app.py

import os
import gradio as gr
#import elevenlabs
#from elevenlabs.client import ElevenLabs
#from speech_recognition import Recognizer, AudioFile
import io
from google import genai
from google.genai import types
#from dotenv import load_dotenv
#from elevenlabs import play
from TTS.api import TTS

#load_dotenv()

#elevenlabs = ElevenLabs(
#  api_key=os.getenv("ELEVENLABS_API_KEY"),
#)


# --- TTS Setup ---  
model_tts = TTS(model_name="tts_models/multilingual/multi-dataset/xtts_v2", progress_bar=False, gpu=False)

# --- API Keys (Set as environment variables for security!) ---
genai_api_key = os.environ.get("GOOGLE_API_KEY")  # Gemini API Key
#elevenlabs_api_key = os.environ.get("ELEVENLABS_API_KEY")

#elevenlabs.set_api_key(elevenlabs_api_key)
#elevenlabs.API_KEY = elevenlabs_api_key

client = genai.Client(api_key=genai_api_key)

# --- ElevenLabs Voice ---
voice = "p225"#"Bella"  # Choose a voice from ElevenLabs

# --- Language Tutor Parameters ---
target_language = "Arabic"
difficulty = 1  # 1 = Easy, 2 = Medium, 3 = Hard

# --- Gemini Model ---
#model = genai.GenerativeModel('gemini-pro')  # Or 'gemini-pro-vision' if you need image input
#model = genai.GenerativeModel('gemini-1.5-pro-latest')
#model = genai.GenerativeModel('gemini-2-flash')
#model = genai.GenerativeModel('gemini-pro', generate_response_clause=genai.types.GenerateResponseClause(model='gemini-pro'))


# --- Functions ---

def generate_question(difficulty):
    """Generates a Arabic question based on difficulty using Gemini."""
    prompt = f"Generate a simple {target_language} question for a language learner at difficulty level {difficulty}. Just the question, no extra text."
    response = client.models.generate_content(
        model="gemini-2.0-flash",
        contents=prompt,
        config=types.GenerateContentConfig(temperature=0.95)
    )
    
    #response = model.generate_content(prompt)
    return response.text.strip()

def evaluate_answer(question, answer):
    """Evaluates the user's answer using Gemini."""
    prompt = f"You are a Arabic language tutor. Evaluate the following answer to the question: '{question}'. Answer: '{answer}'. Provide feedback on grammar, vocabulary, and fluency. Keep the feedback concise (under 50 words). Also, give a score from 1-5 (1 being very poor, 5 being excellent)."
    #response = model.generate_content(prompt)
    response = client.models.generate_content(
        model="gemini-2.0-flash",
        contents=prompt,
        config=types.GenerateContentConfig(temperature=0.1)
    )
    
    return response.text.strip()

def text_to_speech(text, voice):
    """Converts text to speech using ElevenLabs."""

    #audio = elevenlabs.text_to_speech.convert(
    #text=text,
    #voice_id="JBFqnCBsd6RMkjVDRZzb",
    #model_id="eleven_monolingual_v1",
    #output_format="mp3_44100_128",
    #)
    
    #audio = elevenlabs.tts(text=text, voice=voice, model="eleven_monolingual_v1")
    #return b"".join(audio)
    #wav = tts.tts(text=text)#, language=target_language)
    wav = tts_model.tts_to_file(text=text, language=target_language)#, voice="p225", split_sentences=True)# speaker_wav="/path/to/target/speaker.wav", split_sentences=True)
    return wav


def transcribe_audio(audio_file):
    """Transcribes audio using SpeechRecognition."""
    r = Recognizer()
    with AudioFile(audio_file) as source:
        audio = r.record(source)
    try:
        text = r.recognize_google(audio, language=target_language) # You might need to adjust the language code
        return text
    except Exception as e:
        return f"Error transcribing audio: {e}"



def run_tutor():
    """Main function to run the tutor."""
    question = generate_question(difficulty)
    question_audio = text_to_speech(question, voice)

    return question_audio, question

def process_answer(audio_file, question, question_audio):
    """Processes the user's answer and provides feedback."""
    user_answer = transcribe_audio(audio_file)
    feedback = evaluate_answer(question, user_answer)
    feedback_audio = text_to_speech(feedback, voice)

    return feedback_audio, feedback
    

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Adaptive Language Tutor (Arabic)")
    with gr.Row():
        generate_button = gr.Button("Generate Question")
        question_audio_output = gr.Audio(label="Question")
        question_text_output = gr.Textbox(label="Question Text")
    with gr.Row():
        feedback_audio_output = gr.Audio(label="Feedback")
        feedback_text_output = gr.Textbox(label="Feedback")
    mic_input = gr.Audio(label="Speak Your Answer")
    

    generate_button.click(
        fn=run_tutor,
        outputs=[question_audio_output, question_text_output]
    )

    mic_input.change(
        fn=process_answer,
        inputs=[mic_input, question_text_output, question_audio_output],
        outputs=[feedback_audio_output, feedback_text_output]
    )

demo.launch()



'''

def run_tutor(audio_file):
    """Main function to run the tutor."""
    question = generate_question(difficulty)
    question_audio = text_to_speech(question, voice)

        # Display the question in the interface
    yield question_audio, question, None, None

    # Transcribe the user's answer
    user_answer = transcribe_audio(audio_file)

    # Evaluate the answer
    feedback = evaluate_answer(question, user_answer)
    feedback_audio = text_to_speech(feedback, voice)
    
    yield None, None, feedback_audio, feedback
    #return question_audio, feedback_audio, question, user_answer, feedback



    

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Adaptive Language Tutor (Arabic)")
#    with gr.Row():
#        question_audio_output = gr.Audio(label="Question")
#        feedback_audio_output = gr.Audio(label="Feedback")
#    with gr.Row():
#        feedback_audio_output = gr.Audio(label="Feedback")
#        feedback_text_output = gr.Textbox(label="Feedback")


    with gr.Row():
        question_audio_output = gr.Audio(label="Question")
        question_text_output = gr.Textbox(label="Question Text")
    with gr.Row():
        feedback_audio_output = gr.Audio(label="Feedback")
        feedback_text_output = gr.Textbox(label="Feedback")
    
    mic_input = gr.Audio(label="Speak Your Answer")
    generate_button = gr.Button("Generate Question")    

    generate_button.click(
        fn=run_tutor,
        inputs=mic_input,
        outputs=[question_audio_output, question_text_output, feedback_audio_output, feedback_text_output]
    )

#NameError: name 'question_text_output' is not defined. Did you mean: 'question_audio_output'?
        
        #
#        question_text_output = gr.Textbox(label="Question Text")
#        answer_text_output = gr.Textbox(label="Your Answer")
#        feedback_text_output = gr.Textbox(label="Feedback")
#    mic_input = gr.Audio(label="Speak Your Answer")#

#    mic_input.change(
#        fn=run_tutor,
#        inputs=mic_input,
#        outputs=[question_audio_output, feedback_audio_output, question_text_output, answer_text_output, feedback_text_output]
#    )

demo.launch()
'''