Code improvements.

app.py

@@ -1,6 +1,7 @@
 import os
 from io import BytesIO
 import requests
+from datetime import datetime
 
 # Interface utilities
 import gradio as gr
@@ -40,6 +41,8 @@ photo_ids = pd.read_csv("unsplash-dataset/photo_ids.csv")
 photo_ids = list(photo_ids["photo_id"])
 
 def image_from_text(text_input):
+    start=datetime.now()
+
     ## Inference
     with torch.no_grad():
         inputs = tokenizer([text_input],  padding=True, return_tensors="pt")
@@ -53,6 +56,9 @@ def image_from_text(text_input):
     photo_id = photo_ids[idx]
     photo_data = photos[photos["photo_id"] == photo_id].iloc[0]
 
+    print(f"Time spent at CLIP: {datetime.now()-start}")
+
+    start=datetime.now()
     # Downlaod image
     response = requests.get(photo_data["photo_image_url"] + "?w=640")
     pil_image = Image.open(BytesIO(response.content)).convert("RGB") 
@@ -60,32 +66,40 @@ def image_from_text(text_input):
     # Convert RGB to BGR 
     open_cv_image = open_cv_image[:, :, ::-1].copy() 
 
+    print(f"Time spent at Image request: {datetime.now()-start}")
+
     return open_cv_image
 
 def inference(content, style):
+    content_image = image_from_text(content)
+    start=datetime.now()
+
     result = stylepro_artistic.style_transfer(
         images=[{
-            "content": image_from_text(content),
+            "content": content_image,
             "styles": [cv2.imread(style.name)]
         }])
+
+    print(f"Time spent at Style Transfer: {datetime.now()-start}")
     return Image.fromarray(np.uint8(result[0]["data"])[:,:,::-1]).convert("RGB")
-    
-title = "Neural Style Transfer"
-description = "Gradio demo for Neural Style Transfer. To use it, simply enter the text for image content and upload style image. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2003.07694'target='_blank'>Parameter-Free Style Projection for Arbitrary Style Transfer</a> | <a href='https://github.com/PaddlePaddle/PaddleHub' target='_blank'>Github Repo</a></br><a href='https://arxiv.org/abs/2103.00020'target='_blank'>Clip paper</a> | <a href='https://huggingface.co/transformers/model_doc/clip.html' target='_blank'>Hugging Face Clip Implementation</a></p>"
-examples=[
-        ["a cute kangaroo", "styles/starry.jpeg"],
-        ["man holding beer", "styles/mona1.jpeg"],
-    ]
-interface = gr.Interface(inference, 
-    inputs=[
-        gr.inputs.Textbox(lines=1, placeholder="Describe the content of the image", default="a cute kangaroo", label="Describe the image to which the style will be applied"),
-        gr.inputs.Image(type="file", label="Style to be applied"),
-    ], 
-    outputs=gr.outputs.Image(type="pil"),
-    enable_queue=True,
-    title=title,
-    description=description,
-    article=article,
-    examples=examples)
-interface.launch()
+
+if __name__ == "__main__": 
+    title = "Neural Style Transfer"
+    description = "Gradio demo for Neural Style Transfer. To use it, simply enter the text for image content and upload style image. Read more at the links below."
+    article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2003.07694'target='_blank'>Parameter-Free Style Projection for Arbitrary Style Transfer</a> | <a href='https://github.com/PaddlePaddle/PaddleHub' target='_blank'>Github Repo</a></br><a href='https://arxiv.org/abs/2103.00020'target='_blank'>Clip paper</a> | <a href='https://huggingface.co/transformers/model_doc/clip.html' target='_blank'>Hugging Face Clip Implementation</a></p>"
+    examples=[
+            ["a cute kangaroo", "styles/starry.jpeg"],
+            ["man holding beer", "styles/mona1.jpeg"],
+        ]
+    interface = gr.Interface(inference, 
+        inputs=[
+            gr.inputs.Textbox(lines=1, placeholder="Describe the content of the image", default="a cute kangaroo", label="Describe the image to which the style will be applied"),
+            gr.inputs.Image(type="file", label="Style to be applied"),
+        ], 
+        outputs=gr.outputs.Image(type="pil"),
+        enable_queue=True,
+        title=title,
+        description=description,
+        article=article,
+        examples=examples)
+    interface.launch()