import streamlit as st
import cv2
import numpy as np
import tempfile
from typing import Optional, Tuple
from datetime import datetime
from PIL import Image

from pose_detector import PoseDetector
from skeleton_generator import SkeletonGenerator
from animation_exporter import AnimationExporter
from utils import process_video, process_image, process_gif
from database import get_db, ProcessedFile, PoseData, AnimationData

def init_page():
    st.set_page_config(layout="wide", page_title="Pose Detection & Animation Generator")
    
    st.markdown("""
    <style>
    /* Base styling */
    .stApp {
        max-width: 100% !important;
        padding: 2rem;
        transition: background-color 0.3s;
    }
    [data-theme="dark"] .stApp {
        background-color: #1E1E1E;
        color: #FFFFFF;
    }
    .stButton>button {
        background-color: #4CAF50;
        color: white;
        border-radius: 4px;
        padding: 0.5rem 1rem;
    }
    .stDownloadButton>button {
        background-color: #008CBA;
    }
    .stProgress > div > div > div {
        background-color: #4CAF50;
    }
    .stColumn {
        padding: 0 1rem;
    }
    </style>
    """, unsafe_allow_html=True)

    st.sidebar.title("Settings")
    confidence_threshold = st.sidebar.slider(
        "Detection Confidence",
        min_value=0.0,
        max_value=1.0,
        value=0.5,
        step=0.1
    )
    
    export_format = st.sidebar.selectbox(
        "Export Format",
        options=['uasset', 'fbx', 'bvh'],
        key='export_format'
    )
    
    if "manual_correction" not in st.session_state:
        st.session_state.manual_correction = st.sidebar.checkbox("Enable Manual Corrections")
    else:
        st.session_state.manual_correction = st.sidebar.checkbox("Enable Manual Corrections", value=st.session_state.manual_correction)
        
    if st.session_state.manual_correction:
        st.sidebar.info("Интерактивная корректировка временно отключена.")
    
    st.title("Pose Detection & Animation Generator")
    return confidence_threshold

def init_components() -> Tuple[PoseDetector, SkeletonGenerator, AnimationExporter]:
    return PoseDetector(), SkeletonGenerator(), AnimationExporter()

def handle_upload(file_type: str, uploaded_file, components: Tuple, db_session) -> Optional[ProcessedFile]:
    processed_file = ProcessedFile(
        filename=uploaded_file.name,
        file_type='video' if uploaded_file.type == 'image/gif' else file_type,
        processing_status="processing"
    )
    db_session.add(processed_file)
    db_session.commit()
    db_session.refresh(processed_file)
    return processed_file

def main():
    init_page()
    components = init_components()
    
    uploaded_file = st.file_uploader(
        "Choose an image or video file (max 50MB)", 
        type=['jpg', 'jpeg', 'png', 'mp4', 'avi', 'gif']
    )
    
    if not uploaded_file:
        st.warning("Please upload a file to begin.")
        return

    db = next(get_db())
    try:
        file_type = uploaded_file.type.split('/')[0]
        is_gif = uploaded_file.type == 'image/gif'
        
        processed_file = handle_upload(file_type, uploaded_file, components, db)
        
        col1, col2 = st.columns(2)
        with col1:
            st.subheader("Original")
        with col2:
            st.subheader("Processed")

        try:
            if file_type == 'image' and not is_gif:
                process_image_upload(uploaded_file, components, processed_file, db, col1, col2)
            else:
                process_video_upload(uploaded_file, components, processed_file, db, is_gif, col1, col2)
        except Exception as e:
            st.error(f"Processing error: {str(e)}")
            processed_file.processing_status = "failed"
            db.commit()
            return

        processed_file.processing_status = "completed"
        db.commit()

    except Exception as e:
        st.error(f"An error occurred: {str(e)}")
    finally:
        db.close()

