Upload app.py

app.py

@@ -0,0 +1,69 @@
+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)