import streamlit as st
import cv2
import joblib
import mediapipe as mp
import numpy as np

# Load trained model and label encoder
model = joblib.load("pose_classifier.joblib")
label_encoder = joblib.load("label_encoder.joblib")

# Initialize MediaPipe Pose
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()

# Streamlit UI
st.title("Live Pose Classification")
st.write("Real-time pose detection using OpenCV and MediaPipe.")

# OpenCV Video Capture
cap = cv2.VideoCapture(0)

# Streamlit Image Display
frame_placeholder = st.empty()

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        st.warning("Failed to capture video. Check your camera.")
        break

    # Convert frame to RGB
    img_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Process frame with MediaPipe Pose
    results = pose.process(img_rgb)

    if results.pose_landmarks:
        landmarks = results.pose_landmarks.landmark
        pose_data = [j.x for j in landmarks] + [j.y for j in landmarks] + \
                    [j.z for j in landmarks] + [j.visibility for j in landmarks]

        pose_data = np.array(pose_data).reshape(1, -1)

        # Predict pose
        y_pred = model.predict(pose_data)
        predicted_label = label_encoder.inverse_transform(y_pred)[0]

        # Display predicted label
        cv2.putText(frame, f"Pose: {predicted_label}", (20, 50),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)

    # Display frame in Streamlit
    frame_placeholder.image(frame, channels="BGR")

    # Break loop if user stops execution
    if st.button("Stop Camera"):
        break

cap.release()
cv2.destroyAllWindows()