app.py

import gradio as gr
from keras.models import load_model  # TensorFlow is required for Keras to work
from PIL import Image, ImageOps      # Install pillow instead of PIL
import numpy as np

# Load the model
model = load_model("keras_model.h5", compile=False)

# Load the labels
class_names = open("labels.txt", "r").readlines()

# Define the prediction function
def classify_image(image):
    # Resize the image to 224x224 and normalize
    size = (224, 224)
    image = ImageOps.fit(image, size, Image.Resampling.LANCZOS).convert("RGB")
    image_array = np.asarray(image)
    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
    data[0] = normalized_image_array

    # Predict with the model
    prediction = model.predict(data)
    index = np.argmax(prediction)
    class_name = class_names[index].strip()  # Remove any trailing spaces or newline characters
    confidence_score = prediction[0][index]    
    return f"{class_name}, Confidence Score: {float(confidence_score)}"

# Create the Gradio interface
interface = gr.Interface(
    fn = classify_image,
    inputs = gr.Image(type="pil"),     # Accepts an image as input
    outputs = [
        gr.Label(label="Prediction"),  # Class name and confidence score as a labeled output
    ],
    title = "Image Classifier",
    description = "Upload an image, and the model will classify it into one of the predefined classes."
)

# Launch the Gradio app
interface.launch()