Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import tensorflow as tf
+
+def classify_image(Image):
+    model = tf.keras.models.load_model('tumor_model.h5')
+    labels = [
+        'Tumor - Glioma', 
+        'No Tumor', 
+        'Tumor - Meningioma', 
+        'Tumor - Pituitary'
+    ]
+    
+    inp = Image.reshape((-1, 128, 128, 3))
+    inp = tf.keras.applications.resnet50.preprocess_input(inp)
+    prediction = model.predict(inp).flatten()
+    confidences = {labels[i]: float(prediction[i]) for i in range(4)}
+    
+    return confidences
+
+image_input = gr.inputs.Image(shape = (128, 128))
+label_output = gr.outputs.Label(num_top_classes = 4)
+
+title = "Tumor Classification"
+description = "Upload an image and get predictions for tumor classification."
+examples = [
+    ['Example 1.jpg'],
+    ['Example 2.jpg']
+]
+
+interface = gr.Interface(
+    fn = classify_image,
+    inputs = image_input,
+    outputs = label_output,
+    title = title,
+    description = description,
+    examples = examples,
+    button_style ='danger',
+    theme = 'huggingface'
+)
+
+interface.launch()