Create app.py

app.py

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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()