Update app.py

app.py

@@ -2,21 +2,21 @@ import streamlit as st
 import torch
 from PIL import Image
 import torchvision.transforms as transforms
-from model import SiameseNetwork  # Ensure this file exists with the model definition
+from model import SiameseNetwork 
+
 
-# Define the device (GPU or CPU)
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-# Load the pre-trained Siamese model
+
 model = SiameseNetwork().to(device)
 model.load_state_dict(torch.load("siamese_model.pth", map_location=device))
 model.eval()
 
-# Define data transformation (resize, convert to tensor, normalize if needed)
+
 transform = transforms.Compose([
-    transforms.Resize((100, 100)),  # Resize to match the input size of the model
-    transforms.Grayscale(num_output_channels=1),  # Convert images to grayscale for signature comparison
-    transforms.ToTensor(),  # Convert image to tensor
+    transforms.Resize((100, 100)), 
+    transforms.Grayscale(num_output_channels=1),  
+    transforms.ToTensor(),  # Converting image to tensor
 ])
 
 # Streamlit interface
@@ -28,29 +28,29 @@ image1 = st.file_uploader("Upload First Signature Image", type=["png", "jpg", "j
 image2 = st.file_uploader("Upload Second Signature Image", type=["png", "jpg", "jpeg"])
 
 if image1 and image2:
-    # Load and transform the images
+    
     img1 = Image.open(image1).convert("RGB")
     img2 = Image.open(image2).convert("RGB")
 
-    # Display images
+    ## Displaying input image
     col1, col2 = st.columns(2)
     with col1:
         st.image(img1, caption='First Signature Image', use_container_width=True)
     with col2:
         st.image(img2, caption='Second Signature Image', use_container_width=True)
 
-    # Transform the images before feeding them into the model
+    # Transforming the images before feeding them into the model
     img1 = transform(img1).unsqueeze(0).to(device)
     img2 = transform(img2).unsqueeze(0).to(device)
 
-    # Predict similarity using the Siamese model
+    # Predicting similarity using the Siamese model
     output1, output2 = model(img1, img2)
     euclidean_distance = torch.nn.functional.pairwise_distance(output1, output2)
 
-    # Set a threshold for similarity (can be tuned based on model performance)
-    threshold = 0.5  # You can adjust this threshold based on your model's performance
+    # Setting a threshold for similarity 
+    threshold = 0.5 
 
-    # Display similarity score and interpretation
+    # Display similaritying score and interpretation
     st.success(f'Similarity Score (Euclidean Distance): {euclidean_distance.item():.4f}')
     if euclidean_distance.item() < threshold:
         st.write("The signatures are likely from the **same person**.")