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| import streamlit as st | |
| from fastai.vision import open_image, load_learner, show_image | |
| import PIL.Image | |
| from PIL import Image | |
| from io import BytesIO | |
| import requests | |
| import torch.nn as nn | |
| import os | |
| import tempfile | |
| import shutil | |
| # Define the FeatureLoss class | |
| class FeatureLoss(nn.Module): | |
| def __init__(self, m_feat, layer_ids, layer_wgts): | |
| super().__init__() | |
| self.m_feat = m_feat | |
| self.loss_features = [self.m_feat[i] for i in layer_ids] | |
| self.hooks = hook_outputs(self.loss_features, detach=False) | |
| self.wgts = layer_wgts | |
| self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))] + [f'gram_{i}' for i in range(len(layer_ids))] | |
| def make_features(self, x, clone=False): | |
| self.m_feat(x) | |
| return [(o.clone() if clone else o) for o in self.hooks.stored] | |
| def forward(self, input, target): | |
| out_feat = self.make_features(target, clone=True) | |
| in_feat = self.make_features(input) | |
| self.feat_losses = [base_loss(input, target)] | |
| self.feat_losses += [base_loss(f_in, f_out) * w for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)] | |
| self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out)) * w**2 * 5e3 for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)] | |
| self.metrics = dict(zip(self.metric_names, self.feat_losses)) | |
| return sum(self.feat_losses) | |
| def __del__(self): self.hooks.remove() | |
| def add_margin(pil_img, top, right, bottom, left, color): | |
| width, height = pil_img.size | |
| new_width = width + right + left | |
| new_height = height + top + bottom | |
| result = Image.new(pil_img.mode, (new_width, new_height), color) | |
| result.paste(pil_img, (left, top)) | |
| return result | |
| def inference(image_path_or_url, learn): | |
| if image_path_or_url.startswith('http://') or image_path_or_url.startswith('https://'): | |
| response = requests.get(image_path_or_url) | |
| img = PIL.Image.open(BytesIO(response.content)).convert("RGB") | |
| else: | |
| img = PIL.Image.open(image_path_or_url).convert("RGB") | |
| im_new = add_margin(img, 250, 250, 250, 250, (255, 255, 255)) | |
| im_new.save("test.jpg", quality=95) | |
| img = open_image("test.jpg") | |
| p, img_hr, b = learn.predict(img) | |
| return img_hr | |
| # Streamlit application | |
| st.title("Image Inference with Fastai") | |
| # Download the model file from the Hugging Face repository | |
| model_url = "https://huggingface.co/Hammad712/image2sketch/resolve/main/image2sketch.pkl" | |
| model_file_path = 'image2sketch.pkl' | |
| if not os.path.exists(model_file_path): | |
| with st.spinner('Downloading model...'): | |
| response = requests.get(model_url) | |
| with open(model_file_path, 'wb') as f: | |
| f.write(response.content) | |
| st.success('Model downloaded successfully!') | |
| # Create a temporary directory for the model | |
| with tempfile.TemporaryDirectory() as tmpdirname: | |
| shutil.move(model_file_path, os.path.join(tmpdirname, 'export.pkl')) | |
| learn = load_learner(tmpdirname) | |
| # Input for image URL or path | |
| image_path_or_url = st.text_input("Enter image path or URL", "") | |
| # Run inference button | |
| if st.button("Run Inference"): | |
| if image_path_or_url: | |
| with st.spinner('Processing...'): | |
| high_res_image = inference(image_path_or_url, learn) | |
| st.image(high_res_image, caption='High Resolution Image', use_column_width=True) | |
| else: | |
| st.error("Please enter a valid image path or URL.") | |