add app

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+model_path = "brain_classification.keras"
+model = tf.keras.models.load_model(model_path)
+
+labels = ['glioma_tumor', 'meningioma_tumor', 'no_tumor', 'pituitary_tumor']
+
+def predict_image(image):
+    # Bild vorverarbeiten
+    image = Image.fromarray(image.astype('uint8'))  # Konvertierung des Numpy-Arrays in ein PIL-Bild
+    image = image.resize((224, 224))  # Bildgröße anpassen auf 112x112 Pixel
+    image = np.array(image) / 255.0   # In Float konvertieren und normalisieren
+
+    # Sicherstellen, dass das Bild 3 Farbkanäle hat
+    if image.ndim == 2:  # Wenn das Bild grau ist, in RGB konvertieren
+        image = np.stack((image,)*3, axis=-1)
+
+    # Vorhersage
+    prediction = model.predict(image[None, ...])  # Batch-Dimension hinzufügen
+    confidences = {labels[i]: float(prediction[0][i]) for i in range(len(labels))}
+    return confidences
+
+# Gradio-Interface
+input_image = gr.Image()
+output_text = gr.Textbox(label="Predicted Value")
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=input_image, 
+    outputs=gr.Label(),
+    title="Brain tumor classification",
+    examples=[],  # Ensure this is not a directory path
+    description="Please upload an image of your MRI brain scan. The model will classify if you've got a tumor or not. Who needs a radiologist anyway?"
+)
+
+iface.launch(share=True)