import streamlit as st
import cv2
import tempfile
import time
import os

class Video_View:
    def __init__(self, app, model):
        self.app = app
        self.model = model
    def toggle_video_processing(self):
        """Toggle video processing state."""
        st.session_state.video_processed = False
    def show(self):
        # Top navigation
        col1_back, col2_back = st.columns([0.2, 0.8])
        with col1_back:
            if st.button("Back", key='video_back', icon=':material/arrow_back:', type='primary'):
                self.app.change_page("Main")

        st.markdown("<h1 style='text-align: center;'>🧠 Video Detection</h1>", unsafe_allow_html=True)
        st.divider()

        uploaded_file = st.file_uploader("Upload a video", type=["mp4", "avi", "mov"],on_change=self.toggle_video_processing)
        if not st.session_state.video_processed:
            if uploaded_file is not None:
                # Save to temp file
                tfile = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
                tfile.write(uploaded_file.read())

                cap = cv2.VideoCapture(tfile.name)
                total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # Total number of frames
                width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                fps = cap.get(cv2.CAP_PROP_FPS)

                out_path = os.path.join(tempfile.gettempdir(), "predicted_video.mp4")
                out = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*'XVID'), fps, (width, height))

                frame_count = 0  # To track the number of processed frames

                with st.spinner("Processing video... ⏳"):
                    progress_bar = st.progress(0)  # Create a progress bar

                    while cap.isOpened():
                        ret, frame = cap.read()
                        if not ret:
                            break

                        # Run YOLO model on the frame
                        results = self.model(frame)[0]

                        for result in results.boxes.data.tolist():
                            x1, y1, x2, y2, score, _ = result
                            color = (0, 0, 255) if score > 0.5 else (0, 255, 0)
                            label = f"{score:.2f}"
                            cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), color, 1)
                            cv2.putText(frame, label, (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 1)

                        out.write(frame)

                        # Update the progress bar
                        frame_count += 1
                        progress_percentage = frame_count / total_frames  # Calculate the percentage
                        progress_bar.progress(progress_percentage)  # Update the progress bar

                cap.release()
                out.release()
                cv2.destroyAllWindows()
                st.session_state.video_processed = True
                st.success("✅ Detection complete!")

                # Read and display the video
                with open(out_path, 'rb') as video_file:
                    video_bytes = video_file.read()
                    st.video(uploaded_file, loop=True, autoplay=True, muted=False)
                    st.download_button("📥 Download Predicted Video", video_bytes, file_name="predicted_video.mp4",
                                       mime="video/mp4")