import streamlit as st
import cv2
from ultralytics import YOLO
import tempfile
import os

# Load YOLOv8 pose model
model = YOLO("yolo11x-pose.pt")

st.title("🕺 Pose Detection with YOLOv8")

# File uploader
uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Save uploaded file to a temp folder manually
    temp_dir = tempfile.gettempdir()
    file_path = os.path.join(temp_dir, uploaded_file.name)

    with open(file_path, "wb") as f:
        f.write(uploaded_file.read())

    # Run inference
    results = model(file_path)

    for r in results:
        # Get annotated frame
        annotated_frame = r.plot()

        # Convert BGR → RGB for Streamlit
        annotated_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)

        # Show output
        st.image(annotated_frame, caption="Pose Detection Result", use_container_width=True)

        # Optional: save & download
        output_path = os.path.join(temp_dir, "pose_output.jpg")
        cv2.imwrite(output_path, r.plot())
        with open(output_path, "rb") as file:
            st.download_button(
                label="Download Result",
                data=file,
                file_name="pose_detection.jpg",
                mime="image/jpeg"
            )