Update app.py

app.py

@@ -6,51 +6,45 @@ import google.generativeai as genai
 import os
 import markdown2
 
-# Load TensorFlow model
+# Load TensorFlow model & configure Gemini API
 model = tf.saved_model.load('model')
-labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']
-
-# Configure Gemini API
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-# 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}."
-    )
+# Disease labels & inline prompt generation for AI response
+labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']
+prompt_map = lambda disease: (
+    "Provide a congratulatory message for healthy eyes with tips for maintenance." if disease == "normal"
+    else f"Diagnosis: {disease}\nDescription, causes, and prevention advice for {disease}."
+)
+
+# Function for AI-based disease explanation
+def generate_explanation(disease):
     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.")
+        response = genai.GenerativeModel("gemini-1.5-flash").generate_content(prompt_map(disease))
+        return markdown2.markdown(response.text.strip()) if response and response.text else "No response available."
     except Exception as e:
         return f"Error: {e}"
 
-# Process and predict uploaded image
+# Image processing & prediction
 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']
+    img_array = np.expand_dims(np.array(image.resize((224, 224))) / 255.0, axis=0).astype(np.float32)
+    predictions = model.signatures['serving_default'](tf.convert_to_tensor(img_array))['output_0']
 
     top_label = labels[np.argmax(predictions.numpy())]
-    explanation = get_disease_detail(top_label)
-
-    return {top_label: predictions.numpy().max()}, explanation
+    return {top_label: predictions.numpy().max()}, generate_explanation(top_label)
 
-# 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"]]
-
-# Gradio Interface
+# Gradio Interface with minimalist style
 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,
+    examples=[[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"]],
     title="DR Predictor",
-    description=("Upload an eye fundus image, and the model predicts the condition. This model is for educational use only."),
+    description="Upload an eye fundus image to receive a condition prediction. *For educational use only*.",
     allow_flagging="never",
     css=".scrollable-html {height: 206px; overflow-y: auto; border: 1px solid #ccc; padding: 10px; box-sizing: border-box;}"
 )
 
 interface.launch(share=True)
 
+