update app.py

app-old.txt

@@ -0,0 +1,128 @@
+
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import transforms
+from PIL import Image
+import streamlit as st
+import numpy as np
+import requests
+from io import BytesIO
+from kan_linear import KANLinear
+
+class CNNKAN(nn.Module):
+    def __init__(self):
+        super(CNNKAN, self).__init__()
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
+        self.bn1 = nn.BatchNorm2d(32)
+        self.pool1 = nn.MaxPool2d(2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.bn2 = nn.BatchNorm2d(64)
+        self.pool2 = nn.MaxPool2d(2)
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
+        self.bn3 = nn.BatchNorm2d(128)
+        self.pool3 = nn.MaxPool2d(2)
+        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
+        self.bn4 = nn.BatchNorm2d(256)
+        self.pool4 = nn.MaxPool2d(2)
+        self.dropout = nn.Dropout(0.5)
+        self.kan1 = KANLinear(256 * 12 * 12, 512)
+        self.kan2 = KANLinear(512, 1)
+
+    def forward(self, x):
+        x = F.selu(self.bn1(self.conv1(x)))
+        x = self.pool1(x)
+        x = F.selu(self.bn2(self.conv2(x)))
+        x = self.pool2(x)
+        x = F.selu(self.bn3(self.conv3(x)))
+        x = self.pool3(x)
+        x = F.selu(self.bn4(self.conv4(x)))
+        x = self.pool4(x)
+        x = x.view(x.size(0), -1)
+        x = self.dropout(x)
+        x = self.kan1(x)
+        x = self.dropout(x)
+        x = self.kan2(x)
+        return x
+
+def load_model(weights_path, device):
+    model = CNNKAN().to(device)
+    state_dict = torch.load(weights_path, map_location=device)
+    
+    # Remove 'module.' prefix from keys
+    from collections import OrderedDict
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        if k.startswith('module.'):
+            new_state_dict[k[7:]] = v
+        else:
+            new_state_dict[k] = v
+
+    model.load_state_dict(new_state_dict)
+    model.eval()
+    return model
+
+def load_image_from_url(url):
+    response = requests.get(url)
+    img = Image.open(BytesIO(response.content)).convert('RGB')
+    return img
+
+def preprocess_image(image):
+    transform = transforms.Compose([
+        transforms.Resize((200, 200)),
+        transforms.ToTensor()
+    ])
+    return transform(image).unsqueeze(0)
+
+# Streamlit app
+st.title("Cat and Dog Classification with CNN-KAN")
+
+st.sidebar.title("Upload Images")
+uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png", "webp"])
+image_url = st.sidebar.text_input("Or enter image URL...")
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = load_model('weights/best_model_weights_KAN.pth', device)
+
+img = None
+
+if uploaded_file is not None:
+    img = Image.open(uploaded_file).convert('RGB')
+elif image_url:
+    try:
+        img = load_image_from_url(image_url)
+    except Exception as e:
+        st.sidebar.error(f"Error loading image from URL: {e}")
+
+st.sidebar.write("-----")
+
+# Define your information for the footer
+name = "Wayan Dadang"
+
+st.sidebar.write("Follow me on:")
+# Create a footer section with links and copyright information
+st.sidebar.markdown(f"""
+    [LinkedIn](https://www.linkedin.com/in/wayan-dadang-801757116/)
+    [GitHub](https://github.com/Wayan123)
+    [Resume](https://wayan123.github.io/)
+    © {name} - {2024}
+    """, unsafe_allow_html=True)
+
+if img is not None:
+    st.image(np.array(img), caption='Uploaded Image.', use_column_width=True)
+    if st.button('Predict'):
+        img_tensor = preprocess_image(img).to(device)
+
+        with torch.no_grad():
+            output = model(img_tensor)
+            prob = torch.sigmoid(output).item()
+
+        st.write(f"Prediction: {prob:.4f}")
+
+        if prob < 0.5:
+            st.write("This image is classified as a Cat.")
+        else:
+            st.write("This image is classified as a Dog")
+

app.py

@@ -107,7 +107,7 @@ def load_image_from_url(url):
 
 def preprocess_image(image):
     transform = transforms.Compose([
-        transforms.Resize((224, 224)),
+        transforms.Resize((200, 200)),
         transforms.ToTensor()
     ])
     return transform(image).unsqueeze(0)