import torch
from transformers import (
    Qwen2VLForConditionalGeneration, 
    AutoProcessor,
    AutoModelForCausalLM, 
    AutoTokenizer
)
from qwen_vl_utils import process_vision_info
from PIL import Image
import cv2
import numpy as np
import gradio as gr
import spaces

# Load both models and their processors/tokenizers
def load_models():
    # Vision model
    vision_model = Qwen2VLForConditionalGeneration.from_pretrained(
        "Qwen/Qwen2-VL-2B-Instruct",
        torch_dtype=torch.float16,
        device_map="auto"
    )
    vision_processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
    
    # Code model
    code_model = AutoModelForCausalLM.from_pretrained(
        "Qwen/Qwen2.5-Coder-1.5B-Instruct",
        torch_dtype=torch.float16,
        device_map="auto"
    )
    code_tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct")
    
    return vision_model, vision_processor, code_model, code_tokenizer

vision_model, vision_processor, code_model, code_tokenizer = load_models()

VISION_SYSTEM_PROMPT = """You are an AI assistant specialized in analyzing images and videos of code editors. Your task is to:
1. Extract and describe any code snippets visible in the image
2. Identify any error messages, warnings, or highlighting that indicates bugs
3. Describe the programming language and context if visible
Be thorough and accurate in your description, as this will be used to fix the code."""

CODE_SYSTEM_PROMPT = """You are an expert code debugging assistant. Based on the description of code and errors provided, your task is to:
1. Identify the bugs and issues in the code
2. Provide a corrected version of the code
3. Explain the fixes made and why they resolve the issues
Be thorough in your explanation and ensure the corrected code is complete and functional."""

def process_image_for_code(image):
    # First, process with vision model
    vision_messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": f"{VISION_SYSTEM_PROMPT}\n\nDescribe the code and any errors you see in this image."},
            ],
        }
    ]

    vision_text = vision_processor.apply_chat_template(
        vision_messages, 
        tokenize=False, 
        add_generation_prompt=True
    )
    image_inputs, video_inputs = process_vision_info(vision_messages)

    vision_inputs = vision_processor(
        text=[vision_text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    ).to(vision_model.device)

    with torch.no_grad():
        vision_output_ids = vision_model.generate(**vision_inputs, max_new_tokens=512)
    vision_output_trimmed = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(vision_inputs.input_ids, vision_output_ids)
    ]
    vision_description = vision_processor.batch_decode(
        vision_output_trimmed, 
        skip_special_tokens=True, 
        clean_up_tokenization_spaces=False
    )[0]

    # Then, use code model to fix the code
    code_messages = [
        {"role": "system", "content": CODE_SYSTEM_PROMPT},
        {"role": "user", "content": f"Here's a description of code with errors:\n\n{vision_description}\n\nPlease analyze and fix the code."}
    ]
    
    code_text = code_tokenizer.apply_chat_template(
        code_messages,
        tokenize=False,
        add_generation_prompt=True
    )
    
    code_inputs = code_tokenizer([code_text], return_tensors="pt").to(code_model.device)
    
    with torch.no_grad():
        code_output_ids = code_model.generate(
            **code_inputs,
            max_new_tokens=1024,
            temperature=0.7,
            top_p=0.95,
        )
    
    code_output_trimmed = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(code_inputs.input_ids, code_output_ids)
    ]
    fixed_code_response = code_tokenizer.batch_decode(
        code_output_trimmed,
        skip_special_tokens=True
    )[0]
    
    return vision_description, fixed_code_response

def process_video_for_code(video_path, max_frames=16, frame_interval=30):
    cap = cv2.VideoCapture(video_path)
    frames = []
    frame_count = 0
    
    while len(frames) < max_frames:
        ret, frame = cap.read()
        if not ret:
            break
            
        if frame_count % frame_interval == 0:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            frame = Image.fromarray(frame)
            frames.append(frame)
            
        frame_count += 1
        
    cap.release()
    
    # Process the first frame for now (you could extend this to handle multiple frames)
    if frames:
        return process_image_for_code(frames[0])
    else:
        return "No frames could be extracted from the video.", "No code could be analyzed."

@spaces.GPU
def process_content(content):
    if content is None:
        return "Please upload an image or video file of code with errors.", ""

    if content.name.lower().endswith(('.png', '.jpg', '.jpeg')):
        image = Image.open(content.name)
        vision_output, code_output = process_image_for_code(image)
    elif content.name.lower().endswith(('.mp4', '.avi', '.mov')):
        vision_output, code_output = process_video_for_code(content.name)
    else:
        return "Unsupported file type. Please provide an image or video file.", ""

    return vision_output, code_output

# Gradio interface
iface = gr.Interface(
    fn=process_content,
    inputs=gr.File(label="Upload Image or Video of Code with Errors"),
    outputs=[
        gr.Textbox(label="Vision Model Output (Code Description)"),
        gr.Code(label="Fixed Code", language="python")
    ],
    title="Vision Code Debugger",
    description="Upload an image or video of code with errors, and the AI will analyze and fix the issues."
)

if __name__ == "__main__":
    iface.launch()