src/streamlit_app.py

import streamlit as st
from google import genai
import tempfile
import os
import time
import json
from typing import Optional
import pandas as pd
import logging

# Backend API Key Configuration
GEMINI_API_KEY = os.getenv("GEMENI_KEY")

# Page configuration
st.set_page_config(
    page_title="Video Analyser and Script Generator",
    page_icon="🎥",
    layout="wide",
    initial_sidebar_state="expanded"
)

logging.basicConfig(
    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"""
    return genai.Client(api_key=GEMINI_API_KEY)

# Enhanced system prompt with timestamp-based improvements
SYSTEM_PROMPT = f"""{os.getenv("SYS_PROMPT")}"""

def analyze_video_and_generate_script(
    video_bytes,
    video_name,
    offer_details: str = "",
    target_audience: str = "",
    specific_hooks: str = "",
    additional_context: str = ""
):
    """
    Analyze video and generate direct response script variations
    """
    try:
        # Save uploaded video to 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
        
        # Configure Gemini
        client = configure_gemini()
        
        # Show upload progress
        upload_progress = st.progress(0)
        upload_status = st.empty()
        
        upload_status.text("Uploading video to Google AI...")
        upload_progress.progress(20)
        
        # Upload video to Gemini
        video_file_obj = client.files.upload(file=tmp_file_path)
        upload_progress.progress(40)
        
        upload_status.text("Processing video...")
        while video_file_obj.state.name == "PROCESSING":
            time.sleep(2)
            video_file_obj = client.files.get(name=video_file_obj.name)
            upload_progress.progress(60)
        
        if video_file_obj.state.name == "FAILED":
            upload_status.error("Google AI file processing failed. Please try another video.")
            return None
        
        upload_progress.progress(80)
        upload_status.text("Generating script variations...")
            
        # 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.

ADDITIONAL CONTEXT:
- Offer Details: {offer_details if offer_details else 'Extract from video'}
- Target Audience: {target_audience if target_audience else 'Determine from video content'}  
- Specific Hooks to Consider: {specific_hooks if specific_hooks else 'Create based on video analysis'}
- Additional Context: {additional_context}

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."""

        # Generate response
        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!")
        
        # Clean up temporary file
        os.unlink(tmp_file_path)
        
        # Parse JSON response
        try:
            response_text = response.text.strip()
            if response_text.startswith('```json'):
                response_text = response_text[7:-3]
            elif response_text.startswith('```'):
                response_text = response_text[3:-3]
            
            json_response = json.loads(response_text)
            return json_response
            
        except json.JSONDecodeError as e:
            st.error(f"Error parsing AI response: {str(e)}")
            return None
        
    except Exception as e:
        st.error(f"Error processing video: {str(e)}")
        return None

def display_script_variations(json_data):
    """Display script variations in formatted tables"""
    if not json_data or "script_variations" not in json_data:
        st.error("No script variations found in the response")
        return
    
    for i, variation in enumerate(json_data["script_variations"], 1):
        variation_name = variation.get("variation_name", f"Variation {i}")
        
        st.subheader(variation_name)
        
        # Convert script table to DataFrame for better display
        script_data = variation.get("script_table", [])
        if script_data:
            df = pd.DataFrame(script_data)
            
            # Rename columns for better display
            column_mapping = {
                'timestamp': 'Timestamp',
                'script_voiceover': 'Script / Voiceover',
                'visual_direction': 'Visual Direction',
                'psychological_trigger': 'Psychological Trigger',
                'cta_action': 'CTA / Action'
            }
            
            df = df.rename(columns=column_mapping)
            
            # Display as interactive table
            st.dataframe(
                df,
                use_container_width=True,
                hide_index=True,
                column_config={
                    "Timestamp": st.column_config.TextColumn(width="small"),
                    "Script / Voiceover": st.column_config.TextColumn(width="large"),
                    "Visual Direction": st.column_config.TextColumn(width="large"),
                    "Psychological Trigger": st.column_config.TextColumn(width="medium"),
                    "CTA / Action": st.column_config.TextColumn(width="medium")
                }
            )
        else:
            st.warning(f"No script data available for {variation_name}")
        
        st.divider()

def display_video_analysis(json_data):
    """Display video analysis in tabular format"""
    if not json_data or "video_analysis" not in json_data:
        st.error("No video analysis found in the response")
        return
    
    analysis = json_data["video_analysis"]
    
