Update app.py

app.py

@@ -1,52 +1,37 @@
 import gradio as gr
 import tensorflow as tf
-import numpy as np
-from tensorflow.keras.preprocessing import image
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
 from tensorflow.keras.applications.efficientnet_v2 import preprocess_input
-from PIL import Image
+import numpy as np
 
-# Load the trained model
-MODEL_PATH = "setosys_dogs_model.h5"
-model = tf.keras.models.load_model(MODEL_PATH)
+# Load the trained model (replace with correct path to your model)
+model = tf.keras.models.load_model("setosys_dogs_model.h5")
 
-# Get class labels from the model (assuming the model has a 'class_indices' attribute)
-class_labels = list(model.class_indices.keys())  # Fetch class labels from the model
+# Get class labels dynamically from model (from class_indices)
+class_labels = {v: k for k, v in model.class_indices.items()}
 
-# Image preprocessing function using EfficientNetV2S
-def preprocess_image(img: Image.Image) -> np.ndarray:
-    """Preprocess the image to match the model's input requirements."""
-    img = img.resize((224, 224))  # Resize image to model input size
-    img_array = np.array(img)
+# Preprocessing function for EfficientNetV2 model
+def preprocess_image(image_path):
+    img = load_img(image_path, 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
     return img_array
 
 # Prediction function
-def predict_dog_breed(img: Image.Image) -> dict:
-    """Predict the breed of the dog in the uploaded image."""
-    img_array = preprocess_image(img)
+def predict_dog_breed(image):
+    img_array = preprocess_image(image)
     predictions = model.predict(img_array)
-    
-    # Check the shape of the predictions to make sure the output is correct
-    print("Predictions Shape:", predictions.shape)
-    
-    class_idx = np.argmax(predictions)  # Index of the highest prediction probability
-    confidence = float(np.max(predictions))  # Confidence score
-    
-    # Get predicted breed and its confidence score
-    predicted_breed = class_labels[class_idx] if class_idx < len(class_labels) else "Unknown"
-    
-    return {predicted_breed: confidence}
+    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]
+    return breed, confidence
 
-# Create Gradio interface
-interface = gr.Interface(
-    fn=predict_dog_breed,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Label(),
-    title="Dog Breed Classifier",
-    description="Upload an image of a dog to predict its breed."
-)
+# Gradio interface definition
+iface = gr.Interface(fn=predict_dog_breed, 
+                     inputs=gr.inputs.Image(type="filepath"), 
+                     outputs=["text", "number"], 
+                     live=True)
 
 # Launch the Gradio app
-if __name__ == "__main__":
-    interface.launch()
+iface.launch(share=True)  # `share=True` gives you a public link