Upload app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import google.generativeai as genai 
+import os
+import markdown2
+
+# Load the TensorFlow model
+model_path = 'model'
+model = tf.saved_model.load(model_path)
+
+# Configure Gemini API
+api_key = os.getenv("GEMINI_API_KEY")
+genai.configure(api_key=api_key)
+
+labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']
+
+def get_disease_detail(disease_name):
+    if disease_name == "normal":
+        prompt = (
+            "Create a text that congratulates having healthy eyes and gives bullet point tips to keep eyes healthy."
+        )
+    else:
+        prompt = (
+            f"Diagnosis: {disease_name}\n\n"
+            "What is it?\n(Description about {disease_name})\n\n"
+            "What causes it?\n(Explain what causes {disease_name})\n\n"
+            "Suggestion\n(Suggestion to user)\n\n"
+            "Reminder: Always seek professional help, such as a doctor."
+        )
+    try:
+        response = genai.GenerativeModel("gemini-1.5-flash").generate_content(prompt)
+        return markdown2.markdown(response.text.strip())
+    except Exception as e:
+        return f"Error: {e}"
+
+def predict_image(image):
+    image_resized = image.resize((224, 224))
+    image_array = np.array(image_resized).astype(np.float32) / 255.0
+    image_array = np.expand_dims(image_array, axis=0)
+
+    predictions = model.signatures['serving_default'](tf.convert_to_tensor(image_array, dtype=tf.float32))['output_0']
+    
+    # Highest prediction
+    top_index = np.argmax(predictions.numpy(), axis=1)[0]
+    top_label = labels[top_index]
+    top_probability = predictions.numpy()[0][top_index]
+
+    explanation = get_disease_detail(top_label)
+
+    return {top_label: top_probability}, explanation
+
+# Example images
+example_images = [
+    ["exp_eye_images/0_right_h.png"],
+    ["exp_eye_images/03fd50da928d_dr.png"],
+    ["exp_eye_images/108_right_h.png"],
+    ["exp_eye_images/1062_right_c.png"],
+    ["exp_eye_images/1084_right_c.png"],
+    ["exp_eye_images/image_1002_g.jpg"]
+]
+
+# Custom CSS for HTML height
+css = """
+.scrollable-html {
+    height: 206px;
+    overflow-y: auto;  
+    border: 1px solid #ccc;  
+    padding: 10px;  
+    box-sizing: border-box;
+}
+"""
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(num_top_classes=1, label="Prediction"), 
+        gr.HTML(label="Explanation", elem_classes=["scrollable-html"])
+    ],
+    examples=example_images,
+    title="Eye Diseases Classifier",
+    description=(
+        "Upload an image of an eye fundus, and the model will predict it.\n\n"
+        "**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."
+    ),
+    allow_flagging="never",
+    css=css
+)
+
+interface.launch(share=True)