app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import requests
from io import BytesIO

# Load the model and processor
repo_name = "cyan2k/molmo-7B-O-bnb-4bit"
arguments = {
    "device_map": "auto",   # Force CPU inference
    "torch_dtype": "auto",  # Set model to use float32 precision
    "trust_remote_code": True  # Allow the loading of remote code
}

# Load the processor and model
processor = AutoProcessor.from_pretrained(repo_name, **arguments)
model = AutoModelForCausalLM.from_pretrained(repo_name, **arguments)

def describe_image(image):
    # Process the uploaded image
    inputs = processor.process(
        images=[image],
        text="Describe this image in great detail without missing any piece of information"
    )

    # Move inputs to model device
    inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}

    # Generate output
    output = model.generate_from_batch(
        inputs,
        GenerationConfig(max_new_tokens=1024, stop_strings="<|endoftext|>"),
        tokenizer=processor.tokenizer,
    )

    # Decode the generated tokens
    generated_tokens = output[0, inputs["input_ids"].size(1):]
    generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)

    return generated_text


def gradio_app():
    # Define Gradio interface
    image_input = gr.Image(type="pil", label="Upload Image")
    output_text = gr.Textbox(label="Image Description", interactive=False)

    # Create Gradio interface
    interface = gr.Interface(
        fn=describe_image,
        inputs=image_input,
        outputs=output_text,
        title="Image Description App",
        description="Upload an image and get a detailed description using the Molmo 7B model"
    )

    # Launch the interface
    interface.launch()

# Launch the Gradio app
gradio_app()