Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,5 +1,5 @@
-import streamlit as st
-import google.generativeai as genai
+mport streamlit as st
+from google import genai
 import tempfile
 import os
 import time
@@ -11,7 +11,6 @@ from database import insert_analysis_result
 from dotenv import load_dotenv
 
 load_dotenv()
-
 # Backend API Key Configuration
 GEMINI_API_KEY = os.getenv("GEMINI_KEY")
 
@@ -23,48 +22,22 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# Enhanced logging configuration
 logging.basicConfig(
-    level=logging.DEBUG,  # Changed to DEBUG for more detailed logs
-    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
     handlers=[
         logging.StreamHandler()
     ]
 )
 logger = logging.getLogger(__name__)
 
+
 def configure_gemini():
     """Configure Gemini API with backend key"""
-    logger.info("Starting Gemini API configuration...")
-    
-    if not GEMINI_API_KEY:
-        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:
-        error_msg = f"Failed to configure Gemini API: {str(e)}"
-        logger.error(error_msg, exc_info=True)
-        st.error(error_msg)
-        return False
+    return genai.Client(api_key=GEMINI_API_KEY)
 
 # 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}")
+SYSTEM_PROMPT = f"""{os.getenv("SYS_PROMPT")}"""
 
 def analyze_video_and_generate_script(
         video_bytes,
@@ -77,24 +50,14 @@ 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
+        client = configure_gemini()
         
         # Show upload progress
         upload_progress = st.progress(0)
@@ -102,328 +65,200 @@ 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...")
         
-        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 video to Gemini
+        video_file_obj = client.files.upload(file=tmp_file_path)
+        upload_progress.progress(40)
         
         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)
-            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}")
+            video_file_obj = client.files.get(name=video_file_obj.name)
+            upload_progress.progress(60)
         
         if video_file_obj.state.name == "FAILED":
-            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)
+            upload_status.error("Google AI file processing failed. Please try another video.")
             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.
+
         IMPORTANT CONTEXT TO FOLLOW WHEN CREATING OUTPUT:
         - Offer Details: {offer_details}
         - Target Audience: {target_audience}
         - Specific Hooks: {specific_hooks}
+
         ADDITIONAL CONTEXT (MANDATORY TO FOLLOW):
         {additional_context}
+
         You must reflect this additional context in:
         - The script tone, CTA, visuals
         - Compliance or branding constraints
         - Any assumptions about audience or product
+
         Failure to include this will be considered incomplete.
+
         Please provide a comprehensive analysis including:
+
         1. DETAILED VIDEO ANALYSIS with timestamp-based metrics:
            - Break down the video into 5-10 second segments
            - Rate each segment's effectiveness (1-10 scale)
            - Identify specific elements (hook, transition, proof, CTA, etc.)
+
         2. TIMESTAMP-BASED IMPROVEMENTS:
            - Specific recommendations for each time segment
            - Priority level for each improvement
            - Expected impact of implementing changes
+
         3. SCRIPT VARIATIONS:
            - Create 2-3 complete script variations
            - Each with timestamp-by-timestamp breakdown
            - Different psychological triggers and approaches
-        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}")
+        IMPORTANT: Return only valid JSON in the exact format specified in the system prompt. Analyze the video second-by-second for maximum detail."""
 
         # Generate response
-        try:
-            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
+        response = client.models.generate_content(
+            model="gemini-2.0-flash", 
+            contents=[video_file_obj, user_prompt + "\n\n" + SYSTEM_PROMPT]
+        )
         
         upload_progress.progress(100)
         upload_status.success("Analysis complete!")
-        logger.info("Video analysis completed successfully")
         
         # Clean up temporary file
-        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)}")
+        os.unlink(tmp_file_path)
         
         # 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 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)
+        except json.JSONDecodeError as e:
+            st.error(f"Error parsing AI response: {str(e)}")
             return None
         
     except Exception as e:
-        error_msg = f"Unexpected error processing video: {str(e)}"
-        logger.error(error_msg, exc_info=True)
-        st.error(error_msg)
+        st.error(f"Error processing video: {str(e)}")
         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:
-        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)
+        st.error("No script variations found in the response")
         return
     
