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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) |