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import gradio as gr |
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from segmentation import segment_image |
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import os |
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import requests |
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import base64 |
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HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_TOKEN") |
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API_URL = "https://api-inference.huggingface.co/models/google/medgemma-4b-it" |
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HEADERS = {"Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}"} |
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def query_medgemma(image_path, question): |
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with open(image_path, "rb") as f: |
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image_bytes = f.read() |
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encoded_image = base64.b64encode(image_bytes).decode("utf-8") |
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encoded_image = f"data:image/png;base64,{encoded_image}" |
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payload = { |
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"inputs": [ |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "text", "text": question}, |
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{"type": "image", "image": encoded_image} |
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] |
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} |
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] |
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} |
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response = requests.post(API_URL, headers=HEADERS, json=payload) |
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if response.ok: |
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try: |
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return response.json()[0]["generated_text"][-1]["content"] |
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except Exception as e: |
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return f"Parse Error: {str(e)}\nFull Response: {response.json()}" |
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else: |
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return f"Error: {response.status_code} - {response.text}" |
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default_image_path = "./image.png" |
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def segment_only(image_path): |
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_, segmented_image = segment_image(image_path) |
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return segmented_image |
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def analyze_with_medgemma(image, question): |
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return query_medgemma(image, question) |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(scale=1): |
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image_input = gr.Image(type="filepath", label="Upload Image") |
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segmented_output = gr.Image(type="numpy", label="Segmented Image") |
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image_input.change(fn=segment_only, inputs=image_input, outputs=segmented_output) |
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with gr.Column(scale=2): |
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chatbot = gr.Textbox(label="Ask MedGemma", placeholder="Enter your medical question...") |
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image_for_analysis = gr.Image(type="filepath", label="Upload image for analysis") |
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analyze_button = gr.Button("Analyze") |
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response_output = gr.Textbox(label="Response") |
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analyze_button.click(fn=analyze_with_medgemma, inputs=[image_for_analysis, chatbot], outputs=response_output) |
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demo.launch() |
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