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import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image
from PIL import Image
# Load the trained model
MODEL_PATH = "setosys_dogs_model.h5"
model = tf.keras.models.load_model(MODEL_PATH)
# Define image preprocessing function
def preprocess_image(img):
img = img.resize((224, 224)) # Resize image to model input size
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
img_array = img_array / 255.0 # Normalize pixel values
return img_array
# Define the prediction function
def predict_dog_breed(img):
img_array = preprocess_image(img)
predictions = model.predict(img_array)
class_idx = np.argmax(predictions) # Get class index with highest probability
confidence = float(np.max(predictions)) # Get confidence score
# Define your class labels (replace with your actual class names)
class_labels = ["Labrador Retriever", "German Shepherd", "Golden Retriever", "Bulldog", "Poodle"]
predicted_breed = class_labels[class_idx] if class_idx < len(class_labels) else "Unknown"
return {predicted_breed: confidence}
# Create a 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.",
)
# Launch the Gradio app
if __name__ == "__main__":
interface.launch()
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