Update app.py

app.py

@@ -1,32 +1,32 @@
-import gradio as gr
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-
-# Load your custom regression model
-model_path = "Doggos.keras"
-model = tf.keras.models.load_model(model_path)
-
-labels = ['Bedlington_terrier', 'Bernese_mountain_dog', 'Dandie_Dinmont', 'Gordon_setter', 'Ibizan_hound', 'Norwegian_elkhound']  # corrected labels list
-
-# Define regression function
-def predict_regression(image):
-    # Preprocess image
-    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((150, 150)).convert('RGB')  # Resize the image to 150x150 and convert to RGB
-    image = np.array(image)
-    print(image.shape)
-    # Predict
-    prediction = model.predict(image[None, ...])  # Assuming single regression value
-    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
-    return confidences
-
-# Create Gradio interface
-input_image = gr.Image()
-output_text = gr.Textbox(label="Predicted Value")
-interface = gr.Interface(fn=predict_regression, 
-                         inputs=input_image, 
-                         outputs=gr.Label(),
-                         examples=["1.jpg","2.jpg","3.jpg","4.jpg","5.jpg","6.jpg"],   
-                         description="A simple MLP classification model for image classification using the dog breed dataset.")
-interface.launch()
+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load your custom regression model
+model_path = "Doggos.keras"
+model = tf.keras.models.load_model(model_path)
+
+labels = ['Bedlington_terrier', 'Bernese_mountain_dog', 'Dandie_Dinmont', 'Gordon_setter', 'Ibizan_hound', 'Norwegian_elkhound']  # corrected labels list
+
+# Define regression function
+def predict_regression(image):
+    # Preprocess image
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150)).convert('RGB')  # Resize the image to 150x150 and convert to RGB
+    image = np.array(image)
+    print(image.shape)
+    # Predict
+    prediction = model.predict(image[None, ...])  # Assuming single regression value
+    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
+    return confidences
+
+# Create Gradio interface
+input_image = gr.Image()
+output_text = gr.Textbox(label="Predicted Value")
+interface = gr.Interface(fn=predict_regression, 
+                         inputs=input_image, 
+                         outputs=gr.Label(),
+                         examples=["1.jpg","2.jpg","3.jpg","4.jpg","5.jpg","6.jpg"],   
+                         description="Wer ist ein guter Junge? Du bist ein aber ganz feiner und braver Junge! hechel hechel hechel :)")
+interface.launch()