Narendra Modi - Text Speeches Analysis

AirBus Ship Detection/Webapp/app.py

@@ -1,45 +1,45 @@
-# Save this script as 'app.py'
-
-import streamlit as st
-import torch
-import torchvision.transforms as transforms
-from PIL import Image
-import numpy as np
-
-# Function to load the pre-trained model
-@st.cache(allow_output_mutation=True)
-def load_model():
-    model = torch.load(r'Model\RNS_model.pkl', map_location=torch.device('cuda'))
-    model.eval()
-    return model
-
-# Streamlit app
-def main():
-    st.title("AirBus Ship detection with UNet Segmentation Model")
-    st.text("Upload an image for segmentation")
-
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
-
-    if uploaded_file is not None:
-        image = Image.open(uploaded_file).convert("L")
-        st.image(image, caption='Uploaded Image.', use_column_width=True)
-
-        # Perform inference
-        transform = transforms.Compose([
-            transforms.Resize((256, 256)),
-            transforms.ToTensor(),
-        ])
-        image_tensor = transform(image).unsqueeze(0).to('cuda' if torch.cuda.is_available() else 'cpu')
-
-        model = load_model()
-        with torch.no_grad():
-            output = model(image_tensor)
-
-        # Display segmentation result (assuming binary output)
-        mask = output.squeeze().cpu().numpy()
-        mask = (mask > 0.5).astype(np.uint8)  # Threshold to create binary mask
-
-        st.image(mask * 255, caption='Segmentation Mask', use_column_width=True)
-
-if __name__ == '__main__':
-    main()
+# Save this script as 'app.py'
+
+import streamlit as st
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import numpy as np
+
+# Function to load the pre-trained model
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model = torch.load(r'Model\RNS_model.pkl', map_location=torch.device('cuda'))
+    model.eval()
+    return model
+
+# Streamlit app
+def main():
+    st.title("AirBus Ship detection with UNet Segmentation Model")
+    st.text("Upload an image for segmentation")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file).convert("L")
+        st.image(image, caption='Uploaded Image.', use_column_width=True)
+
+        # Perform inference
+        transform = transforms.Compose([
+            transforms.Resize((256, 256)),
+            transforms.ToTensor(),
+        ])
+        image_tensor = transform(image).unsqueeze(0).to('cuda' if torch.cuda.is_available() else 'cpu')
+
+        model = load_model()
+        with torch.no_grad():
+            output = model(image_tensor)
+
+        # Display segmentation result (assuming binary output)
+        mask = output.squeeze().cpu().numpy()
+        mask = (mask > 0.5).astype(np.uint8)  # Threshold to create binary mask
+
+        st.image(mask * 255, caption='Segmentation Mask', use_column_width=True)
+
+if __name__ == '__main__':
+    main()