Update app.py

app.py

@@ -2,54 +2,95 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 from PIL import Image
-import google.generativeai as genai
+import google.generativeai as genai 
 import os
 import markdown2
 
-# Load TensorFlow model
-model = tf.saved_model.load('model')
-labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']
+# Load the TensorFlow model
+model_path = 'model'
+model = tf.saved_model.load(model_path)
 
 # Configure Gemini API
-genai.configure(api_key=os.getenv("AIzaSyClC-moPGp4ONPUQ0FnNjMPh035AN7oqtY"))
-
-# Generate AI-based explanation for the predicted disease
-def get_disease_detail(disease):
-    prompt = (
-        "Create a text congratulating on healthy eyes with tips to keep them healthy."
-        if disease == "normal" else
-        f"Diagnosis: {disease}\n\n"
-        f"What is {disease}?\nCauses and suggestions to prevent {disease}."
-    )
+api_key = os.getenv("GOOGLE_API_KEY") 
+api_key = "AIzaSyClC-moPGp4ONPUQ0FnNjMPh035AN7oqtY"  
+
+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() if response and response.text else "No response.")
+        return markdown2.markdown(response.text.strip())
     except Exception as e:
         return f"Error: {e}"
 
-# Process and predict uploaded image
 def predict_image(image):
-    img_array = np.expand_dims(np.array(image.resize((224, 224))).astype(np.float32) / 255.0, axis=0)
-    predictions = model.signatures['serving_default'](tf.convert_to_tensor(img_array, dtype=tf.float32))['output_0']
+    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]
 
-    top_label = labels[np.argmax(predictions.numpy())]
     explanation = get_disease_detail(top_label)
 
-    return {top_label: predictions.numpy().max()}, explanation
+    return {top_label: top_probability}, explanation
 
 # Example images
-example_images = [[f"exp_eye_images/{img}"] for img in ["0_right_h.png", "03fd50da928d_dr.png", "108_right_h.png", "1062_right_c.png", "1084_right_c.png", "image_1002_g.jpg"]]
+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"])],
+    outputs=[
+        gr.Label(num_top_classes=1, label="Prediction"), 
+        gr.HTML(label="Explanation", elem_classes=["scrollable-html"])
+    ],
     examples=example_images,
-    title="DR Predictor",
-    description=("Upload an eye fundus image, and the model predicts the condition."),
+    title="DR PREDICTOR",
+    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=".scrollable-html {height: 206px; overflow-y: auto; border: 1px solid #ccc; padding: 10px; box-sizing: border-box;}"
+    css=css
 )
 
 interface.launch(share=True)