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import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
from PIL import Image
import requests
# Load the processor
processor = AutoProcessor.from_pretrained(
'allenai/Molmo-7B-D-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
# Load the model
model = AutoModelForCausalLM.from_pretrained(
'allenai/Molmo-7B-D-0924',
trust_remote_code=True,
torch_dtype='auto',
device_map='auto'
)
def describe_image(image):
# Process the image and text
inputs = processor.process(
images=[image],
text='''an image of a human sitting properly , with a laptop/pc clearly visible and the student’s face at least 40%-50% visible. The student should be looking at the laptop screen with both hands on the keyboard. There should be no other accessories other than laptop/pc, and no other second person should be present ." // analyse image on this conditions // if all condition satisfied answer YES else NO// Answer only in YES or NO'''
)
# 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; maximum 200 new tokens; stop generation when <|endoftext|> is generated
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
# Create the Gradio interface
iface = gr.Interface(
fn=describe_image,
inputs=gr.Image(type="pil", label="Upload an Image"),
outputs=gr.Textbox(label="Description"),
title="OPPE",
description="OPPE VERRFICATION."
)
# Launch the interface
iface.launch()
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