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#import library
import pandas as pd
import numpy as np
import streamlit as st
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import matplotlib.pyplot as plt
from PIL import Image
import tensorflow as tf
from tensorflow.keras.models import load_model
#import pickle
import pickle
#load model
def run():
file = st.file_uploader("Upload an image", type=["jpg", "png"])
model = tf.keras.models.load_model('model_ann_sequential_improve.h5')
target_size=(50, 50)
def import_and_predict(image_data, model):
image = tf.keras.utils.load_img(image_data, target_size=(50, 50))
x = tf.keras.utils.img_to_array(image)
x = np.expand_dims(x, axis=0)
plt.imshow(image)
plt.axis('off')
plt.show()
# Make prediction
classes = model.predict(x)
result_pred = np.argmax(classes, axis=1)
labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J',
'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',
'U', 'V', 'W', 'X', 'Y', 'Z', '_']
predicted_class = labels[result_pred[0]] # Get the predicted class
return f"Prediction: {predicted_class}"
if file is None:
st.text("Please upload an image file")
else:
prediction = import_and_predict(file, model)
st.image(file)
st.write(prediction, font="Arial", size=50) # Increase text size
if __name__ == "__main__":
run()
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