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import gradio as gr |
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import tensorflow as tf |
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import numpy as np |
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from PIL import Image |
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import google.generativeai as genai |
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import os |
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import markdown2 |
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model_path = 'model' |
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model = tf.saved_model.load(model_path) |
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api_key = os.getenv("GEMINI_API_KEY") |
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genai.configure(api_key=api_key) |
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labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal'] |
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def get_disease_detail(disease_name): |
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if disease_name == "normal": |
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prompt = ( |
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"Hello! Your eye health looks great. Keep up the good work!\n\n" |
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"Here are some tips to maintain healthy eyes:\n" |
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"- Eat a balanced diet rich in vitamins A, C, and E.\n" |
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"- Get regular eye check-ups.\n" |
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"- Protect your eyes from UV light by wearing sunglasses.\n" |
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"Stay healthy and always take care of your eyes!" |
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) |
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else: |
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prompt = ( |
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f"Diagnosis: {disease_name}\n\n" |
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"What is it?\n(Description about {disease_name})\n\n" |
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"What causes it?\n(Explain what causes {disease_name})\n\n" |
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"Suggestion\n(Suggestion to user)\n\n" |
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"Reminder: Always seek professional help, such as a doctor." |
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) |
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try: |
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response = genai.GenerativeModel("gemini-1.5-flash").generate_content(prompt) |
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return markdown2.markdown(response.text.strip()) |
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except Exception as e: |
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return f"Error: {e}" |
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def predict_image(image): |
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image_resized = image.resize((224, 224)) |
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image_array = np.array(image_resized).astype(np.float32) / 255.0 |
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image_array = np.expand_dims(image_array, axis=0) |
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predictions = model.signatures['serving_default'](tf.convert_to_tensor(image_array, dtype=tf.float32))['output_0'] |
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top_index = np.argmax(predictions.numpy(), axis=1)[0] |
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top_label = labels[top_index] |
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top_probability = predictions.numpy()[0][top_index] |
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explanation = get_disease_detail(top_label) |
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return {top_label: top_probability}, explanation |
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example_images = [ |
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["exp_eye_images/0_right_h.png"], |
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["exp_eye_images/03fd50da928d_dr.png"], |
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["exp_eye_images/108_right_h.png"], |
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["exp_eye_images/1062_right_c.png"], |
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["exp_eye_images/1084_right_c.png"], |
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["exp_eye_images/image_1002_g.jpg"] |
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] |
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css = """ |
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.scrollable-html { |
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height: 210px; |
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overflow-y: auto; |
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border: 1px solid #ccc; |
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padding: 10px; |
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box-sizing: border-box; |
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} |
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""" |
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interface = gr.Interface( |
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fn=predict_image, |
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inputs=gr.Image(type="pil"), |
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outputs=[ |
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gr.Label(num_top_classes=1, label="Prediction"), |
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gr.HTML(label="Explanation", elem_classes=["scrollable-html"]) |
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], |
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examples=example_images, |
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title="Eye Diseases Classifier", |
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description=( |
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"Upload an image of an eye fundus, and the model will predict it.\n\n" |
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"**Disclaimer:** This model is intended as a form of learning process in the field of health-related machine learning and was trained with a limited amount and variety of data with a total of about 4000 data, so the prediction results may not always be correct. There is still a lot of room for improvisation on this model in the future." |
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), |
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allow_flagging="never", |
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css=css |
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) |
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interface.launch(share=True) |
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