app.py

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.
Note: In video, irrelevant frames may occur (e.g., other windows tabs, eterniq website, etc.) in video. Please focus on code-specific frames as we have to extract that content only.
"""

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.
Note: Please provide the output in a well-structured Markdown format. Remove all unnecessary information and exclude any additional code formatting such as triple backticks or language identifiers. The response should be ready to be rendered as Markdown content.
"""

def process_video_for_code(video_path, transcribed_text, 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()
    
    if not frames:
        return "No frames could be extracted from the video.", "No code could be analyzed."

    # Process all frames
    vision_descriptions = []
    for frame in frames:
        vision_description = process_image_for_vision(frame, transcribed_text)
        vision_descriptions.append(vision_description)

    # Combine all vision descriptions
    combined_vision_description = "\n\n".join(vision_descriptions)

    # Use code model to fix the code based on combined description
    fixed_code_response = process_for_code(combined_vision_description)

    return combined_vision_description, fixed_code_response

def process_image_for_vision(image, transcribed_text):
    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. User's description: {transcribed_text}"},
            ],
        }
    ]

    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)
    ]
    return vision_processor.batch_decode(
        vision_output_trimmed, 
        skip_special_tokens=True, 
        clean_up_tokenization_spaces=False
    )[0]

def process_for_code(vision_description):
    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)
    ]
    return code_tokenizer.batch_decode(
        code_output_trimmed,
        skip_special_tokens=True
    )[0]

@spaces.GPU
def process_content(video, transcribed_text):
    if video is None:
        return "Please upload a video file of code with errors.", ""

    vision_output, code_output = process_video_for_code(video.name, transcribed_text)
    return vision_output, code_output

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

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