import os import streamlit as st import tensorflow as tf import numpy as np from huggingface_hub import HfApi, hf_hub_download from PIL import Image from io import BytesIO import requests # Hugging Face credentials api = HfApi() # Set your Hugging Face username and model repository name username = "Hammad712" repo_name = "CycleGAN-Model" repo_id = f"{username}/{repo_name}" # Download model files from Hugging Face local_dir = "CycleGAN" # Changed to a relative path os.makedirs(local_dir, exist_ok=True) for file in api.list_repo_files(repo_id=repo_id, repo_type="model"): hf_hub_download(repo_id=repo_id, filename=file, local_dir=local_dir) # Load the model custom_objects = {'InstanceNormalization': tf.keras.layers.Layer} # Adjust custom objects as needed loaded_model = tf.keras.models.load_model(local_dir, custom_objects=custom_objects) # Helper functions def load_and_preprocess_image(image): img = image.resize((256, 256)) img = np.array(img) img = (img - 127.5) / 127.5 # Normalize to [-1, 1] img = np.expand_dims(img, axis=0) # Add batch dimension return img def infer_image(model, image): preprocessed_img = load_and_preprocess_image(image) generated_img = model(preprocessed_img, training=False) generated_img = tf.squeeze(generated_img, axis=0) # Remove batch dimension generated_img = (generated_img * 127.5 + 127.5).numpy().astype(np.uint8) # De-normalize to [0, 255] return generated_img def load_image_from_url(url): response = requests.get(url) img = Image.open(BytesIO(response.content)) return img # Custom CSS combined_css = """ .main, .sidebar .sidebar-content { background-color: #1c1c1c; color: #f0f2f6; } .block-container { padding: 1rem 2rem; background-color: #333; border-radius: 10px; box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.5); } .stButton>button, .stDownloadButton>button { background: linear-gradient(135deg, #ff7e5f, #feb47b); color: white; border: none; padding: 10px 24px; text-align: center; text-decoration: none; display: inline-block; font-size: 16px; margin: 4px 2px; cursor: pointer; border-radius: 5px; } .stSpinner { color: #4CAF50; } .title { font-size: 3rem; font-weight: bold; display: flex; align-items: center; justify-content: center; } .colorful-text { background: -webkit-linear-gradient(135deg, #ff7e5f, #feb47b); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .black-white-text { color: black; } .small-input .stTextInput>div>input { height: 2rem; font-size: 0.9rem; } .small-file-uploader .stFileUploader>div>div { height: 2rem; font-size: 0.9rem; } .custom-text { font-size: 1.2rem; color: #feb47b; text-align: center; margin-top: -20px; margin-bottom: 20px; } """ # Streamlit application st.set_page_config(layout="wide") st.markdown(f"", unsafe_allow_html=True) st.markdown('