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#import spaces
import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import torch
import requests
# 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'
)
#@spaces.GPU
def describe_image(image, prompt):
# Process the image with the user-provided text prompt
inputs = processor.process(images=[image], text=prompt)
# 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 with a maximum of 200 new tokens
output = model.generate_from_batch(
inputs,
GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
tokenizer=processor.tokenizer
)
# Decode and return the generated text
generated_tokens = output[0, inputs['input_ids'].size(1):]
generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True)
return generated_text
# Gradio interface using the latest API
with gr.Blocks() as demo:
gr.Markdown("# Visual Language Model - Molmo")
with gr.Row():
image_input = gr.Image(type="pil", label="Upload an image")
text_input = gr.Textbox(label="Enter a prompt", placeholder="Describe this image...")
output_text = gr.Textbox(label="Generated Description")
submit_button = gr.Button("Generate Description")
# Connect the inputs (image, text prompt) to the function and output
submit_button.click(fn=describe_image, inputs=[image_input, text_input], outputs=output_text)
# Launch the app
demo.launch()
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