Update app.py

app.py

@@ -1,41 +1,41 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
- 
-model_path = "xception.keras"
-model = tf.keras.models.load_model(model_path)
- 
-# Define the core prediction function
-def predict_tumor(image):
-    # Preprocess image
-    print(type(image))
-    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale
-    image = np.array(image)
-    image = np.expand_dims(image, axis=0) # same as image[None, ...]
-   
-    # Predict
-    prediction = model.predict(image)
- 
-    # No need to apply sigmoid, as the output layer already uses softmax
-    # Convert the probabilities to rounded values
-    prediction = np.round(prediction, 2)
- 
-    # Separate the probabilities for each class
-    p_non_tumor = prediction[0][0]  # Probability for class 'charmander'
-    tumor = prediction[0][1]   # Probability for class 'mewto'
- 
-    return {'non_tumor': p_non_tumor, 'mewto': tumor}
- 
- 
-# Create the Gradio interface
-input_image = gr.Image()
-iface = gr.Interface(
-    fn=predict_tumor,
-    inputs=input_image,
-    outputs=gr.Label(),
-    examples=["images/1 no.jpeg", "images/3 no.jpg", "images/2 no.jpeg", "images/5 no.jpg", "images/4 no.jpg", "images/Y1.jpg", "images/Y2.jpg", "images/Y7.jpg", "images/Y4.jpg", "images/Y8.jpg"],  
-    description="TEST.")
- 
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+ 
+model_path = "xception.keras"
+model = tf.keras.models.load_model(model_path)
+ 
+# Define the core prediction function
+def predict_tumor(image):
+    # Preprocess image
+    print(type(image))
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0) # same as image[None, ...]
+   
+    # Predict
+    prediction = model.predict(image)
+ 
+    # No need to apply sigmoid, as the output layer already uses softmax
+    # Convert the probabilities to rounded values
+    prediction = np.round(prediction, 2)
+ 
+    # Separate the probabilities for each class
+    p_non_tumor = prediction[0][0]  # Probability for class 'charmander'
+    tumor = prediction[0][1]   # Probability for class 'mewto'
+ 
+    return {'kein tumor': p_non_tumor, 'tumor': tumor}
+ 
+ 
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_tumor,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["images/1 no.jpeg", "images/3 no.jpg", "images/2 no.jpeg", "images/5 no.jpg", "images/4 no.jpg", "images/Y1.jpg", "images/Y2.jpg", "images/Y7.jpg", "images/Y4.jpg", "images/Y8.jpg"],  
+    description="TEST.")
+ 
 iface.launch()