app.py

import requests
import torch
from PIL import Image
from transformers import pipeline

pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")

# Load the processor
processor = AutoProcessor.from_pretrained(model_id)

# Define an image URL
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"

# Fetch the image using requests
image = Image.open(requests.get(url, stream=True).raw)

# Define the messages in a format the model understands (adjust as needed)
messages = [
    {"role": "user", "content": [
        {"type": "image"},  # This indicates that the input contains an image
        {"type": "text", "text": "Can you please describe this image in one sentence?"}
    ]}
]

# Generate input text with the processor
input_text = processor.apply_chat_template(messages, add_generation_prompt=True)

# Process the image and input text, prepare them for the model
inputs = processor(image, input_text, return_tensors="pt").to(model.device)

# Run the model to generate a response
output = model.generate(**inputs, max_new_tokens=70)

# Decode and print the output
print(processor.decode(output[0][inputs["input_ids"].shape[-1]:]))