def process_image_upload(uploaded_file, components, processed_file, db, col1, col2):
    """Обработка изображения с сохранением состояния. Интерактивная коррекция отключена."""
    pose_detector, skeleton_generator, animation_exporter = components
    
    if "uploaded_image" not in st.session_state:
        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
        image = cv2.imdecode(file_bytes, 1)
        st.session_state.uploaded_image = image
    else:
        image = st.session_state.uploaded_image

    with col1:
        st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), use_column_width=True)
    
    if "original_skeleton_data" not in st.session_state:
        processed_image, skeleton_data = process_image(image, pose_detector, skeleton_generator)
        st.session_state.original_skeleton_data = skeleton_data
        st.session_state.processed_image = processed_image
    else:
        skeleton_data = st.session_state.original_skeleton_data
        processed_image = st.session_state.processed_image

    save_pose_data(db, processed_file.id, skeleton_data)
    animation_data_binary = animation_exporter.export_pose(skeleton_data)
    save_animation_data(db, processed_file.id, skeleton_data)

    with col2:
        processed_rgb = cv2.cvtColor(processed_image, cv2.COLOR_BGR2RGB)
        st.image(processed_rgb, use_column_width=True)
        if st.session_state.get('manual_correction', False):
            st.info("Интерактивная корректировка временно отключена.")

def process_video_upload(uploaded_file, components, processed_file, db, is_gif, col1, col2):
    """Обработка видео/GIF файлов."""
    pose_detector, skeleton_generator, animation_exporter = components
    progress_bar = st.progress(0)
    
    with tempfile.NamedTemporaryFile() as tfile:
        tfile.write(uploaded_file.read())
        tfile.seek(0)
        
        with col1:
            st.video(tfile.read())

        video_path = tfile.name
        if is_gif:
            processed_video_path, animation_frames = process_gif(video_path, pose_detector, skeleton_generator)
        else:
            processed_video_path, animation_frames = process_video(video_path, pose_detector, skeleton_generator)

        if not animation_frames:
            raise ValueError("No poses detected in the video/gif")

        save_video_data(db, processed_file.id, animation_frames)
        animation_data_binary = animation_exporter.export_animation(animation_frames)

        with col2:
            if processed_video_path:
                with open(processed_video_path, "rb") as f:
                    st.video(f.read())

        provide_download_button(animation_data_binary)

def save_pose_data(db, file_id: int, skeleton_data: dict):
    pose_data = PoseData(file_id=file_id, landmarks=skeleton_data)
    db.add(pose_data)
    db.commit()

def save_animation_data(db, file_id: int, skeleton_data: dict):
    animation_data = AnimationData(file_id=file_id, skeleton_data=skeleton_data)
    db.add(animation_data)
    db.commit()

def save_video_data(db, file_id: int, animation_frames: list):
    for frame_num, frame_data in enumerate(animation_frames):
        pose_data = PoseData(file_id=file_id, frame_number=frame_num, landmarks=frame_data)
        db.add(pose_data)
    db.commit()

def provide_download_button(animation_data_binary):
    st.download_button(
        label="Download Animation Data",
        data=animation_data_binary,
        file_name="animation.uasset",
        mime="application/octet-stream"
    )

def save_corrected_pose(db, file_id: int, joints: dict):
    pose_data = PoseData(file_id=file_id, landmarks=joints, corrected_landmarks=joints, is_corrected=True)
    db.add(pose_data)
    db.commit()

def show_instructions():
    with st.expander("Instructions"):
        st.markdown("""
        1. Upload an image or video using the file uploader.
        2. Wait for processing to complete.
        3. The **Detection Confidence** slider in the sidebar sets the minimum confidence threshold for detecting a pose.
           A higher value (e.g., 0.7) means that only detections with high certainty are considered, which may improve accuracy.
        4. The processed image/video will be displayed in the "Processed" panel.
        5. Download animation data.
        
        Supported formats:
        - Images: JPG, PNG
        - Videos: MP4, GIF
        """)

if __name__ == "__main__":
    main()
    show_instructions()