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Update app.py
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app.py
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@@ -1,11 +1,7 @@
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# Get API token from environment variable
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#api_token = os.getenv("HF_TOKEN").strip()
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import gradio as gr
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from transformers import AutoModel, AutoTokenizer
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import torch
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# Load the model and tokenizer
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model_name = "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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def process_query(image, question):
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iface = gr.Interface(
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fn=process_query,
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inputs=[
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outputs="text",
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title="Medical Multimodal Assistant",
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description="Upload a medical image and ask
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)
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iface.launch()
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import gradio as gr
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from transformers import AutoModel, AutoTokenizer
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import torch
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from PIL import Image
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# Load the model and tokenizer
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model_name = "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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def process_query(image, question):
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try:
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# Construct the messages for the model
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msgs = [{"role": "user", "content": question}]
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# Handle cases with and without an image
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if image is not None:
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# Convert the image to the required format
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image_input = [Image.fromarray(image).convert("RGB")]
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response = model.chat(
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image=image_input,
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msgs=msgs,
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tokenizer=tokenizer,
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)
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else:
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# For text-only queries, omit the `image` parameter
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response = model.chat(
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image=None,
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msgs=msgs,
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tokenizer=tokenizer,
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)
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return response
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except Exception as e:
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return f"Error: {str(e)}"
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# Gradio interface
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iface = gr.Interface(
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fn=process_query,
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inputs=[
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gr.Image(type="numpy", label="Upload Medical Image (Optional)"),
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gr.Textbox(label="Enter Your Medical Question"),
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],
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outputs="text",
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title="Medical Multimodal Assistant",
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description="Upload a medical image and/or ask a question for AI-powered assistance.",
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)
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iface.launch()
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