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import gradio as gr
from transformers import AutoModel, AutoTokenizer
import torch
from PIL import Image
# Load the model and tokenizer
model_name = "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1"
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, device_map="auto", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
def process_query(image, question):
try:
# Construct the messages for the model
msgs = [{"role": "user", "content": question}]
# Handle cases with and without an image
if image is not None:
# Convert the image to the required format
image_input = [Image.fromarray(image).convert("RGB")]
response = model.chat(
image=image_input,
msgs=msgs,
tokenizer=tokenizer,
)
else:
# For text-only queries, omit the `image` parameter
response = model.chat(
image=None,
msgs=msgs,
tokenizer=tokenizer,
)
return response
except Exception as e:
return f"Error: {str(e)}"
# Gradio interface
iface = gr.Interface(
fn=process_query,
inputs=[
gr.Image(type="numpy", label="Upload Medical Image (Optional)"),
gr.Textbox(label="Enter Your Medical Question"),
],
outputs="text",
title="Medical Multimodal Assistant",
description="Upload a medical image and/or ask a question for AI-powered assistance.",
)
iface.launch()
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