Add base of demo app

.streamlit/config.toml

@@ -0,0 +1,6 @@
+[theme]
+primaryColor="#a06c4b"
+backgroundColor="#110305"
+secondaryBackgroundColor="#121010"
+textColor="#a06c4b"
+font="sans serif"

README.md

@@ -177,4 +177,9 @@ To add further tests, simply add a new file in the `tests` folder, and name it `
 *Coming soon.*
 
 ## Running the web application
-*Coming soon.*
+To start the inference web application, run the following command from the root directory of the project:
+```bash
+$ streamlit run app/app.py
+```
+The web application should then be accessible at `localhost:8501` in your browser and allow you to upload images in any size and format, enhance them and download the enhanced version:
+![Screenshot from the demonstration application](utils/documentation_images/demo_app.png)

app/app.py

@@ -0,0 +1,125 @@
+import streamlit as st
+from PIL import Image
+import io
+import sys, os
+import torch
+import torchvision.transforms as T
+import torchvision.utils as vutils
+import base64
+
+sys.path.append(".")
+from model.MIRNet.model import MIRNet
+
+
+def run_model(input_image):
+    device = (
+        torch.device("cuda")
+        if torch.cuda.is_available()
+        else torch.device("mps")
+        if torch.backends.mps.is_available()
+        else torch.device("cpu")
+    )
+    print(f"-> {device.type} device detected.")
+
+    model = MIRNet(num_features=64).to(device)
+    checkpoint = torch.load(
+        "model/weights/Mirnet_enhance_finetune-35-early-stopped_64x64.pth",
+        map_location=device,
+    )
+    model.load_state_dict(checkpoint["model_state_dict"])
+
+    model.eval()
+    with torch.no_grad():
+        img = input_image
+        img_tensor = T.Compose(
+            [
+                T.Resize(400),
+                T.ToTensor(),
+                T.Normalize([0.0, 0.0, 0.0], [1.0, 1.0, 1.0]),
+            ]
+        )(img).unsqueeze(0)
+        img_tensor = img_tensor.to(device)
+
+        if img_tensor.shape[2] % 8 != 0:
+            img_tensor = img_tensor[:, :, : -(img_tensor.shape[2] % 8), :]
+        if img_tensor.shape[3] % 8 != 0:
+            img_tensor = img_tensor[:, :, :, : -(img_tensor.shape[3] % 8)]
+
+        output = model(img_tensor)
+
+        vutils.save_image(output, open(f"temp.png", "wb"))
+        output_image = Image.open("temp.png")
+        os.remove("temp.png")
+        return output_image
+
+
+def get_base64_font(font_path):
+    with open(font_path, "rb") as font_file:
+        return base64.b64encode(font_file.read()).decode()
+
+
+st.set_page_config(layout="wide")
+
+font_name = "Gloock"
+gloock_b64 = get_base64_font("utils/assets/Gloock-Regular.ttf")
+font_name_text = "Merriweather sans"
+merri_b64 = get_base64_font("utils/assets/MerriweatherSans-Regular.ttf")
+hide_streamlit_style = f"""
+            <style>
+            #MainMenu {'{visibility: hidden;}'}
+            footer {'{visibility: hidden;}'}
+            
+            @font-face {{
+                font-family: '{font_name}';
+                src: url(data:font/ttf;base64,{gloock_b64}) format('truetype');
+            }}
+            @font-face {{
+                font-family: '{font_name_text}';
+                src: url(data:font/ttf;base64,{merri_b64}) format('truetype');
+            }}
+            span {{
+                font-family: '{font_name_text}';  
+            }}
+            .e1nzilvr1, .st-emotion-cache-10trblm {{
+                font-family: '{font_name}';
+                font-size: 65px;
+            }}
+            
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+st.title("Low-light event-image enhancement with MIRNet.")
+
+# File uploader widget
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+if uploaded_file is not None:
+    # To read file as bytes:
+    bytes_data = uploaded_file.getvalue()
+    image = Image.open(io.BytesIO(bytes_data)).convert("RGB")
+
+    # Create two columns for images
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.image(image, caption="Original Image", use_column_width="always")
+
+        # Button to enhance image
+        if st.button("Enhance Image"):
+            with col2:
+                # Assume your model has a function 'enhance' to enhance the image
+                enhanced_image = run_model(image)
+                st.image(
+                    enhanced_image, caption="Enhanced Image", use_column_width="always"
+                )
+
+                # Download button
+                buf = io.BytesIO()
+                enhanced_image.save(buf, format="JPEG")
+                byte_im = buf.getvalue()
+                st.download_button(
+                    label="Download image",
+                    data=byte_im,
+                    file_name="enhanced_image.jpg",
+                    mime="image/jpeg",
+                )

requirements.txt

@@ -7,3 +7,4 @@ wandb
 pyyaml
 tqdm
 pytest
+streamlit