    # Display general analysis
    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'))
    
    # Display video metrics in tabular format
    st.subheader("Detailed Video Metrics (Timestamp Analysis)")
    video_metrics = analysis.get('video_metrics', [])
    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")
            }
        )
    else:
        st.warning("No detailed video metrics available")

def display_timestamp_improvements(json_data):
    """Display timestamp-based improvements in tabular format"""
    if not json_data or "timestamp_improvements" not in json_data:
        st.error("No timestamp improvements found in the response")
        return
    
    st.subheader("Timestamp-by-Timestamp Improvement Recommendations")
    
    improvements = json_data["timestamp_improvements"]
    if improvements:
        improvements_df = pd.DataFrame(improvements)
        
        # Rename columns for better display
        column_mapping = {
            'timestamp': 'Timestamp',
            'current_element': 'Current Element',
            'improvement_type': 'Improvement Type',
            'recommended_change': 'Recommended Change',
            'expected_impact': 'Expected Impact',
            'priority': 'Priority'
        }
        
        improvements_df = improvements_df.rename(columns=column_mapping)
        
        # Color code priority
        def color_priority(val):
            if val == 'High':
                return 'background-color: #ffcccb'
            elif val == 'Medium':
                return 'background-color: #ffffcc'
            elif val == 'Low':
                return 'background-color: #ccffcc'
            return ''
        
        styled_df = improvements_df.style.applymap(color_priority, subset=['Priority'])
        
        st.dataframe(
            styled_df,
            use_container_width=True,
            hide_index=True,
            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"""
    
    # Header
    st.title("Video Analyser and Script Generator")
    st.divider()

    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)
    else:
    
        # Sidebar for inputs
        with st.sidebar:
            st.header("Input Configuration")
            
            # Video upload
            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"
            )
            
            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
            generate_button = st.button(
                "Generate Script Variations",
                type="primary",
                use_container_width=True
            )
            
            # Clear results button (only show if results exist)
            if "analysis_results" in st.session_state and st.session_state["analysis_results"]:
                if st.button(
                    "Clear Results",
                    type="secondary",
                    use_container_width=True
                ):
                    del st.session_state["analysis_results"]
                    st.rerun()
        
        # Main content area
        if uploaded_video is None:
            st.info("Please upload a reference video to begin analysis.")
            
            # Instructions
            with st.expander("How to Use This Tool"):
                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
                """)
        
        elif generate_button:
            if not GEMINI_API_KEY or GEMINI_API_KEY == "your-gemini-api-key-here":
                st.error("Please configure your Gemini API key in the backend.")
                return
            
            # Process video
            with st.spinner("Analyzing video and generating scripts..."):
                video_bytes = uploaded_video.read()
                
                # Reset file pointer for potential re-use
                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:
                # Store results in session state
                st.session_state["analysis_results"] = json_response
                st.success("Analysis complete! Here are your script variations:")
            else:
                st.error("Failed to generate script variations. Please try again.")
        
        # Display results if they exist in session state
        if "analysis_results" in st.session_state and st.session_state["analysis_results"]:
            json_response = st.session_state["analysis_results"]
            
            # Create tabs for different outputs
            tab1, tab2, tab3 = st.tabs(["Script Variations", "Video Analysis", "Improvement Recommendations"])
            
            with tab1:
                display_script_variations(json_response)
                
                # CSV Download button
                csv_content = create_csv_download(json_response)
                st.download_button(
                    label="Download All Scripts (CSV)",
                    data=csv_content,
                    file_name="video_script_variations.csv",
                    mime="text/csv",
                    type="secondary",
                    use_container_width=True
                )
            
            with tab2:
                display_video_analysis(json_response)
            
            with tab3:
                display_timestamp_improvements(json_response)
        
        else:
            st.info("Configure your inputs in the sidebar and click 'Generate Script Variations' to begin.")

if __name__ == "__main__":
    try:
        logger.info("Launching Streamlit app...")
        main()
    except Exception as e:
        logger.exception("Unhandled error during app launch.")