Update app.py

app.py

@@ -1,46 +1,23 @@
-import gradio as gr
-import numpy as np
-from PIL import Image
-from keras.models import load_model
-import json
-
-# Load the pre-trained model
-model = load_model('brain_tumor_model.h5')
-
-# Function to process the image and make predictions
 def predict_image(image):
-    # Resize the image
-    img = image.resize((128, 128))
-    # Convert the image to a NumPy array
-    img = np.array(img)
-    # Check if the image has 3 color channels
-    if img.shape == (128, 128):  # If grayscale, convert to RGB
-        img = np.stack((img,) * 3, axis=-1)
-    # Add a batch dimension
-    img = np.expand_dims(img, axis=0) / 255.0  # Normalize the image
-    # Make the prediction
-    prediction = model.predict(img)
-    # Get the predicted class and confidence level
-    predicted_class = np.argmax(prediction)
-    confidence = np.max(prediction) * 100  # Convert to percentage
-    # Return the results
-    if predicted_class == 0:
-        return f'No tumor detected. Confidence: {confidence:.2f}%'
-    else:
-        return f'Tumor detected. Confidence: {confidence:.2f}%'
-
-# Load examples from the JSON file
-with open('examples.json') as f:
-    examples = json.load(f)
-
-# Create the Hugging Face Space
-iface = gr.Interface(
-    fn=predict_image,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Textbox(),
-    title="Brain Tumor Detection AI App",
-    description="Upload an image to detect brain tumors.",
-    examples=examples
-)
-
-iface.launch(share=True)
+    try:
+        # Resize the image
+        img = image.resize((128, 128))
+        # Convert the image to a NumPy array
+        img = np.array(img)
+        # Check if the image has 3 color channels
+        if img.shape == (128, 128):  # If grayscale, convert to RGB
+            img = np.stack((img,) * 3, axis=-1)
+        # Add a batch dimension
+        img = np.expand_dims(img, axis=0) / 255.0  # Normalize the image
+        # Make the prediction
+        prediction = model.predict(img)
+        # Get the predicted class and confidence level
+        predicted_class = np.argmax(prediction)
+        confidence = np.max(prediction) * 100  # Convert to percentage
+        # Return the results
+        if predicted_class == 0:
+            return f'No tumor detected. Confidence: {confidence:.2f}%'
+        else:
+            return f'Tumor detected. Confidence: {confidence:.2f}%'
+    except Exception as e:
+        return f'Error processing image: {str(e)}'