import gradio as gr
from transformers import pipeline
import torch
from TTS.api import TTS
import tempfile
import os
import speech_recognition as sr
from difflib import SequenceMatcher

# Load models
qg_pipeline = pipeline("text2text-generation", model="valhalla/t5-small-e2e-qg")
tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False, gpu=False)

# Extract answer for comparison
def extract_answer(question, context):
    for line in context.split("\n"):
        if any(word.lower() in line.lower() for word in question.split()[:3]):
            return line
    return ""

# Generate questions from text
def generate_questions(text):
    output = qg_pipeline(f"generate questions: {text}", num_return_sequences=3)
    questions = [q["generated_text"] for q in output]
    return (questions, text, 0)  # This is stored in conversation_state

# Play the next question
def ask_question(state):
    questions, context, idx = state
    if idx >= len(questions):
        return "✅ All questions asked.", None, state

    question = questions[idx]
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as fp:
        tts.tts_to_file(text=question, file_path=fp.name)
        audio_path = fp.name

    return question, audio_path, (questions, context, idx + 1)

# Transcribe and provide feedback
def transcribe_and_feedback(audio_path, state):
    questions, context, idx = state
    if idx == 0 or idx > len(questions):
        return "❗ Please ask a question first.", state

    recognizer = sr.Recognizer()
    with sr.AudioFile(audio_path) as source:
        audio_data = recognizer.record(source)
        try:
            user_answer = recognizer.recognize_google(audio_data)
        except:
            return "❌ Could not understand your answer.", state

    # Compare with expected answer
    question = questions[idx - 1]
    expected = extract_answer(question, context)
    ratio = SequenceMatcher(None, user_answer.lower(), expected.lower()).ratio()
    if ratio > 0.6:
        feedback = f"✅ Good answer: {user_answer}"
    else:
        feedback = f"❌ Try again. You said: {user_answer}"

    return feedback, (questions, context, idx)

# Gradio UI
with gr.Blocks() as app:
    gr.Markdown("## 🎓 Interactive Speaking Practice")

    with gr.Row():
        course_text = gr.Textbox(lines=8, label="📘 Paste Coursebook Text")
        gen_btn = gr.Button("🔄 Generate Questions")

    question_text = gr.Textbox(label="🎤 Current Question")
    question_audio = gr.Audio(label="🔊 Listen to Question", type="filepath")
    ask_btn = gr.Button("▶️ Ask Next Question")

    user_audio = gr.Audio(label="🎙️ Your Answer (Record)", sources="microphone", type="filepath")
    transcribe_btn = gr.Button("📝 Submit Answer")
    feedback_output = gr.Textbox(label="💬 Feedback")

    conversation_state = gr.State()

    gen_btn.click(fn=generate_questions, inputs=course_text, outputs=conversation_state)
    ask_btn.click(fn=ask_question, inputs=conversation_state, outputs=[question_text, question_audio, conversation_state])
    transcribe_btn.click(fn=transcribe_and_feedback, inputs=[user_audio, conversation_state], outputs=[feedback_output, conversation_state])

app.launch()