Update app.py

app.py

@@ -1,59 +1,61 @@
-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
-
-# 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
-])
-
-# Streamlit interface
-st.title("Signature Forgery Detection with Siamese Network")
-st.write("Upload two signature images to check if they are from the same person or if one is forged.")
-
-# Upload images
-image1 = st.file_uploader("Upload First Signature Image", type=["png", "jpg", "jpeg"])
-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
-    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
-    img1 = transform(img1).unsqueeze(0).to(device)
-    img2 = transform(img2).unsqueeze(0).to(device)
-
-    # Predict 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
-
-    # Display similarity 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**.")
-    else:
-        st.write("The signatures **do not match**, one might be **forged**.")
-
+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
+
+# 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
+])
+
+# Streamlit interface
+st.title("Signature Forgery Detection with Siamese Network")
+st.write("Upload two signature images to check if they are from the same person or if one is forged.")
+
+# Upload images
+image1 = st.file_uploader("Upload First Signature Image", type=["png", "jpg", "jpeg"])
+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")
+
+    # Transform 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
+    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
+
+    # Display both images and results side by side
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image(img1.squeeze(0).cpu().permute(1, 2, 0), caption='First Signature Image', use_container_width=True)
+    with col2:
+        st.image(img2.squeeze(0).cpu().permute(1, 2, 0), caption='Second Signature Image', use_container_width=True)
+
+    # Display similarity score and interpretation side by side
+    col1, col2 = st.columns(2)
+    with col1:
+        st.success(f'Similarity Score (Euclidean Distance): {euclidean_distance.item():.4f}')
+    with col2:
+        if euclidean_distance.item() < threshold:
+            st.write("The signatures are likely from the **same person**.")
+        else:
+            st.write("The signatures **do not match**, one might be **forged**.")