app.py

import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import requests
import gradio as gr
import spaces  # Import Hugging Face Spaces package

# Load model and tokenizer
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_name = 'scb10x/llama-3-typhoon-v1.5-8b-instruct-vision-preview'

@spaces.GPU(duration=60)  # Decorate the function to dynamically request and release GPU
def load_model():
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16 if device == 'cuda' else torch.float32,
        device_map='auto',
        trust_remote_code=True
    )
    return model

model = load_model()

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

def prepare_inputs(text, image, device='cuda'):
    messages = [
        {"role": "system", "content": "You are a helpful vision-capable assistant who eagerly converses with the user in their language."},
    ]
    messages.append({"role": "user", "content": "<|image|>\n" + text})

    inputs_formatted = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=False
    )

    text_chunks = [tokenizer(chunk).input_ids for chunk in inputs_formatted.split('<|image|>')]
    input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
    attention_mask = torch.ones_like(input_ids).to(device)

    return input_ids, attention_mask

@spaces.GPU(duration=60)  # Decorate the function for GPU use
def predict(prompt, img_url):
    try:
        image = Image.open(requests.get(img_url, stream=True).raw)
        image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device)

        input_ids, attention_mask = prepare_inputs(prompt, image, device=device)

        output_ids = model.generate(
            input_ids,
            images=image_tensor,
            max_new_tokens=100,
            use_cache=True,
            temperature=0.2,
            top_p=0.2,
            repetition_penalty=1.0
        )[0]

        result = tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()
        return result
    except Exception as e:
        return str(e)

# Gradio Interface
inputs = [
    gr.Textbox(label="Prompt", placeholder="Ask about the food in the image"),
    gr.Textbox(label="Image URL", placeholder="Enter an image URL")
]

outputs = gr.Textbox(label="Generated Output")

gr.Interface(
    fn=predict, inputs=inputs, outputs=outputs, title="Food Image AI Assistant",
    description="This model can analyze food images and answer questions about them."
).launch()