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app.py

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+import streamlit as st
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+import tempfile
+import os
+
+# Load the model from local path
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')
+
+def detect_fall(image):
+    results = model(image)
+    return results
+
+def main():
+    st.title("Fall Detection Application")
+
+    option = st.sidebar.selectbox("Choose input type", ("Upload Image/Video", "Use Camera"))
+
+    if option == "Upload Image/Video":
+        uploaded_file = st.file_uploader("Upload Image or Video", type=['jpg', 'jpeg', 'png', 'mp4', 'avi', 'mov'])
+        if uploaded_file is not None:
+            if uploaded_file.type.startswith('image'):
+                file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+                image = cv2.imdecode(file_bytes, 1)
+                st.image(image, caption='Uploaded Image.', use_column_width=True)
+                results = detect_fall(image)
+                st.image(np.squeeze(results.render()), caption='Processed Image.', use_column_width=True)
+            elif uploaded_file.type.startswith('video'):
+                tfile = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
+                tfile.write(uploaded_file.read())
+                tfile.close()
+                st.write(f'Temporary file path: {tfile.name}')
+                vidcap = cv2.VideoCapture(tfile.name)
+                stframe = st.empty()
+                
+                while vidcap.isOpened():
+                    success, frame = vidcap.read()
+                    if not success:
+                        break
+                    results = detect_fall(frame)
+                    processed_frame = np.squeeze(results.render())
+                    stframe.image(processed_frame, channels="BGR")
+                vidcap.release()
+                os.remove(tfile.name)
+
+    elif option == "Use Camera":
+        stframe = st.empty()
+        cap = cv2.VideoCapture(0)
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            results = detect_fall(frame)
+            processed_frame = np.squeeze(results.render())
+            stframe.image(processed_frame, channels="BGR")
+        cap.release()
+
+if __name__ == '__main__':
+    main()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2c9ca74475ce6228480fa8adf90d09c808e858cc54ff6c5a752b5bb294f912dc
+size 14465461

requirements.txt

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+streamlit
+torch
+opencv-python-headless
+numpy
+pillow