app.py

# app.py
import gradio as gr
import asyncio
import os
import thinkingframes
import soundfile as sf
import numpy as np
import logging
from openai import OpenAI
from dotenv import load_dotenv
from policy import user_acceptance_policy
from styles import theme
from thinkingframes import generate_prompt, strategy_options
from utils import get_image_html, collect_student_info
from database_functions import add_submission
from tab_teachers_dashboard import create_teachers_dashboard_tab
from config import CLASS_OPTIONS
from concurrent.futures import ThreadPoolExecutor

# Load CSS from external file
with open('styles.css', 'r') as file:
    css = file.read()

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

load_dotenv()

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

# Create a thread pool executor
executor = ThreadPoolExecutor()

def transcribe_audio(audio_path):
    with open(audio_path, "rb") as audio_file:
        transcript = client.audio.transcriptions.create(file=audio_file, model="whisper-1", language="en")
    return transcript.text

async def generate_feedback(user_id, question_choice, strategy_choice, message, feedback_level):
    current_question_index = thinkingframes.questions.index(question_choice)
    strategy, explanation = thinkingframes.strategy_options[strategy_choice]

    conversation = [{
        "role": "system",
        "content": f"You are an expert Primary 6 English Language Teacher in a Singapore Primary school, "
                   f"directly guiding a Primary 6 student in Singapore in their oral responses. "
                   f"Format the feedback in Markdown so that it can be easily read. "
                   f"Address the student directly in the second person in your feedback. "
                   f"The student is answering the question: '{thinkingframes.questions[current_question_index]}'. "
                   f"For Question 1, consider the picture description: '{thinkingframes.description}'. "
                   f"For Questions 2 and 3, the picture is not relevant, so the student should not refer to it in their response. "
                   f"Analyze the student's response using the following step-by-step approach: "
                   f"1. Evaluate the response against the {strategy} thinking frame. "
                   f"2. Assess how well the student's response addresses each component of the {strategy} thinking frame: "
                   f"   - Assign emoticon scores based on how well the student comprehensively covered each component: "
                   f"     - 😊😊😊 (three smiling faces) for a good coverage "
                   f"     - 😊😊 (two smiling faces) for an average coverage "
                   f"     - 😊 (one smiling face) for a poor coverage "
                   f"   - Provide a clear, direct, and concise explanation of how well the answer addresses each component. "
                   f"   - Identify specific areas for improvement in students responses, and provide targeted suggestions for improvement. "
                   f"3. Identify overall strengths and areas for improvement in the student's response using the {strategy} to format and provide targeted areas for improvement. "
                   f"4. Provide specific feedback on grammar, vocabulary, and sentence structure. "
                   f"   Suggest age-appropriate enhancements that are one level higher than the student's current response. "
                   f"5. Conclude with follow-up questions for reflection. "
                   f"If the student's response deviates from the question, provide clear and concise feedback to help them refocus and try again. "
                   f"Ensure that the vocabulary and sentence structure recommendations are achievable for Primary 6 students in Singapore. "
                   f"Example Feedback Structure for Each Component: "
                   f"Component: [Component Name] "
                   f"Score: [Smiling emoticons] "
                   f"Explanation: [Clear, direct, and concise explanation of how well the answer addresses the component. Identify specific areas for improvement, and provide targeted suggestions for improvement.] "
                   f"{thinkingframes.generate_prompt(feedback_level)}"
    }, {
        "role": "user",
        "content": message
    }]

    response = client.chat.completions.create(
        model='gpt-4o-2024-05-13',
        messages=conversation,
        temperature=0.6,
        max_tokens=1000,
        stream=True
    )

    chat_history = []  # Initialize chat history outside the loop
    full_feedback = ""  # Accumulate the entire feedback message
    try:
        for chunk in response:
            if chunk.choices[0].delta and chunk.choices[0].delta.content:
                feedback_chunk = chunk.choices[0].delta.content
                full_feedback += feedback_chunk  # Accumulate the feedback
                await asyncio.sleep(0)

        # Append the complete feedback to the chat history
        chat_history.append(("Oral Coach  ⚡ ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", full_feedback))
        yield chat_history  # Yield the chat history only once

    except Exception as e:
        logging.error(f"An error occurred during feedback generation: {str(e)}")

    questionNo = current_question_index + 1
    # Save complete feedback after streaming
    add_submission(user_id, message, full_feedback, int(0), "", questionNo)

async def generate_audio_feedback(feedback_buffer):
    try:
        response = client.audio.speech.create(
            model="tts-1",
            voice="alloy",
            input=feedback_buffer,
            response_format="wav"
        )

        audio_data = np.frombuffer(response.read(), dtype=np.int16)
        sample_rate = 24000  # Default sample rate for OpenAI's WAV output

        return (sample_rate, audio_data)

    except Exception as e:
        logging.error(f"An error occurred during speech generation: {str(e)}")
        return None  # Return None in case of an error

async def predict(question_choice, strategy_choice, feedback_level, audio):
    current_audio_output = None  # Initialize current_audio_output to None
    final_feedback = ""  # Store only the assistant's feedback

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

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

    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 ...")]
    yield chat_history, current_audio_output

    try:
        transcription_future = executor.submit(transcribe_audio, audio_path)
        student_response = await asyncio.wrap_future(transcription_future)

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

        chat_history.append(("Student", student_response))  # Add student's transcript
        yield chat_history, current_audio_output

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

        moderation_response = client.moderations.create(input=student_response)
        flagged = any(result.flagged for result in moderation_response.results)
        if flagged:
            moderated_message = "The message has been flagged. Please see your teacher to clarify."
            questionNo = thinkingframes.questions.index(question_choice) + 1
            add_submission(int(user_state.value), moderated_message, "", int(0), "", questionNo)
            yield chat_history, current_audio_output
            return

        async for chat_update in generate_feedback(int(user_state.value), question_choice, strategy_choice, student_response, feedback_level):
                # Append the assistant's feedback to the existing chat_history
                chat_history.extend(chat_update)  
                final_feedback = chat_history[-1][1]  # Update final_feedback with the latest chunk
                yield chat_history, current_audio_output # Yield audio output

        feedback_buffer = final_feedback  # Use final_feedback for TTS
        audio_task = asyncio.create_task(generate_audio_feedback(feedback_buffer))
        current_audio_output = await audio_task  # Store audio output
        yield chat_history, current_audio_output  # Yield audio output

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

with gr.Blocks(title="Oral Coach  ⚡ ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡", theme=theme, css=css) as app:
    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("## English Language Oral Coach  ⚡ ϞϞ(๑⚈ ․̫ ⚈๑)∩ ⚡")
            gr.Markdown(img_html)  # Display the image
            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
            #submit_answer_here        
                    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],
                api_name="predict"
            )

        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)
            validation_passed, message, userid = collect_student_info(class_name, index_no)
            if not validation_passed:
                return message, gr.update(visible=False)
            user_state.value = userid
            return message, gr.update(visible=True)

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

    # Define other tabs like Teacher's Dashboard
    create_teachers_dashboard_tab()

app.queue(max_size=20).launch(
    debug=True,
    server_port=int(os.environ.get("PORT", 10000)),
    favicon_path="favicon.ico"
)