adjust output for disease explanation

app.py

@@ -5,75 +5,45 @@ from PIL import Image
 import google.generativeai as genai 
 import os
 
-# Load TensorFlow model
+# Load the TensorFlow model
 model_path = 'model'
 model = tf.saved_model.load(model_path)
 
-# Set up Gemini API
+# Configure Gemini API
 api_key = os.getenv("GEMINI_API_KEY")
 genai.configure(api_key=api_key)
 
-# Labels for classification
 labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']
 
 def get_disease_detail(disease_name):
     prompt = (
         f"Diagnosis: {disease_name}\n\n"
-        "What is it?\n(Description about the disease)\n\n"
-        "What causes it?\n(Explain what causes the disease)\n\n"
-        "Suggestions\n(Suggestion to user)\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."
     )
-    response = genai.GenerativeModel("gemini-1.5-flash").generate_content(prompt)
-
-    # Make sure we check for candidates and handle possible missing attributes correctly
-    if response.candidates and response.candidates[0].text:
-        return response.candidates[0].text.strip()
-    else:
-        return "No detailed explanation available."
-
-def safe_extract_section(text, start_keyword, end_keyword):
-    """ Safely extract sections from the Gemini response based on start and end keywords."""
-    if start_keyword in text and end_keyword in text:
-        return text.split(start_keyword)[1].split(end_keyword)[0].strip()
-    elif start_keyword in text:
-        return text.split(start_keyword)[1].strip()
-    else:
-        return "Information not available."
+    try:
+        response = genai.GenerativeModel("gemini-1.5-flash").generate_content(prompt)
+        return response.text.strip()
+    except Exception as e:
+        return f"Error: {e}"
 
 def predict_image(image):
-    # Preprocess the 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)
 
-    # Run prediction
     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] * 100  # Convert to percentage
 
-    # Get explanation from Gemini API
     explanation = get_disease_detail(top_label)
 
-    # Extract relevant sections from the explanation
-    diagnosis_section = f"**Diagnosis:** {top_label}"
-    what_is_it = safe_extract_section(explanation, "What is it?", "What causes it?")
-    causes = safe_extract_section(explanation, "What causes it?", "Suggestions")
-    suggestions = safe_extract_section(explanation, "Suggestions", "Reminder")
-    reminder = "Always seek professional help, such as a doctor."
-
-    # Format explanation
-    formatted_explanation = (
-        f"{diagnosis_section}\n\n"
-        f"**What is it?** {what_is_it}\n\n"
-        f"**What causes it?** {causes}\n\n"
-        f"**Suggestions:** {suggestions}\n\n"
-        f"**Reminder:** {reminder}"
-    )
-
-    # Return both the prediction and the explanation
-    return {top_label: top_probability}, formatted_explanation
+    return {top_label: top_probability}, explanation
 
 # Example images
 example_images = [
@@ -91,7 +61,7 @@ interface = gr.Interface(
     inputs=gr.Image(type="pil"),
     outputs=[
         gr.Label(num_top_classes=1, label="Prediction"), 
-        gr.Textbox(label="Explanation")
+        gr.Textbox(label="Explanation", lines=15)
     ],
     examples=example_images,
     title="Eye Diseases Classifier",