app.py

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
import streamlit as st

# Disable scientific notation for clarity
np.set_printoptions(suppress=True)

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

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

st.title("Cat Breed Identifier")
st.header("Upload an Image to classify")

uploaded_file = st.file_uploader("Choose the image...", type=['jpg','jpeg', 'png'])
if uploaded_file is not None:
    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
    
    # Replace this with the path to your image
    image = Image.open(uploaded_file).convert("RGB")
    st.image(image, caption="Uploaded Image...",use_column_width=True)
    
    # resizing the image to be at least 224x224 and then cropping from the center
    size = (224, 224)
    image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
    
    # turn the image into a numpy array
    image_array = np.asarray(image)
    
    # Normalize the image
    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
    
    # Load the image into the array
    data[0] = normalized_image_array
    
    # Predicts the model
    prediction = model.predict(data)
    index = np.argmax(prediction)
    class_name = class_names[index]
    confidence_score = prediction[0][index]
    
    # Print prediction and confidence score
    st.write(f"Class: {class_name[2:].strip()}")
    st.write(f"Confidence Score: {confidence_score}")