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import os
import torch
from transformers import AutoModel, AutoTokenizer, BitsAndBytesConfig
import gradio as gr
from PIL import Image
from torchvision.transforms import ToTensor
# Get API token from environment variable
api_token = os.getenv("HF_TOKEN").strip()
# Quantization configuration
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.float16
)
# Initialize model and tokenizer
model = AutoModel.from_pretrained(
"ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1",
quantization_config=bnb_config,
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True,
attn_implementation="flash_attention_2",
token=api_token
)
tokenizer = AutoTokenizer.from_pretrained(
"ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1",
trust_remote_code=True,
token=api_token
)
def analyze_input(image, question):
try:
if image is not None:
# Convert to RGB if image is provided
image = image.convert('RGB')
# Prepare messages in the format expected by the model
msgs = [{'role': 'user', 'content': [image, question]}]
# Generate response using the chat method
response_stream = model.chat(
image=image,
msgs=msgs,
tokenizer=tokenizer,
sampling=True,
temperature=0.95,
stream=True
)
# Collect the streamed response
generated_text = ""
for new_text in response_stream:
generated_text += new_text
print(new_text, flush=True, end='')
return {"status": "success", "response": generated_text}
except Exception as e:
import traceback
error_trace = traceback.format_exc()
print(f"Error occurred: {error_trace}")
return {"status": "error", "message": str(e)}
# Create Gradio interface
demo = gr.Interface(
fn=analyze_input,
inputs=[
gr.Image(type="pil", label="Upload Medical Image"),
gr.Textbox(
label="Medical Question",
placeholder="Give the modality, organ, analysis, abnormalities (if any), treatment (if abnormalities are present)?",
value="Give the modality, organ, analysis, abnormalities (if any), treatment (if abnormalities are present)?"
)
],
outputs=gr.JSON(label="Analysis"),
title="Medical Image Analysis Assistant",
description="Upload a medical image and ask questions about it. The AI will analyze the image and provide detailed responses."
)
# Launch the Gradio app
if __name__ == "__main__":
demo.launch(
share=True,
server_name="0.0.0.0",
server_port=7860
) |