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# -*- coding: utf-8 -*-
"""Untitled1.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1AAiRPNd-Nnhg1OZNqQdo0_vdvVyOqala
"""
import streamlit as st
from tensorflow.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
# 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()
def predict_image(image_path):
# Create the array of the right shape to feed into the keras model
# The 'length' or number of images you can put into the array is
# determined by the first position in the shape tuple, in this case 1
data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
# Replace this with the path to your image
image = Image.open(image_path).convert("RGB")
# 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]
return class_name[2:], confidence_score
st.title("Image Classification App")
st.write("Upload an image and the app will predict its class.")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image.', use_column_width=True)
class_name, confidence_score = predict_image(uploaded_file)
st.write("Class:", class_name)
st.write("Confidence Score:", confidence_score) |