Update app.py

app.py

@@ -1,61 +1,46 @@
-import gradio as gr
-import tensorflow as tf
-print(tf.__version__)
-import numpy as np
-from PIL import Image
-
-import os
-
-#model_path = "apple_model_transferlearning.keras"
-model_path = r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\3_Model_Training_and_Application\apple_model_transferlearning.keras"
-print("Existiert die Modell-Datei?:", os.path.exists(model_path))
-model = tf.keras.models.load_model(model_path)
-
-
-
-def predict_pokemon(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 150x150 pixels
-    image = np.array(image)
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
-
-    # Predict
-    prediction = model.predict(image)
-    # Convert the probabilities to rounded values
-    prediction = np.round(prediction, 2)
-
-    # Make sure the indices are correct according to your model's training
-    p_schorf        = prediction[0][0]   # Probability for "Schorf"
-    p_schwarzfaeule = prediction[0][1]   # Probability for "Schwarzfaeule"
-    p_zederapfel    = prediction[0][2]   # Probability for "Zederapfel"
-    p_gesund        = prediction[0][3]   # Probability for "Gesund"    
-
-    return {'gesund': p_gesund, 'schorf': p_schorf, 'schwarzfaeule': p_schwarzfaeule, 'zederapfel': p_zederapfel}
-
- 
- 
-# Create the Gradio interface
-input_image = gr.Image()
-iface = gr.Interface(
-    fn=predict_pokemon,
-    inputs=input_image,
-    outputs=gr.Label(),
-    examples=[r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Gesund1.jpg",
-              r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Gesund2.jpg",
-              r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Gesund3.jpg",
-              r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Schorf1.jpg",
-              r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Schorf2.jpg",
-              r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Schorf3.jpg",
-              r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Schwarzfaeule1.jpg",
-              r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Schwarzfaeule2.jpg",
-              r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Schwarzfaeule3.jpg",
-              r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Zederapfel1.jpg",
-              r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Zederapfel2.jpg",
-              r"C:\Users\dom-k\Visual Studio Code\6. Semester\Project1_Kupredom\Image_Classification\2_Data_collection\apple_images\model\Zederapfel3.jpg"],
-    description="Model")
- 
-iface.launch()
- 
-
+import gradio as gr
+import tensorflow as tf
+print(tf.__version__)
+import numpy as np
+from PIL import Image
+
+import os
+
+model_path = "apple_model_transferlearning.keras"
+model = tf.keras.models.load_model(model_path)
+
+def predict_apple(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 150x150 pixels
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+
+    # Predict
+    prediction = model.predict(image)
+    # Convert the probabilities to rounded values
+    prediction = np.round(prediction, 2)
+
+    # Make sure the indices are correct according to your model's training
+    p_schorf        = prediction[0][0]   # Probability for "Schorf"
+    p_schwarzfaeule = prediction[0][1]   # Probability for "Schwarzfaeule"
+    p_zederapfel    = prediction[0][2]   # Probability for "Zederapfel"
+    p_gesund        = prediction[0][3]   # Probability for "Gesund"    
+
+    return {'gesund': p_gesund, 'schorf': p_schorf, 'schwarzfaeule': p_schwarzfaeule, 'zederapfel': p_zederapfel}
+ 
+ 
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_apple,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["images/Gesund1.jpg", 
+              "images/Gesund3.jpg", 
+              "images/Gesund3.jpg"], 
+    description="APPLE MODEL")
+ 
+iface.launch()
+