Update app.py

app.py

@@ -1,43 +1,43 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-
-model_path = "p_inference_fruits/Xeption_fruits.keras"
-model = tf.keras.models.load_model(model_path)
-
-# Define the core prediction function
-def predict_fruit(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, 3)
-
-    # Separate the probabilities for each class
-    p_apple = prediction[0][0]  # Probability for class 'articuno'
-    p_banana = prediction[0][1]   # Probability for class 'moltres'
-    p_pinenapple = prediction[0][2]    # Probability for class 'zapdos'
-    p_strawberries = prediction[0][3]
-    p_watermelon = prediction[0][4]
- 
-    return {'apple':  p_apple, 'banana': p_banana, 'pinenapple': p_pinenapple, 'strawberries': p_strawberries, 'watermelon': p_watermelon}
-
-# Create the Gradio interface
-input_image = gr.Image()
-iface = gr.Interface(
-    fn=predict_fruit,
-    inputs=input_image,
-    outputs=gr.Label(),
-    examples=["p_inference_fruits/images/ap1.jpeg", "p_inference_fruits/images/ap2.jpeg", "p_inference_fruits/images/ap3.jpeg", "p_inference_fruits/images/ba1.jpeg", "p_inference_fruits/images/ba2.jpeg", "p_inference_fruits/images/ba3.jpeg", "p_inference_fruits/images/pi1.jpeg","p_inference_fruits/images/pi2.jpeg","p_inference_fruits/images/pi3.jpeg","p_inference_fruits/images/st1.jpeg", "p_inference_fruits/images/st2.jpeg", "p_inference_fruits/images/st3.jpeg","p_inference_fruits/images/wa1.jpeg","p_inference_fruits/images/wa2.jpeg","p_inference_fruits/images/wa3.jpeg"],  
-    description="TEST.")
-
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+model_path = "Xeption_fruits.keras"
+model = tf.keras.models.load_model(model_path)
+
+# Define the core prediction function
+def predict_fruit(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, 3)
+
+    # Separate the probabilities for each class
+    p_apple = prediction[0][0]  # Probability for class 'articuno'
+    p_banana = prediction[0][1]   # Probability for class 'moltres'
+    p_pinenapple = prediction[0][2]    # Probability for class 'zapdos'
+    p_strawberries = prediction[0][3]
+    p_watermelon = prediction[0][4]
+ 
+    return {'apple':  p_apple, 'banana': p_banana, 'pinenapple': p_pinenapple, 'strawberries': p_strawberries, 'watermelon': p_watermelon}
+
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_fruit,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["images/ap1.jpeg", "images/ap2.jpeg", "images/ap3.jpeg", "images/ba1.jpeg", "images/ba2.jpeg", "images/ba3.jpeg", "images/pi1.jpeg","images/pi2.jpeg","images/pi3.jpeg","images/st1.jpeg", "images/st2.jpeg", "images/st3.jpeg","images/wa1.jpeg","images/wa2.jpeg","images/wa3.jpeg"],  
+    description="TEST.")
+
 iface.launch()