added UI using streamlit

helpers.py

@@ -16,7 +16,7 @@
 def generate_image(image_path:str):
     input_image = np.array(Image.open(image_path))
     H,W,C = input_image.shape
-    if H or W >512:
+    if (H or W) >=512:
         img = config.custom_transform(image=input_image)['image']
     else:
         img = config.test_transform(image=input_image)['image']

streamlit_app.py

@@ -0,0 +1,58 @@
+from streamlit import cli as stcli
+import streamlit
+from requests_toolbelt.multipart.encoder import MultipartEncoder
+import requests
+from PIL import Image
+import io
+import sys
+
+def main():
+    streamlit.title('Enhanced Super Resolution GAN')
+
+    # fastapi endpoint
+    url = 'http://127.0.0.1:8000'
+    endpoint = '/uploadfile/'
+    col_1, col_2 = streamlit.beta_columns(2)
+    col_1.image("https://pytorch.org/assets/images/pytorch-logo.png", use_column_width=True)
+    col_2.image("https://images4.programmersought.com/878/c8/c8b175f9d26f422afd56a6a20285302e.png", use_column_width=True)
+    streamlit.write('''ESRGAN model is implemented in PyTorch.
+            This streamlit example uses a FastAPI service as backend.
+            Visit this URL at `:8000/docs` for FastAPI documentation.''') # description and instructions
+
+    image = streamlit.file_uploader('insert image')  # image upload widget
+
+    @streamlit.cache
+    def process(image, server_url: str):
+
+        m = MultipartEncoder(
+            fields={'file': ('filename.jpg', image, 'image/jpeg')}
+            )
+
+        r = requests.post(server_url,
+                        data=m,
+                        headers={'Content-Type': m.content_type},
+                        timeout=8000)
+
+        return r
+
+
+    if streamlit.button('Generate'):
+
+        if image == None:
+            streamlit.write("Insert an image!")  # handle case with no image
+        else:
+            col1, col2 = streamlit.beta_columns(2)
+            input_image = process(image, url+endpoint)
+            generated_image = Image.open(io.BytesIO(input_image.content)).convert('RGB')
+            col1.header("Input Image")
+            col1.image(image, use_column_width=True)
+            col2.header("Output Image")
+            col2.image(generated_image, use_column_width=True)
+            # streamlit.image([image, segmented_image], width=300)
+
+if __name__ == '__main__':
+    if streamlit._is_running_with_streamlit:
+        main()
+    else:
+        sys.argv = ["streamlit", "run", sys.argv[0]]
+        sys.exit(stcli.main())