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import torch |
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import gradio as gr |
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import os |
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import requests |
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import base64 |
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from libra.eval import libra_eval |
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def generate_radiology_description( |
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prompt: str, |
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uploaded_current: str, |
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uploaded_prior: str, |
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temperature: float, |
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top_p: float, |
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num_beams: int, |
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max_new_tokens: int |
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) -> str: |
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if not uploaded_current or not uploaded_prior: |
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return "Please upload both current and prior images." |
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model_path = "X-iZhang/libra-v1.0-7b" |
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conv_mode = "libra_v1" |
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try: |
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print("Before calling libra_eval") |
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output = libra_eval( |
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model_path=model_path, |
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model_base=None, |
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image_file=[uploaded_current, uploaded_prior], |
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query=prompt, |
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temperature=temperature, |
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top_p=top_p, |
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num_beams=num_beams, |
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length_penalty=1.0, |
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num_return_sequences=1, |
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conv_mode=conv_mode, |
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max_new_tokens=max_new_tokens |
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) |
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print("After calling libra_eval, result:", output) |
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return output |
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except Exception as e: |
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return f"An error occurred: {str(e)}" |
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with gr.Blocks() as demo: |
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gr.Markdown("# Libra Radiology Report Generator (Local Upload Only)") |
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gr.Markdown("Upload **Current** and **Prior** images below to generate a radiology description using the Libra model.") |
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prompt_input = gr.Textbox( |
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label="Prompt", |
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value="Describe the key findings in these two images." |
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) |
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with gr.Row(): |
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uploaded_current = gr.Image( |
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label="Upload Current Image", |
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type="filepath" |
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) |
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uploaded_prior = gr.Image( |
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label="Upload Prior Image", |
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type="filepath" |
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) |
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with gr.Row(): |
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temperature_slider = gr.Slider( |
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label="Temperature", |
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minimum=0.1, |
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maximum=1.0, |
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step=0.1, |
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value=0.7 |
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) |
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top_p_slider = gr.Slider( |
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label="Top P", |
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minimum=0.1, |
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maximum=1.0, |
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step=0.1, |
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value=0.8 |
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) |
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num_beams_slider = gr.Slider( |
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label="Number of Beams", |
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minimum=1, |
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maximum=20, |
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step=1, |
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value=2 |
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) |
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max_tokens_slider = gr.Slider( |
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label="Max New Tokens", |
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minimum=10, |
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maximum=4096, |
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step=10, |
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value=128 |
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) |
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output_text = gr.Textbox( |
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label="Generated Description", |
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lines=10 |
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) |
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generate_button = gr.Button("Generate Description") |
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generate_button.click( |
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fn=generate_radiology_description, |
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inputs=[ |
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prompt_input, |
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uploaded_current, |
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uploaded_prior, |
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temperature_slider, |
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top_p_slider, |
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num_beams_slider, |
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max_tokens_slider |
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], |
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outputs=output_text |
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) |
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if __name__ == "__main__": |
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demo.launch() |
