.to_csv(index=False)
-    else:
-        return "No script data available"
-
-def check_token(user_token):
-    ACCESS_TOKEN = os.getenv("ACCESS_TOKEN")
-    if not ACCESS_TOKEN:
-        logger.critical("ACCESS_TOKEN not set in environment.")
-        return False, "Server error: Access token not configured."
-    if user_token == ACCESS_TOKEN:
-        logger.info("Access token validated successfully.")
-        return True, ""
-    logger.warning("Invalid access token attempt.")
-    return False, "Invalid token."
-
-def main():
-    """Main application function"""
-
-    if "authenticated" not in st.session_state:
-        st.session_state["authenticated"] = False
-
-    if not st.session_state["authenticated"]:
-        st.markdown("## Access Required")
-        token_input = st.text_input("Enter Access Token", type="password")
-        if st.button("Unlock App"):
-            ok, error_msg = check_token(token_input)
-            if ok:
-                st.session_state["authenticated"] = True
-                st.rerun()
-            else:
-                st.error(error_msg)
-        return
-
-
-    # Sidebar navigation
-    if st.session_state["authenticated"]:
-
-        selected_tab = st.sidebar.radio("Select Mode", ["Script Generator", "History"])
-
-        # ========== SCRIPT GENERATOR ==========
-        if selected_tab == "Script Generator":
-            with st.expander("How to Use This Tool", expanded=False):
-                st.markdown("""
-                ### Upload Guidelines:
-                - **Best videos to analyze**: Already profitable Facebook/TikTok ads in your niche  
-                - **Video length**: 30–90 seconds work best for analysis  
-                - **Quality**: Clear audio and visuals help with better analysis  
-
-                ### Context Tips:
-                - **Offer details**: Be specific about your main promise and mechanism  
-                - **Audience**: Include demographics, pain points, and desires  
-                - **Hooks**: Mention any specific angles that have worked for you  
-
-                ### Script Optimization:
-                - Generated scripts focus on stopping scroll and driving clicks  
-                - Each variation tests different psychological triggers  
-                - Use the timestamp format for precise video production  
-                - Test multiple variations to find your best performer  
-                """)
-            st.subheader("Input Configuration")
-
-            uploaded_video = st.file_uploader(
-                "Upload Reference Video",
-                type=['mp4', 'mov', 'avi', 'mkv'],
-                help="Upload a profitable ad video to analyze and create variations from"
-            )
-            if uploaded_video is None:
-                st.info("Please upload a reference video to begin analysis.")
-
-            st.subheader("Additional Context (Optional)")
-            
-            offer_details = st.text_area(
-                "Offer Details",
-                placeholder="e.g., Solar installation with $0 down payment...",
-                height=80,
-                help="Describe the product/service and main promise"
-            )
-            
-            target_audience = st.text_area(
-                "Target Audience",
-                placeholder="e.g., 40+ homeowners with high electricity bills...",
-                height=80,
-                help="Describe the ideal customer demographics and pain points"
-            )
-            
-            specific_hooks = st.text_area(
-                "Specific Hooks to Test",
-                placeholder="e.g., Government rebate angle, celebrity endorsement...",
-                height=80,
-                help="Any specific angles or hooks you want to incorporate"
-            )
-            
-            additional_context = st.text_area(
-                "Additional Context",
-                placeholder="Any other relevant information...",
-                height=100,
-                help="Compliance requirements, brand guidelines, or other notes"
-            )
-
-            generate_button = st.button("Generate Script Variations", use_container_width=True)
-
-            if "analysis_results" in st.session_state and st.session_state["analysis_results"]:
-                if st.button("Clear Results", use_container_width=True):
-                    del st.session_state["analysis_results"]
-                    st.rerun()
-
-            # Generate & show results
-            if uploaded_video and generate_button:
-                with st.spinner("Analyzing video and generating scripts..."):
-                    video_bytes = uploaded_video.read()
-                    uploaded_video.seek(0)
-
-                    json_response = analyze_video_and_generate_script(
-                        video_bytes,
-                        uploaded_video.name,
-                        offer_details,
-                        target_audience,
-                        specific_hooks,
-                        additional_context
-                    )
-
-                if json_response:
-                    insert_analysis_result(
-                        video_name=uploaded_video.name,
-                        offer_details=offer_details,
-                        target_audience=target_audience,
-                        specific_hook=specific_hooks,
-                        additional_context=additional_context,
-                        response=json_response
-                    )
-                    st.session_state["analysis_results"] = json_response
-
-            if "analysis_results" in st.session_state:
-                json_response = st.session_state["analysis_results"]
-
-                tab1, tab2, tab3 = st.tabs(["Script Variations", "Video Analysis", "Improvement Recommendations"])
-                with tab1:
-                    display_script_variations(json_response)
-                    csv_content = create_csv_download(json_response)
-                    st.download_button("Download All Scripts (CSV)", data=csv_content,
-                                       file_name="video_script_variations.csv", mime="text/csv")
-                with tab2:
-                    display_video_analysis(json_response)
-                with tab3:
-                    display_timestamp_improvements(json_response)
-
-        # ========== HISTORY ==========
-        elif selected_tab == "History":
-            from database import get_all_results
-            history_items = get_all_results(limit=20)
-
-            if history_items:
-                video_titles = [
-                    f"{item['video_name']} ({item['created_at'].strftime('%Y-%m-%d %H:%M')})"
-                    for item in history_items
-                ]
-
-                selected = st.sidebar.radio("History Items", video_titles, index=0)
-                selected_index = video_titles.index(selected)
-                selected_data = history_items[selected_index]
-
-                st.subheader(f"Analysis for: {selected_data['video_name']}")
-                json_response = selected_data.get("response")
-
-                if json_response:
-                    tab1, tab2, tab3 = st.tabs(["Script Variations", "Video Analysis", "Improvement Recommendations"])
-
-                    with tab1:
-                        display_script_variations(json_response)
-                    with tab2:
-                        display_video_analysis(json_response)
-                    with tab3:
-                        display_timestamp_improvements(json_response)
-                else:
-                    st.warning("No valid response data for this analysis.")
-            else:
-                st.sidebar.info("No saved analyses found.")
-                st.info("No saved history available.")
-
-
-if __name__ == "__main__":
-    try:
-        logger.info("Launching Streamlit app...")
-        main()
-    except Exception as e:
-        logger.exception("Unhandled error during app launch.")
+                "Improvement Type": st.