Create app.py

app.py

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+import streamlit as st
+import cv2
+import tempfile
+import numpy as np
+from ultralytics import YOLO, solutions
+
+# Load the YOLOv8 model
+model = YOLO("yolov8n-pose.pt")
+
+# Streamlit App
+st.title("Workout Monitoring App")
+st.write("Upload a video to monitor your ab workout.")
+
+uploaded_file = st.file_uploader("Choose a video file", type=["mp4", "mov", "avi"])
+
+if uploaded_file is not None:
+    # Save the uploaded video to a temporary file
+    tfile = tempfile.NamedTemporaryFile(delete=False)
+    tfile.write(uploaded_file.read())
+    tfile.close()
+
+    # Load the video with OpenCV
+    cap = cv2.VideoCapture(tfile.name)
+    assert cap.isOpened(), "Error reading video file"
+    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
+
+    # Initialize AIGym object
+    gym_object = solutions.AIGym(
+        line_thickness=2,
+        view_img=False,  # Set to False since we are using Streamlit to display
+        pose_type="abworkout",  # Use 'abworkout' as the pose type
+        kpts_to_check=[6, 8, 10],
+    )
+
+    # List to store processed frames
+    processed_frames = []
+
+    # Process the video frame by frame
+    st.write("Analyzing video... Please wait.")
+    while cap.isOpened():
+        success, im0 = cap.read()
+        if not success:
+            break
+
+        results = model.track(im0, verbose=False)  # Tracking recommended
+        im0 = gym_object.start_counting(im0, results)
+
+        # Append the processed frame to the list
+        processed_frames.append(im0)
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+    # Create a temporary file to save the processed video
+    output_video_file = tempfile.NamedTemporaryFile(delete=False, suffix='.avi')
+    output_video_path = output_video_file.name
+
+    # Write the processed frames to a video file
+    video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+    for frame in processed_frames:
+        video_writer.write(frame)
+    video_writer.release()
+
+    # Display the processed video in Streamlit
+    st.write("Analysis complete. Displaying processed video:")
+    st.video(output_video_path)
+
+    # Provide a download link for the processed video
+    st.write("Download the processed video:")
+    with open(output_video_path, "rb") as video_file:
+        st.download_button("Download", video_file, "workouts.avi")