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import streamlit as st
import cv2
import numpy as np
from PIL import Image
from transformers import pipeline
# Function to load model from Hugging Face
@st.cache(allow_output_mutation=True)
def load_model():
return pipeline("pose-detection", device=0) # Adjust device as per your requirement
# Function to detect yoga pose from image
def detect_yoga_pose(image):
# Convert PIL image to OpenCV format
cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
# Your pose detection logic here
# Replace the following line with your actual pose detection code
return "Detected yoga pose: Warrior II"
def main():
st.title("Yoga Pose Detection from Live Camera Feed")
# Load the model
model = load_model()
# Accessing the webcam
cap = cv2.VideoCapture(0)
# Run the app
while True:
ret, frame = cap.read()
# Display the webcam feed
st.image(frame, channels="BGR")
# Convert the OpenCV frame to PIL image
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pil_image = Image.fromarray(frame)
# Detect yoga pose from the image
pose = detect_yoga_pose(pil_image)
# Display the detected yoga pose
st.write("Detected Yoga Pose:", pose)
# Close the webcam feed
if st.button("Stop"):
break
# Release the webcam and close Streamlit app
cap.release()
st.stop()
if __name__ == "__main__":
main()
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