-    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}")
+    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}")
+        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:
-                warning_msg = f"No script data for {variation_name}"
-                logger.warning(warning_msg)
-                st.warning(warning_msg)
-                continue
+        #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)
+
+        # 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.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:
-        error_msg = "No video analysis found in the response"
-        logger.error(error_msg)
-        st.error(error_msg)
+        st.error("No video analysis found in the response")
         return
     
-    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'
-            }
-            
-            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")
+    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)
         
-    except Exception as e:
-        error_msg = f"Error displaying video analysis: {str(e)}"
-        logger.error(error_msg, exc_info=True)
-        st.error(error_msg)
+        # 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")
+            }
+        )
+    else:
+        st.warning("No detailed video metrics available")
 
 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:
-        error_msg = "No timestamp improvements found in the response"
-        logger.error(error_msg)
-        st.error(error_msg)
+        st.error("No timestamp improvements found in the response")
         return
 
     if not improvements:
-        warning_msg = "No timestamp improvements available"
-        logger.warning(warning_msg)
-        st.warning(warning_msg)
+        st.warning("No timestamp improvements available")
         return
 
-    try:
-        st.subheader("Timestamp-by-Timestamp Improvement Recommendations")
-        logger.info(f"Processing {len(improvements)} improvement recommendations")
-        
+    st.subheader("Timestamp-by-Timestamp Improvement Recommendations")
+    
+    improvements = json_data["timestamp_improvements"]
+    if improvements:
         improvements_df = pd.DataFrame(improvements)
         
         # Rename columns for better display
@@ -437,7 +272,6 @@ 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):
@@ -464,127 +298,94 @@ def display_timestamp_improvements(json_data):
                 "Priority": st.column_config.TextColumn(width="small")
             }
         )
-        
-        logger.info("Timestamp improvements displayed successfully")
-        
-    except Exception as e:
-        error_msg = f"Error displaying timestamp improvements: {str(e)}"
-        logger.error(error_msg, exc_info=True)
-        st.error(error_msg)
+    else:
+        st.warning("No timestamp improvements available")
 
 def create_csv_download(json_data):
     """Create CSV content with all scripts combined"""
-    logger.info("Creating CSV download...")
+    all_scripts_data = []
     
-    try:
-        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}")
-            logger.debug(f"Processing variation for CSV: {variation_name}")
-            
-            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)
+    # 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}")
         
-        # Convert to DataFrame and then to CSV
-        if all_scripts_data:
-            df = pd.DataFrame(all_scripts_data)
-            csv_content = df.to_csv(index=False)
-            logger.info(f"CSV created successfully with {len(all_scripts_data)} rows")
-            return csv_content
-        else:
-            logger.warning("No script data available for CSV")
-            return "No script data available"
-            
-    except Exception as e:
-        error_msg = f"Error creating CSV: {str(e)}"
-        logger.error(error_msg, exc_info=True)
-        return f"Error creating CSV: {error_msg}"
+        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):
-    logger.info("Checking access token...")
-    
     ACCESS_TOKEN = os.getenv("ACCESS_TOKEN")
     if not ACCESS_TOKEN:
-        error_msg = "ACCESS_TOKEN not set in environment."
-        logger.critical(error_msg)
+        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"""
-    logger.info("Starting main application...")
+
+    st.set_page_config(
+        page_title="Video Analyser and Script Generator",
+        page_icon="🎥",
+        layout="wide",
+        initial_sidebar_state="expanded"
+    )
+
+    st.title("Video Analyser and Script Generator")
+    st.divider()
 
     if "authenticated" not in st.session_state:
         st.session_state["authenticated"] = False
-        logger.debug("Authentication state initialized")
 
     if not st.session_state["authenticated"]:
-        logger.info("User not authenticated, showing login screen")
         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
-                logger.info("User authenticated successfully")
                 st.rerun()
             else:
-                logger.warning(f"Authentication failed: {error_msg}")
                 st.error(error_msg)
         return
 
