app.py

import os
import gradio as gr
import torch
from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor
from peft import PeftModel
from huggingface_hub import login
import spaces

# Login to Hugging Face
if "HF_TOKEN" not in os.environ:
    raise ValueError("Please set the HF_TOKEN environment variable with your Hugging Face token")
login(token=os.environ["HF_TOKEN"])

# Load model and processor (do this outside the inference function to avoid reloading)
base_model_path = "meta-llama/Llama-3.2-11B-Vision-Instruct"
lora_weights_path = "taesiri/BunsBunny-LLama-3.2-11B-Vision-Instruct-DummyTask2"

processor = AutoProcessor.from_pretrained(base_model_path)
model = MllamaForConditionalGeneration.from_pretrained(
    base_model_path,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, lora_weights_path)

@spaces.GPU
def inference(image, question):
    # Prepare input
    messages = [
        {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": question}]}
    ]
    input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
    inputs = processor(image, input_text, add_special_tokens=False, return_tensors="pt").to(model.device)
    
    # Run inference
    with torch.no_grad():
        output = model.generate(**inputs, max_new_tokens=2048)
    
    # Decode output
    result = processor.decode(output[0], skip_special_tokens=True)
    return result

# Create Gradio interface
demo = gr.Interface(
    fn=inference,
    inputs=[
        gr.Image(type="pil", label="Upload Image"),
        gr.Textbox(label="Enter your question")
    ],
    outputs=gr.Textbox(label="Response"),
    title="Image Analysis AI",
    description="Upload an image and ask a question about it. The AI will analyze and respond.",
)

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