.

.gitignore

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+venv

app.py

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+import streamlit as st
+from ultralytics import YOLO
+import tempfile
+
+# Load the model
+model = YOLO('best.pt')
+
+st.title('YOLO Object Detection')
+
+uploaded_file = st.file_uploader("Upload an image", type=['jpg', 'jpeg', 'png'])
+
+if uploaded_file is not None:
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
+        tmp_file.write(uploaded_file.getvalue())
+        uploaded_image_path = tmp_file.name
+
+    # Display the uploaded image
+    st.image(uploaded_image_path, caption='Uploaded Image', use_column_width=True)
+
+    # Perform inference and save the result
+    results = model(uploaded_image_path)
+    saved_image_path = 'predicted_image.jpg'  # Define the path where the image will be saved
+    for result in results:
+        result.save(filename=saved_image_path)  # Save the predicted image
+
+    # Display the image with predictions
+    st.image(saved_image_path, caption='Predicted Image', use_column_width=True)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9184ee1d77314b5ee0ca76ad74878743ea9266e1667e11a8972825f052262efc
+size 6232793

requirements.txt

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+streamlit
+ultralytics
+dill