import streamlit as st
import google.generativeai as 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"
)

# Enhanced logging configuration
logging.basicConfig(
    level=logging.DEBUG,  # Changed to DEBUG for more detailed logs
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
    handlers=[
        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:
        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

# 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,
        video_name,
        offer_details: str = "",
        target_audience: str = "",
        specific_hooks: str = "",
        additional_context: str = ""
):
    """
    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
        upload_progress = st.progress(0)
        upload_status = st.empty()
        
        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_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}")
        
        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)
            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}")

        # 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
        
        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)}")
        
        # 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)
            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)
        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)
        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}")

            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

            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)
        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")
        
    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:
        error_msg = "No timestamp improvements found in the response"
        logger.error(error_msg)
        st.error(error_msg)
        return

    if not improvements:
        warning_msg = "No timestamp improvements available"
        logger.warning(warning_msg)
        st.warning(warning_msg)
        return

    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
        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)
        logger.debug(f"Improvements dataframe columns: {list(improvements_df.columns)}")
        
        # 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.