import cv2
import base64
import os
import argparse
import sys
from openai import OpenAI

def extract_frames(video_path, num_frames=5):
    """Extract a few evenly spaced frames from a video and encode to base64."""
    cap = cv2.VideoCapture(video_path)
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    frames = []
    
    if total_frames == 0:
        return frames
        
    step = max(1, total_frames // num_frames)
    
    for i in range(num_frames):
        cap.set(cv2.CAP_PROP_POS_FRAMES, min(i * step, total_frames - 1))
        ret, frame = cap.read()
        if ret:
            # encode to jpeg
            _, buffer = cv2.imencode('.jpg', frame)
            b64_img = base64.b64encode(buffer).decode('utf-8')
            frames.append(f"data:image/jpeg;base64,{b64_img}")
            
    cap.release()
    return frames

def analyze_video(video_source, prompt, api_key=None, model="MiniMaxAI/MiniMax-M3", base_url="https://api.featherless.ai/v1"):
    api_key = api_key or os.getenv("FEATHERLESS_API_KEY")
    if not api_key:
        raise ValueError("API key must be provided or set in FEATHERLESS_API_KEY env var")
        
    client = OpenAI(base_url=base_url, api_key=api_key)
    
    print(f"[+] Extracting frames from {video_source}...")
    frames = extract_frames(video_source, num_frames=5)
    
    if not frames:
        raise RuntimeError("Failed to extract any frames from the video.")
    
    content = [{"type": "text", "text": prompt}]
    for frame in frames:
        content.append({"type": "image_url", "image_url": {"url": frame}})
        
    messages = [
        {
            "role": "user",
            "content": content
        }
    ]
    
    print(f"[+] Sending {len(frames)} frames to vision model...")
    response = client.chat.completions.create(
        model=model,
        messages=messages
    )
    return response.choices[0].message.content

def main():
    parser = argparse.ArgumentParser(description="Analyze video using Featherless AI vision models.")
    parser.add_argument("--video-url", required=True, help="Video file path or URL.")
    parser.add_argument("--prompt", required=True, help="Prompt for analysis.")
    parser.add_argument("--api-key", help="Featherless API key (overrides FEATHERLESS_API_KEY env var).")
    parser.add_argument("--model", default="MiniMaxAI/MiniMax-M3", help="Model name.")
    parser.add_argument("--base-url", default="https://api.featherless.ai/v1", help="Base API URL.")
    args = parser.parse_args()

    try:
        description = analyze_video(
            video_source=args.video_url,
            prompt=args.prompt,
            api_key=args.api_key,
            model=args.model,
            base_url=args.base_url,
        )
        print("\n--- Video Analysis ---\n")
        print(description)
        print("\n----------------------\n")
    except Exception as e:
        print(f"Error: {e}", file=sys.stderr)
        sys.exit(1)

if __name__ == "__main__":
    main()