Create app.py

app.py

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+import streamlit as st
+from keras.models import load_model
+import numpy as np
+from PIL import Image
+from matplotlib import pyplot as plt
+import requests
+
+
+# Load your trained model
+model = load_model('Demarker_v1.h5')
+
+# Load and preprocess your custom image
+def load_and_preprocess_image(image_path, target_shape=(256, 256)):
+    # Open the image using PIL
+    custom_image = Image.open(image_path)
+    
+    # Resize the image to the desired shape
+    custom_image = custom_image.resize(target_shape)
+    
+    # Convert the image to an array
+    custom_image = np.array(custom_image)
+    
+    # Assuming the input image is in RGB format
+    # Check if the image needs normalization (pixel values in [0, 255] range)
+    if custom_image.max() > 1.0:
+        # Normalize the pixel values to the range [-1, 1]
+        custom_image = (custom_image.astype(np.float32) - 127.5) / 127.50
+    
+    # Add a batch dimension
+    custom_image = np.expand_dims(custom_image, axis=0)
+    
+    return custom_image
+
+
+# Plot source and generated images
+def plot_images(src_img, gen_img):
+    images = np.vstack((src_img, gen_img))
+    # Scale from [-1,1] to [0,1]
+    images = (images + 1) / 2.0
+    titles = ['Original', 'Generated']
+    # Plot images row by row
+    for i in range(len(images)):
+        # Define subplot
+        plt.subplot(1, 2, 1 + i)
+        # Turn off axis
+        plt.axis('off')
+        # Plot raw pixel data
+        plt.imshow(images[i])
+        # Show title
+        plt.title(titles[i])
+    st.pyplot()
+
+# Streamlit app
+st.title("GAN Image Generator")
+
+# Upload custom image
+custom_image = st.file_uploader("Upload a custom image", type=["png", "jpg", "jpeg"])
+
+# Check if an image is uploaded
+if custom_image:
+    # Generate an image from your custom source
+    gen_image = model.predict(load_and_preprocess_image(custom_image))
+
+    # Plot both images together
+    plot_images(load_and_preprocess_image(custom_image), gen_image)