"""
Whisper-based Video Analysis Service
Optimized endpoint that finds trim point and extracts frame in one call
"""

from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
from typing import Optional
import tempfile
import os
import httpx

router = APIRouter()

# Check Whisper availability
try:
    from utils.whisper_trim import find_last_word_timestamp, transcribe_video, is_whisper_available
    from utils.video_processor import extract_frame, get_video_info
    WHISPER_AVAILABLE = is_whisper_available()
except ImportError:
    WHISPER_AVAILABLE = False


class WhisperAnalyzeRequest(BaseModel):
    video_url: str
    dialogue: str  # The expected dialogue/script for this segment
    buffer_time: float = 0.3  # Time after last word for frame extraction
    model_size: str = "base"  # Whisper model size


class WhisperAnalyzeResponse(BaseModel):
    success: bool
    last_word_timestamp: Optional[float] = None  # When last word ends
    trim_point: Optional[float] = None  # Recommended trim point (last_word + buffer)
    frame_timestamp: Optional[float] = None  # Where frame was extracted
    frame_base64: Optional[str] = None  # Base64 encoded frame image
    video_duration: float = 0  # Total video duration
    transcribed_text: Optional[str] = None  # What Whisper actually heard (for consistency check)
    error: Optional[str] = None


@router.post("/whisper/analyze-and-extract", response_model=WhisperAnalyzeResponse)
async def analyze_and_extract_frame(request: WhisperAnalyzeRequest):
    """
    Analyze video with Whisper to find last spoken word,
    then extract frame at that point for visual continuity.
    
    This is the optimized flow:
    1. Download video
    2. Use Whisper to find last spoken word timestamp
    3. Extract frame at (last_word_time + buffer)
    4. Return frame + trim metadata
    
    The trim metadata can be used later during final merge.
    """
    temp_video = None
    
    try:
        # Download video to temp file
        print(f"🎤 Downloading video for Whisper analysis...")
        temp_video = tempfile.mktemp(suffix='.mp4')
        
        async with httpx.AsyncClient(timeout=120.0) as client:
            response = await client.get(request.video_url)
            if response.status_code != 200:
                return WhisperAnalyzeResponse(
                    success=False,
                    error=f"Failed to download video: {response.status_code}"
                )
            
            with open(temp_video, 'wb') as f:
                f.write(response.content)
        
        # Get video duration
        video_info = get_video_info(temp_video)
        video_duration = float(video_info['format']['duration'])
        print(f"📹 Video duration: {video_duration:.2f}s")
        
        # Try Whisper-based analysis
        last_word_time = None
        frame_base64 = None
        trim_point = None
        frame_timestamp = None
        transcribed_text = None
        
        if WHISPER_AVAILABLE:
            try:
                print(f"🎤 Running Whisper transcription (model: {request.model_size})...")
                
                # Get full transcription and last word timestamp
                transcribed_text, last_word_time = transcribe_video(
                    video_path=temp_video,
                    model_size=request.model_size
                )
                
                if last_word_time and last_word_time > 0:
                    print(f"✅ Last spoken word at: {last_word_time:.2f}s")
                    
                    # Calculate trim point and frame timestamp
                    trim_point = min(last_word_time + request.buffer_time, video_duration)
                    frame_timestamp = min(last_word_time + request.buffer_time, video_duration - 0.1)
                    
                    print(f"📍 Trim point: {trim_point:.2f}s, Frame at: {frame_timestamp:.2f}s")
                else:
                    print(f"⚠️ Could not find last word, using fallback")
                    
            except Exception as whisper_err:
                print(f"⚠️ Whisper analysis failed: {str(whisper_err)}")
        else:
            print("⚠️ Whisper not available, using fallback")
        
        # Fallback: use end of video
        if frame_timestamp is None:
            frame_timestamp = max(0, video_duration - 0.5)
            trim_point = video_duration
            print(f"📍 Fallback: Frame at {frame_timestamp:.2f}s (near end)")
        
        # Extract frame at the calculated timestamp (uncompressed for continuity)
        print(f"📸 Extracting frame at {frame_timestamp:.2f}s")
        frame_base64 = extract_frame(
            video_path=temp_video,
            timestamp=frame_timestamp,
            return_base64=True,
            compress=False  # No compression for continuity frames
        )
        print(f"✅ Frame extracted successfully")
        
        return WhisperAnalyzeResponse(
            success=True,
            last_word_timestamp=last_word_time,
            trim_point=trim_point,
            frame_timestamp=frame_timestamp,
            frame_base64=frame_base64,
            video_duration=video_duration,
            transcribed_text=transcribed_text,
            error=None
        )
        
    except Exception as e:
        print(f"❌ Whisper analyze error: {str(e)}")
        import traceback
        traceback.print_exc()
        
        return WhisperAnalyzeResponse(
            success=False,
            error=str(e)
        )
        
    finally:
        # Clean up temp file
        if temp_video and os.path.exists(temp_video):
            try:
                os.remove(temp_video)
            except:
                pass


@router.get("/whisper/status")
async def whisper_status():
    """Check if Whisper is available and ready"""
    return {
        "available": WHISPER_AVAILABLE,
        "message": "Whisper is ready" if WHISPER_AVAILABLE 
                   else "Whisper not installed. Run: pip install openai-whisper moviepy"
    }