Create app.py

app.py

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+import streamlit as st
+from fastai.vision import open_image, show_image
+import PIL.Image
+from PIL import Image
+from io import BytesIO
+import requests
+import torch.nn as nn
+import os
+
+# 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!')
+
+# Load the trained model
+learn = load_learner(model_file_path)
+
+# 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.")