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()