Update app.py

app.py

@@ -1,23 +1,58 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+from keras.models import load_model
+
+# Load your pre-trained model (make sure the model file is in the same directory)
+model = load_model('brain_tumor_model.h5')
+
+# Function to process image and make predictions
 def predict_image(image):
-    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)}'
+    # 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)
+
+    # Make the prediction
+    prediction = model.predict(img)
+
+    # Get the predicted class and confidence level
+    predicted_class = np.argmax(prediction)
+    confidence = np.max(prediction)
+
+    # Return the results
+    if predicted_class == 0:
+        return f'No tumor detected. Confidence: {confidence:.2f}'
+    else:
+        return f'Tumor detected. Confidence: {confidence:.2f}'
+
+# Create custom CSS for background color
+css = """
+body {
+    background-color: #f0f4f7;
+}
+"""
+
+
+# Create the Gradio interface
+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.",
+    css=css,  # Apply the custom background color
+    theme="dark",  # Apply a dark theme to the interface
+    flagging_options=["Incorrect Diagnosis", "Image Not Clear", "Model Error"],  # Add flagging options
+)
+
+# Launch the interface
+iface.launch()