Added file

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+# Load the YOLO model
+model = YOLO('yolov5s.pt')  # You can use a different model if needed
+
+def count_people(video_file):
+    count = 0
+    cap = cv2.VideoCapture(video_file)
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        results = model(frame)
+        detections = results.pred[0]  # Get predictions
+
+        # Count people detected (class ID for person is usually 0)
+        for det in detections:
+            if det[5] == 0:  # Check if class ID is 0 (person)
+                count += 1
+
+    cap.release()
+    return count
+
+# Streamlit app layout
+st.title("Person Detection in Video")
+st.write("Upload a video file to count the number of times a person appears.")
+
+# File uploader for video files
+video_file = st.file_uploader("Choose a video file", type=["mp4", "avi", "mov"])
+
+if video_file is not None:
+    # Save the uploaded video to a temporary location
+    with open("temp_video.mp4", "wb") as f:
+        f.write(video_file.getbuffer())
+
+    st.video(video_file)  # Display the video
+
+    if st.button("Count People"):
+        with st.spinner("Counting..."):
+            count = count_people("temp_video.mp4")
+        st.success(f"Total number of people detected: {count}")