app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import torch

# Load the processor and model
processor = AutoProcessor.from_pretrained(
    'allenai/Molmo-7B-D-0924',
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)

model = AutoModelForCausalLM.from_pretrained(
    'allenai/Molmo-7B-D-0924',
    trust_remote_code=True,
    torch_dtype='auto',
    device_map='auto'
)

def process_image_and_text(image, text):
    # Process the image and text
    inputs = processor.process(
        images=[Image.fromarray(image)],
        text=text
    )

    # Move inputs to the correct device and make a batch of size 1
    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=200, stop_strings="<|endoftext|>"),
        tokenizer=processor.tokenizer
    )

    # Only get generated tokens; decode them to text
    generated_tokens = output[0, inputs['input_ids'].size(1):]
    generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)

    return generated_text

def chatbot(image, text, history):
    if image is None:
        return history + [("Please upload an image first.", None)]

    response = process_image_and_text(image, text)
    history.append((text, response))
    return history

# Define the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Image Chatbot with Molmo-7B-D-0924")
    
    with gr.Row():
        image_input = gr.Image(type="numpy")
        chatbot_output = gr.Chatbot()
    
    text_input = gr.Textbox(placeholder="Ask a question about the image...")
    submit_button = gr.Button("Submit")

    state = gr.State([])

    submit_button.click(
        chatbot,
        inputs=[image_input, text_input, state],
        outputs=[chatbot_output]
    )

    text_input.submit(
        chatbot,
        inputs=[image_input, text_input, state],
        outputs=[chatbot_output]
    )

demo.launch()