-    # Add API test button for debugging
-    if st.sidebar.button("🔧 Test API Connection"):
-        logger.info("Testing API connection...")
-        try:
-            genai.configure(api_key=GEMINI_API_KEY)
-            models = list(genai.list_models())
-            st.sidebar.success(f"✅ API Working! Found {len(models)} models")
-            logger.info(f"API test successful, found {len(models)} models")
-            for model in models[:3]:  # Show first 3 models
-                st.sidebar.text(f"• {model.name}")
-        except Exception as e:
-            error_msg = f"❌ API Test Failed: {str(e)}"
-            st.sidebar.error(error_msg)
-            logger.error(f"API test failed: {str(e)}", exc_info=True)
 
     # Sidebar navigation
     if st.session_state["authenticated"]:
-        logger.info("User authenticated, showing main interface")
 
         selected_tab = st.sidebar.radio("Select Mode", ["Script Generator", "History"])
-        logger.debug(f"Selected tab: {selected_tab}")
 
         # ========== SCRIPT GENERATOR ==========
         if selected_tab == "Script Generator":
-            logger.info("Script Generator mode selected")
-            
             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  
@@ -598,10 +399,7 @@ def main():
                 type=['mp4', 'mov', 'avi', 'mkv'],
                 help="Upload a profitable ad video to analyze and create variations from"
             )
-            
-            if uploaded_video is not None:
-                logger.info(f"Video uploaded: {uploaded_video.name}, size: {uploaded_video.size} bytes")
-            else:
+            if uploaded_video is None:
                 st.info("Please upload a reference video to begin analysis.")
 
             st.subheader("Additional Context (Optional)")
@@ -639,13 +437,10 @@ def main():
             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"]
-                    logger.info("Analysis results cleared")
                     st.rerun()
 
             # Generate & show results
             if uploaded_video and generate_button:
-                logger.info("Starting video analysis process...")
-                
                 with st.spinner("Analyzing video and generating scripts..."):
                     video_bytes = uploaded_video.read()
                     uploaded_video.seek(0)
@@ -660,31 +455,20 @@ def main():
                     )
 
                 if json_response:
-                    logger.info("Analysis completed successfully, saving to database...")
-                    try:
-                        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
-                        )
-                        logger.info("Results saved to database")
-                    except Exception as db_error:
-                        logger.error(f"Failed to save to database: {str(db_error)}", exc_info=True)
-                        st.warning("Analysis completed but failed to save to database")
-                    
+                    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
-                else:
-                    logger.error("Analysis failed, no response received")
 
             if "analysis_results" in st.session_state:
-                logger.info("Displaying analysis results...")
                 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)
@@ -697,58 +481,41 @@ def main():
 
         # ========== HISTORY ==========
         elif selected_tab == "History":
-            logger.info("History mode selected")
-            
-            try:
-                from database import get_all_results
-                history_items = get_all_results(limit=20)
-                logger.info(f"Retrieved {len(history_items) if history_items else 0} history items")
-
-                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]
-                    
-                    logger.info(f"Selected history item: {selected_data['video_name']}")
-
-                    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:
-                        warning_msg = "No valid response data for this analysis."
-                        logger.warning(warning_msg)
-                        st.warning(warning_msg)
+            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:
-                    logger.info("No history items found")
-                    st.sidebar.info("No saved analyses found.")
-                    st.info("No saved history available.")
-                    
-            except Exception as history_error:
-                error_msg = f"Error loading history: {str(history_error)}"
-                logger.error(error_msg, exc_info=True)
-                st.error(error_msg)
+                    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("=" * 50)
-        logger.info("LAUNCHING VIDEO ANALYZER APPLICATION")
-        logger.info("=" * 50)
+        logger.info("Launching Streamlit app...")
         main()
     except Exception as e:
-        logger.exception("CRITICAL ERROR: Unhandled error during app launch")
-        st.error(f"Critical application error: {str(e)}")
-        st.error("Please check the logs for more details.")
+	logger.exception("Unhandled error during app launch.")