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. Focus exclusively on frames containing code snippets or development environments.
2. Extract any visible code snippets, ignoring non-code content.
3. Identify any error messages, warnings, or highlighting that indicates bugs within the code.

Important:
- Ignore frames showing irrelevant content such as the Eterniq dashboard, other window tabs, or non-code related screens.
- Provide a thorough and accurate description of the code-specific content only.
- If multiple code snippets are visible in different frames, describe each separately.

Your analysis will be used to understand and potentially fix the code, so maintain a high level of detail and accuracy in your descriptions of code-related content.
"""

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_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()