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import streamlit as st
import numpy as np
from PIL import Image
import os
import cv2
import random
import numpy as np
from glob import glob
from PIL import Image, ImageOps
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
def plot_results(images, titles, figure_size=(12, 12)):
fig = plt.figure(figsize=figure_size)
for i in range(len(images)):
fig.add_subplot(1, len(images), i + 1).set_title(titles[i])
_ = plt.imshow(images[i])
plt.axis("off")
plt.show()
def infer(original_image):
image = keras.utils.img_to_array(original_image)
image = image.astype("float32") / 255.0
image = np.expand_dims(image, axis=0)
output = model.predict(image)
output_image = output[0] * 255.0
output_image = output_image.clip(0, 255)
output_image = output_image.reshape(
(np.shape(output_image)[0], np.shape(output_image)[1], 3)
)
output_image = Image.fromarray(np.uint8(output_image))
original_image = Image.fromarray(np.uint8(original_image))
return output_image
# Mock model for image prediction (replace this with your actual model)
def predict_image(img):
original_image = Image.open(uploaded_image)
enhanced_image = infer(original_image)
plot_results(
[original_image, enhanced_image],
["Original", "MIRNet Enhanced"],
(20, 12),
)
# Streamlit UI
st.title("Image Prediction App")
# Upload image window
uploaded_image = st.file_uploader("Choose an image...", type="jpg")
if uploaded_image is not None:
# Display uploaded image
image = Image.open(uploaded_image)
st.image(image, caption="Uploaded Image.", use_column_width=True)
# Button to run prediction
if st.button("Predict"):
# Get the prediction
prediction = predict_image(image)
# Display the prediction
st.write(f"Prediction: {prediction}")
st.image(image, caption=f"Predicted as {prediction}.", use_column_width=True)
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