Update app.py

app.py

@@ -1,31 +1,33 @@
 import gradio as gr
 import numpy as np
 import tensorflow as tf
-from tensorflow.keras.preprocessing import image
-import os
+from PIL import Image
 
 # Load the trained model
-model = tf.keras.models.load_model("BRAIINTUMORMODEL.h5")  # Ensure model path is correct
+model = tf.keras.models.load_model("BRAIINTUMORMODEL.h5")
 
 # Define class labels
-class_labels = ["glioma_tumor", "meningioma_tumor", "no_tumor", "pituitary_tumor"]
+class_labels = ["Glioma", "Meningioma", "No Tumor", "Pituitary"]
 
-# Function to make predictions
 def predict_brain_tumor(img):
-    img = img.resize((224, 224))  # Resize to match model input
-    img_array = np.array(img) / 255.0  # Normalize
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    # Convert to RGB if needed
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    
+    # Preprocess image
+    img = img.resize((224, 224))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
 
+    # Make prediction
     prediction = model.predict(img_array)[0]
+    predicted_class = np.argmax(prediction)
+    confidence = np.max(prediction) * 100
     
-    # Create the formatted output string
-    output_str = ""
-    for label, confidence in zip(class_labels, prediction):
-        output_str += f"{label}\n{'‾'*50}\n{confidence*100:.0f}%\n\n\n"
-    
-    return output_str
+    # Format output
+    return f"Predicted Tumor Type: {class_labels[predicted_class]} (Confidence: {confidence:.2f}%)"
 
-# Example images (replace these paths with your actual example image paths)
+# Example images
 example_images = [
     ["example_glioma.jpg"],
     ["example_meningioma.jpeg"],
@@ -37,11 +39,10 @@ example_images = [
 iface = gr.Interface(
     fn=predict_brain_tumor,
     inputs=gr.Image(type="pil"),
-    outputs=gr.Textbox(label="Tumor Prediction Probabilities"),
+    outputs=gr.Textbox(label="Prediction Result"),
     title="🧠 Brain Tumor Classification",
-    description="Upload an MRI image to classify whether it has a tumor. You can also click on the example images below.",
+    description="Upload an MRI image to classify the tumor type. You can also click on the example images below.",
     examples=example_images
 )
 
-# Launch Gradio App
 iface.launch()