import streamlit as st
from faster_whisper import WhisperModel
from transformers import pipeline
from textblob import TextBlob
import numpy as np
import queue
import threading
import time

# Initialize shared state
audio_queue = queue.Queue()
transcription_results = []
ai_detection_results = []

# Global stop event for threads
stop_event = threading.Event()

def initialize_model():
    """Initialize Whisper model and AI detectors."""
    st.session_state.model = WhisperModel(model_size="small", device="cpu", compute_type="int8")
    st.session_state.ai_detector = pipeline('text-classification', model='roberta-base-openai-detector')

def advanced_ai_detection(text, ai_detector):
    """Perform AI detection on the text."""
    if len(text.split()) < 5:  # Skip short texts
        return "Insufficient Data"

    result = ai_detector(text)[0]
    return result

def analyze_text_linguistics(text):
    """Perform linguistic analysis on the text."""
    blob = TextBlob(text)
    return {
        "sentiment": blob.sentiment.polarity,
        "subjectivity": blob.sentiment.subjectivity
    }

def transcribe_audio(audio_chunk, model):
    """Transcribe audio using the Whisper model."""
    segments, _ = model.transcribe(audio_chunk, language="en")
    return [segment.text for segment in segments]

def run_app():
    """Main Streamlit app function."""
    st.title("AI Speech Detector")
    st.subheader("Real-Time Speech Transcription and AI Detection")
    st.text("This app transcribes audio input and detects if the text is AI-generated.")

    # Sidebar for controls
    st.sidebar.title("Controls")
    start_button = st.sidebar.button("Start Recording")
    stop_button = st.sidebar.button("Stop Recording")

    if "model" not in st.session_state:
        st.text("Loading AI models...")
        initialize_model()
        st.text("Models loaded successfully!")

    # Display transcript
    st.text_area("Real-Time Transcript", height=200, key="transcript")

    # Display AI detection results
    st.text_area("AI Detection Results", height=200, key="ai_detection")

    if start_button:
        st.session_state.is_recording = True
        threading.Thread(target=process_audio_stream).start()

    if stop_button:
        st.session_state.is_recording = False
        stop_event.set()

def process_audio_stream():
    """Simulated audio capture and processing."""
    model = st.session_state.model
    ai_detector = st.session_state.ai_detector

    while not stop_event.is_set():
        try:
            # Simulate real-time audio input
            fake_audio_chunk = np.random.rand(16000 * 3).astype(np.float32)  # 3 seconds of fake audio
            transcription = transcribe_audio(fake_audio_chunk, model)

            # Update real-time transcript
            for text in transcription:
                st.session_state.transcript += text + "\n"

                # Perform AI detection
                ai_result = advanced_ai_detection(text, ai_detector)
                st.session_state.ai_detection += f"Text: {text}\nResult: {ai_result}\n\n"

        except Exception as e:
            st.error(f"Error during transcription: {str(e)}")
            break

    stop_event.clear()

if __name__ == "__main__":
    run_app()