app.py

import streamlit as st
from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np


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


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


def predict(image):
    
    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
    # Preprocess 
    image = ImageOps.fit(image, (224, 224), Image.LANCZOS)
    image_array = np.asarray(image)
    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
    data[0] = normalized_image_array
    # Make prediction
    prediction = model.predict(data)
    index = np.argmax(prediction)
    class_name = class_names[index].strip()
    confidence_score = prediction[0][index]
    return class_name, confidence_score


st.title("Image Classification")

uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    image = Image.open(uploaded_file).convert("RGB")
    st.image(image, caption="Uploaded Image", use_column_width=True)
    class_name, confidence_score = predict(image)
    st.write("Class:", class_name)
    st.write("Confidence Score:", confidence_score)