app.py

import gradio as gr
import os
import thinkingframes
import soundfile as sf
import numpy as np
import logging
from dotenv import load_dotenv
from policy import user_acceptance_policy
from styles import theme
from thinkingframes import generate_prompt, strategy_options, questions
from utils import get_image_html, collect_student_info
from database_functions import add_user_privacy, add_submission
from tab_teachers_dashboard import create_teachers_dashboard_tab
from config import CLASS_OPTIONS
import spaces
import edge_tts
import tempfile

# Load environment variables
load_dotenv()

# Whisper API settings
API_URL = "https://api-inference.huggingface.co/models/whisper-large"
headers = {"Authorization": f"Bearer {os.getenv('HF_AUTH_TOKEN')}"}

def whisper_query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()

# For maintaining user session (to keep track of userID)
user_state = gr.State(value="")

# Load the Meta-Llama-3-8B model from Hugging Face
llm = gr.load("meta-llama/Meta-Llama-3-8B", src="models")

image_path = "picturePerformance.jpg"
img_html = get_image_html(image_path)

@spaces.GPU(duration=120)
def transcribe(audio_path):
    response = whisper_query(audio_path)
    if "text" in response:
        return response["text"]
    else:
        raise ValueError("Transcription failed.")

@spaces.GPU(duration=120)
def generate_feedback(user_id, question_choice, strategy_choice, message, feedback_level):
    current_question_index = questions.index(question_choice)
    strategy, explanation = strategy_options[strategy_choice]

    conversation = [{
        "role": "system",
        "content": thinkingframes.generate_system_message(current_question_index, feedback_level)
    }, {
        "role": "user",
        "content": message
    }]

    feedback = llm(conversation)[0]["generated_text"]

    questionNo = current_question_index + 1
    add_submission(user_id, message, feedback, int(0), "", questionNo)

    return feedback

@spaces.GPU(duration=60)
def generate_audio_feedback(feedback_buffer):
    communicate = edge_tts.Communicate(feedback_buffer)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
        tmp_path = tmp_file.name
        asyncio.run(communicate.save(tmp_path))
    return tmp_path

def predict(question_choice, strategy_choice, feedback_level, audio):
    current_audio_output = None

    if audio is None:
        return [("Oral Coach ⚡ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", "No audio data received. Please try again.")], current_audio_output

    sample_rate, audio_data = audio

    if audio_data is None or len(audio_data) == 0:
        return [("Oral Coach ⚡ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", "No audio data received. Please try again.")], current_audio_output

    audio_path = "audio.wav"
    if not isinstance(audio_data, np.ndarray):
        raise ValueError("audio_data must be a numpy array")
    sf.write(audio_path, audio_data, sample_rate)

    chat_history = [("Oral Coach ⚡ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", "Transcribing your audio, please listen to your oral response while waiting ...")]

    try:
        student_response = transcribe(audio_path)

        if not student_response.strip():
            return [("Oral Coach ⚡ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", "Transcription failed. Please try again or seek assistance.")], current_audio_output

        chat_history.append(("Student", student_response))

        chat_history.append(("Oral Coach ⚡ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", "Transcription complete. Generating feedback. Please continue listening to your oral response while waiting ..."))

        feedback = generate_feedback(int(user_state.value), question_choice, strategy_choice, student_response, feedback_level)

        chat_history.append(("Oral Coach ⚡ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", feedback))

        audio_output_path = generate_audio_feedback(feedback)

        current_audio_output = (24000, audio_output_path)
        return chat_history, current_audio_output

    except Exception as e:
        logging.error(f"An error occurred: {str(e)}", exc_info=True)
        return [("Oral Coach ⚡ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", "An error occurred. Please try again or seek assistance.")], current_audio_output

def toggle_oral_coach_visibility(class_name, index_no, policy_checked):
    if not policy_checked:
        return "Please agree to the Things to Note When using the Oral Coach ⚡ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡ before submitting.", gr.update(visible=False)
    user_id, message = add_user_privacy(class_name, index_no)
    if "Error" in message:
        return message, gr.update(visible=False)
    user_state.value = user_id
    return message, gr.update(visible=True)

with gr.Blocks(title="Oral Coach powered by ZeroGPU⚡ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡ and Meta AI 🦙 (LLama3)", theme=theme, css="footer {visibility: hidden}textbox{resize:none}") as demo:
    with gr.Tab("Oral Coach ⚡ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡"):
        gr.Markdown("## Student Information")
        class_name = gr.Dropdown(label="Class", choices=CLASS_OPTIONS)
        index_no = gr.Dropdown(label="Index No", choices=[f"{i:02}" for i in range(1, 46)])

        policy_text = gr.Markdown(user_acceptance_policy)
        policy_checkbox = gr.Checkbox(label="I have read and agree to the Things to Note When using the Oral Coach ⚡ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", value=False)

        submit_info_btn = gr.Button("Submit Info")
        info_output = gr.Text()

        with gr.Column(visible=False) as oral_coach_content:
            gr.Markdown("## Powered by Hugging Face")
            gr.Markdown(img_html)
            with gr.Row():
                with gr.Column(scale=1):
                    gr.Markdown("### Step 1: Choose a Question")
                    question_choice = gr.Radio(thinkingframes.questions, label="Questions", value=thinkingframes.questions[0])
                    gr.Markdown("### Step 2: Choose a Thinking Frame")
                    strategy_choice = gr.Dropdown(list(strategy_options.keys()), label="Thinking Frame", value=list(strategy_options.keys())[0])
                    gr.Markdown("### Step 3: Choose Feedback Level")
                    feedback_level = gr.Radio(["Brief Feedback", "Moderate Feedback", "Comprehensive Feedback"], label="Feedback Level")
                    feedback_level.value = "Brief Feedback"

                with gr.Column(scale=1):
                    gr.Markdown("### Step 4: Record Your Answer")
                    audio_input = gr.Audio(type="numpy", sources=["microphone"], label="Record")
                    submit_answer_btn = gr.Button("Submit Oral Response")

                    gr.Markdown("### Step 5: Review your personalised feedback")
                    feedback_output = gr.Chatbot(label="Feedback", scale=4, height=700, show_label=True)
                    audio_output = gr.Audio(type="numpy", label="Audio Playback", format="wav", autoplay="True")

                    submit_answer_btn.click(
                        predict,
                        inputs=[question_choice, strategy_choice, feedback_level, audio_input],
                        outputs=[feedback_output, audio_output]
                    )

        submit_info_btn.click(
            toggle_oral_coach_visibility,
            inputs=[class_name, index_no, policy_checkbox],
            outputs=[info_output, oral_coach_content]
        )

    create_teachers_dashboard_tab()

demo.queue(max_size=20)
demo.launch()