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
from database import insert_analysis_result
from dotenv import load_dotenv

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

        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."""

        # 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.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)

        # 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("---")


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
    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")
            }
        )
    else:
        st.warning("No detailed video metrics available")

def display_timestamp_improvements(json_data):
    """Display timestamp-based improvements in tabular format"""
    improvements = json_data.get("timestamp_improvements")

    if improvements is None:
        st.error("No timestamp improvements found in the response")
        return

    if not improvements:
        st.warning("No timestamp improvements available")
        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"""

    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

    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.")