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best (1).pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b3dbe7873c29c002885d80f22a0617585be6a70766ad7585dfec5b57548e9da2
+size 52035329

sample.py

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+import streamlit as st
+from PIL import Image
+import torch
+from torchvision import transforms
+
+# Load the YOLO model
+@st.cache
+def load_model():
+    # Replace 'model.pt' with the path to your YOLO model file
+    model = torch.load('best.pt')
+    return model
+
+# Define YOLO processing function
+def process_image(image, model):
+    # Preprocess the image
+    preprocess = transforms.Compose([
+        transforms.Resize((416, 416)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    input_tensor = preprocess(image)
+    input_batch = input_tensor.unsqueeze(0)
+    
+    # Perform inference
+    with torch.no_grad():
+        output = model(input_batch)
+    
+    # Post-process the output (e.g., draw bounding boxes)
+    # Replace this with your post-processing code
+    
+    # Convert tensor to PIL Image
+    output_image = transforms.ToPILImage()(output[0].cpu().squeeze())
+
+    return output_image
+
+# Main Streamlit code
+def main():
+    st.title('YOLO Image Detection')
+    
+    # Upload image file
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_file is not None:
+        # Load YOLO model
+        model = load_model()
+        
+        # Process uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Original Image', use_column_width=True)
+        
+        output_image = process_image(image, model)
+        st.image(output_image, caption='Processed Image', use_column_width=True)
+
+if __name__ == '__main__':
+    main()