Update app.py

app.py

@@ -1,30 +1,33 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from tensorflow.keras.preprocessing import image
+from tensorflow.keras.models import load_model
+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
+from google.colab import files
+
+# Step 1: Upload the model to Colab (run this in a Colab cell)
+uploaded = files.upload()  # Upload the .h5 model file
 
-# Load the trained model
-MODEL_PATH = "setosys_dogs_model.h5"
-model = tf.keras.models.load_model(MODEL_PATH)
+# Step 2: Load the trained model
+MODEL_PATH = "setosys_dogs_model.h5"  # Adjust if needed
+model = load_model(MODEL_PATH)
 
-# 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
+# Step 3: Define the class labels manually (as per your model's training setup)
+# You need to know the classes that were used during model training
+class_labels = ["Labrador Retriever", "German Shepherd", "Golden Retriever", "Bulldog", "Poodle"]  # Example, update this list
 
-# Image preprocessing function using EfficientNetV2S
-def preprocess_image(img: Image.Image) -> np.ndarray:
+# Step 4: Define image preprocessing function for EfficientNetV2
+def preprocess_image(image_path):
     """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)
+    img = load_img(image_path, target_size=(224, 224))  # Resize image to model input size
+    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:
+# Step 5: Define prediction function
+def predict_dog_breed(image_path):
     """Predict the breed of the dog in the uploaded image."""
-    img_array = preprocess_image(img)
+    img_array = preprocess_image(image_path)
     predictions = model.predict(img_array)
     
     # Check the shape of the predictions to make sure the output is correct
@@ -36,17 +39,12 @@ def predict_dog_breed(img: Image.Image) -> dict:
     # 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}
+    return predicted_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."
-)
+# Step 6: Upload and test with an image
+uploaded_image = files.upload()  # Upload a test image
+image_path = list(uploaded_image.keys())[0]  # Get the filename of the uploaded image
 
-# Launch the Gradio app
-if __name__ == "__main__":
-    interface.launch()
+# Step 7: Run prediction
+breed, confidence = predict_dog_breed(image_path)
+print(f"Predicted Breed: {breed}, Confidence: {confidence:.2f}")