import logging
import queue
from collections import deque
from concurrent.futures import ThreadPoolExecutor

import streamlit as st
import cv2
from streamlit_webrtc import WebRtcMode, webrtc_streamer
from model import Predictor
import openai

# Настройки
DEFAULT_WIDTH = 50
openai.api_key = 'sk-proj-GDxupB1DFvTTWBg38VyST3BlbkFJ7MdcACLwu3u0U1QvWeMb'
logger = logging.getLogger(__name__)

def correct_text_gpt3(input_text):
    prompt = f"Исправь грамматические ошибки в тексте: '{input_text}'"
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are a helpful assistant that corrects grammatical errors."},
            {"role": "user", "content": prompt}
        ],
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )
    return response.choices[0].message['content'].strip()

# Центрируем контент
width = 50
side = max((100 - width) / 1.2, 0.01)
_, container, _ = st.columns([side, width, side])

# Модель инференса
class SLInference:
    def __init__(self, config_path):
        self.config = self.load_config(config_path)
        self.predictor = Predictor(self.config)
        self.input_queue = deque(maxlen=32)
        self.pred = ''

    def load_config(self, config_path):
        import json
        with open(config_path, 'r') as f:
            return json.load(f)

    def start(self):
        pass

    def predict(self, frames):
        frames_resized = [cv2.resize(frame, (224, 224)) for frame in frames]
        while len(frames_resized) < 32:
            frames_resized.append(frames_resized[-1])
        result = self.predictor.predict(frames_resized)
        if result:
            return result["labels"][0]
        return 'no'

def process_batch(inference_thread, frames, gestures):
    gesture = inference_thread.predict(frames)
    if gesture not in ['no', ''] and gesture not in gestures:
        gestures.append(gesture)

# Основной интерфейс
def main(config_path):
    # --- Заголовок блока ---
    st.markdown("""
    <div class="upload-section">
        <h3>🎥 Sign Language Recognition Demo</h3>
        <p>Upload a short video clip to detect sign gestures:</p>
    </div>
    """, unsafe_allow_html=True)

    # --- Скрытый лейбл uploader'а в стилизованной обёртке ---
    with st.container():
        uploaded_file = st.file_uploader(" ", type=["mp4", "avi", "mov", "gif"], label_visibility="collapsed")

    if uploaded_file is not None:
        video_bytes = uploaded_file.read()
        container.video(data=video_bytes)

        inference_thread = SLInference(config_path)
        inference_thread.start()

        text_output = st.empty()

        if st.button("🔍 Predict Gestures"):
            import tempfile
            tfile = tempfile.NamedTemporaryFile(delete=False)
            tfile.write(video_bytes)
            cap = cv2.VideoCapture(tfile.name)

            gestures = []
            frames = []
            batch_size = 32

            def process_frames(batch):
                process_batch(inference_thread, batch, gestures)

            with ThreadPoolExecutor() as executor:
                while cap.isOpened():
                    ret, frame = cap.read()
                    if not ret:
                        break
                    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                    frames.append(frame)
                    if len(frames) == batch_size:
                        executor.submit(process_frames, frames)
                        frames = []

                if frames:
                    executor.submit(process_frames, frames)

            cap.release()

            # Вывод результата
            text_output.markdown(
                f'<div class="section"><p style="font-size:20px">🖐️ Detected gestures: <b>{" ".join(gestures)}</b></p></div>',
                unsafe_allow_html=True
            )

            # Исправление текста
            st.text(correct_text_gpt3(" ".join(gestures)))

if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    main("configs/config.json")