Update app.py

app.py

@@ -1,18 +1,41 @@
-import gradio as gr
+import os
+import numpy as np
 import tensorflow as tf
 from tensorflow.keras.preprocessing.image import load_img, img_to_array
 from tensorflow.keras.applications.efficientnet_v2 import preprocess_input
-import numpy as np
+from tensorflow.keras.models import load_model
+import gradio as gr
 
-# Load the trained model (replace with correct path to your model)
-model = tf.keras.models.load_model("setosys_dogs_model.h5")
+# List of class labels from the Stanford Dogs dataset
+class_labels = [
+    'Affenpinscher', 'African_hunting_dog', 'Airedale', 'American_Staffordshire_terrier', 'American_water_spaniel', 
+    'Anatolian_shepherd_dog', 'Australian_terrier', 'Basenji', 'Basset_hound', 'Beagle', 'Bearded_collie', 
+    'Beauceron', 'Bedlington_terrier', 'Belgian_malinois', 'Belgian_sheepdog', 'Bernese_mountain_dog', 
+    'Biewer', 'Black-and-tan_coonhound', 'Black_russian_terrier', 'Border_collie', 'Border_terrier', 
+    'Borzoi', 'Boston_bull', 'Bouvier_des_Flandres', 'Boxer', 'Boykin_spaniel', 'Briard', 'Brittany', 
+    'Bull_mastiff', 'Cairn_terrier', 'Canaan_dog', 'Cavalier_king_charles_spaniel', 'Chihuahua', 
+    'Chinese_crested', 'Chow', 'Clumber_spaniel', 'Cocker_spaniel', 'Collie', 'Curly-coated_retriever', 
+    'Dachshund', 'Dalmatian', 'Dandie_Dinmont_terrier', 'Doberman', 'English_cocker_spaniel', 
+    'English_setter', 'English_springer_spaniel', 'Entlebucher_mountain_dog', 'Field_spaniel', 'Finnish_spitz', 
+    'Flat-coated_retriever', 'French_bulldog', 'German_pinscher', 'German_shepherd', 'German_short-haired_pointer', 
+    'Giant_schnauzer', 'Glen_of_imaal_terrier', 'Golden_retriever', 'Goldendoodle', 'Great_dane', 'Great_pyrenees', 
+    'Greater_swiss_mountain_dog', 'Havanese', 'Irish_setter', 'Irish_terrier', 'Irish_water_spaniel', 
+    'Italian_greyhound', 'Japanese_chin', 'Keeshond', 'Kerry_blue_terrier', 'King_charles_spaniel', 
+    'Klee_kai', 'Labrador_retriever', 'Lakeland_terrier', 'Lhasa', 'Maltese_dog', 'Manchester_terrier', 
+    'Mastiff', 'Miniature_pinscher', 'Miniature_schnauzer', 'Newfoundland', 'Norfolk_terrier', 'Norwegian_elkhound', 
+    'Norwich_terrier', 'Old_english_sheepdog', 'Otterhound', 'Papillon', 'Pekingese', 'Pembroke', 'Pharaoh_hound', 
+    'Plott', 'Pointer', 'Pomeranian', 'Poodle', 'Portuguese_water_dog', 'Rottweiler', 'Saint_bernard', 
+    'Saluki', 'Samoyed', 'Schipperke', 'Scotch_terrier', 'Shiba_inu', 'Shih-tzu', 'Siberian_husky', 'Silky_terrier', 
+    'Staffordshire_bullterrier', 'Standard_schnauzer', 'Tibetan_mastiff', 'Tibetan_terrier', 'Weimaraner', 
+    'Welsh_springer_spaniel', 'West_highland_white_terrier', 'Whippet', 'Yorkshire_terrier'
+]
 
-# Get class labels dynamically from model (from class_indices)
-class_labels = {v: k for k, v in model.class_indices.items()}
+# Load the trained model
+model = load_model("setosys_dogs_model.h5")
 
-# Preprocessing function for EfficientNetV2 model
-def preprocess_image(image_path):
-    img = load_img(image_path, target_size=(224, 224))  # Resize image to 224x224
+# Preprocessing function
+def preprocess_image(image):
+    img = load_img(image, target_size=(224, 224))  # Resize image to 224x224
     img_array = img_to_array(img)
     img_array = preprocess_input(img_array)  # EfficientNetV2 preprocessing
     img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
@@ -23,15 +46,15 @@ def predict_dog_breed(image):
     img_array = preprocess_image(image)
     predictions = model.predict(img_array)
     class_idx = np.argmax(predictions)  # Get index of the class with the highest probability
-    breed = class_labels[class_idx]  # Get label using the index
-    confidence = predictions[0][class_idx]
+    breed = class_labels[class_idx]  # Map the index to the breed name
+    confidence = predictions[0][class_idx]  # Confidence score
     return breed, confidence
 
-# Gradio interface definition
+# Create a Gradio interface
 iface = gr.Interface(fn=predict_dog_breed, 
-                     inputs=gr.inputs.Image(type="filepath"), 
+                     inputs=gr.inputs.Image(type="pil"), 
                      outputs=["text", "number"], 
                      live=True)
 
 # Launch the Gradio app
-iface.launch(share=True)  # `share=True` gives you a public link
+iface.launch()