import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
from PIL import Image
import subprocess
from datetime import datetime
import numpy as np
import os

# Install flash-attn
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

# Model and Processor Loading (Done once at startup)
MODEL_ID = "Qwen/Qwen2-VL-7B-Instruct"
model = Qwen2VLForConditionalGeneration.from_pretrained(MODEL_ID, trust_remote_code=True, torch_dtype=torch.float16).to("cuda").eval()
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)

DESCRIPTION = "[Qwen2-VL-7B Demo](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)"

@spaces.GPU
def qwen_inference(media_path, text_input=None):

    image_extensions = Image.registered_extensions()
    if media_path.endswith(tuple([i for i, f in image_extensions.items()])):
        media_type = "image"
    elif media_path.endswith(("avi", "mp4", "mov", "mkv", "flv", "wmv", "mjpeg", "wav", "gif", "webm", "m4v", "3gp")):  # Check if it's a video path
        media_type = "video"
    else:
        raise ValueError("Unsupported media type. Please upload an image or video.")
    
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": media_type,
                    media_type: media_path,
                    **({"fps": 8.0} if media_type == "video" else {}), 
                },
                {"type": "text", "text": text_input},
            ],
        }
    ]

    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    image_inputs, video_inputs = process_vision_info(messages) 
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    ).to("cuda")

    generated_ids = model.generate(**inputs, max_new_tokens=1024)
    generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
    output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    
    return output_text

css = """
  #output {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown(DESCRIPTION)

    with gr.Tab(label="Image/Video Input"):
        with gr.Row():
            with gr.Column():
                input_media = gr.File(label="Upload Image or Video", type="filepath") 
                text_input = gr.Textbox(label="Question")
                submit_btn = gr.Button(value="Submit")
            with gr.Column():
                output_text = gr.Textbox(label="Output Text")

        submit_btn.click(qwen_inference, [input_media, text_input], [output_text])

demo.launch(debug=True)