app.py

import openai
import gradio as gr
from gradio.components import Audio, Textbox
import os
import re
from transformers import GPT2Tokenizer
import whisper
import pandas as pd
from datetime import datetime, timezone, timedelta
import notion_df

openai.api_key = os.environ["OPENAI_API_KEY"]
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

initial_message = {"role": "system", "content": 'You are a USMLE Tutor. Respond with ALWAYS layered "bullet points" (listing rather than sentences) to all input with a fun mneumonics to memorize that list. But you can answer up to 1200 words if the user requests longer response.'}
messages = [initial_message]

answer_count = 0

# set up whisper model
model = whisper.load_model("base")

def num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301"):
    """Returns the number of tokens used by a list of messages."""
    try:
        encoding = tiktoken.encoding_for_model(model)
    except KeyError:
        encoding = tiktoken.get_encoding("cl100k_base")
    if model == "gpt-3.5-turbo-0301":  # note: future models may deviate from this
        num_tokens = 0
        for message in messages:
            num_tokens += 4  # every message follows <im_start>{role/name}\n{content}<im_end>\n
            for key, value in message.items():
                num_tokens += len(encoding.encode(value))
                if key == "name":  # if there's a name, the role is omitted
                    num_tokens += -1  # role is always required and always 1 token
        num_tokens += 2  # every reply is primed with <im_start>assistant
        return num_tokens
    else:
        raise NotImplementedError(f"""num_tokens_from_messages() is not presently implemented for model {model}.
See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")

def transcribe(audio, text):
    global messages
    global answer_count

    if audio is not None:
        audio_file = open(audio, "rb")
        transcript = openai.Audio.transcribe("whisper-1", audio_file, language="en")
        messages.append({"role": "user", "content": transcript["text"]})

    if text is not None:
        # Split the input text into sentences
        sentences = re.split("(?<=[.!?]) +", text)

        # Tokenize the sentences using the GPT-2 tokenizer
        sentence_tokens = [tokenizer.encode(sentence) for sentence in sentences]

        # Flatten the list of tokens
        input_tokens = [token for sentence in sentence_tokens for token in sentence]

        # Check if adding the input tokens would exceed the token limit
        num_tokens = num_tokens_from_messages(messages)
        if num_tokens + len(input_tokens) > 2200:
            # Reset the messages list and answer counter
            messages = [initial_message]
            answer_count = 0
            input_text = 'Can you click the Submit button one more time? (say Yes)'
            messages.append({"role": "user", "content": input_text})
        else:
            # Add the input tokens to the messages list
            input_text = tokenizer.decode(input_tokens)
            messages.append({"role": "user", "content": input_text})

    # Check if the accumulated tokens have exceeded the limit
    num_tokens = num_tokens_from_messages(messages)
    if num_tokens > 2096:
        # Concatenate the chat history
        chat_transcript = ""
        for message in messages:
            if message['role'] != 'system':
                chat_transcript += f"[ANSWER {answer_count}]{message['role']}: {message['content']}\n\n"
        # Append the number of tokens used to the end of the chat transcript
        chat_transcript += f"Number of tokens used: {num_tokens}\n\n"
        
        # Get the current time in Eastern Time (ET)
        now_et = datetime.now(timezone(timedelta(hours=-5)))
        # Format the time as string (YY-MM-DD HH:MM)
        published_date = now_et.strftime('%m-%d-%y %H:%M')
        
        # Upload the chat transcript to Notion
        df = pd.DataFrame([chat_transcript])
        notion_df.upload(df, 'https://www.notion.so/personal-5e3978680ca848bda844452129955138?pvs=4', title=str(published_date), api_key=API_KEY)
        
        # Reset the messages list and answer counter
        messages = [initial_message]
        answer_count = 0

    # Increment the answer counter
    answer_count += 1

    # Generate the system message using the OpenAI API
    system_message = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=messages,
        max_tokens=2000
    )["choices"][0]["message"]



    # Add the system message to the messages list
    messages.append({"role": "system", "content": system_message})

    # Concatenate the chat history
    chat_transcript = ""
    for message in messages:
        if message['role'] != 'system':
            chat_transcript += f"[ANSWER {answer_count}]{message['role']}: {message['content']}\n\n"
    
    # Append the number of tokens used to the end of the chat transcript
    num_tokens = num_tokens_from_messages(messages)
    chat_transcript += f"Number of tokens used: {num_tokens}\n\n"
    
    # Get the current time in Eastern Time (ET)
    now_et = datetime.now(timezone(timedelta(hours=-5)))
    # Format the time as string (YY-MM-DD HH:MM)
    published_date = now_et.strftime('%m-%d-%y %H:%M')
    
    # Upload the chat transcript to Notion
    df = pd.DataFrame([chat_transcript])
    notion_df.upload(df, 'https://www.notion.so/personal-5e3978680ca848bda844452129955138?pvs=4', title=str(published_date), api_key=API_KEY)
    
    # Reset the messages list and answer counter if the token limit is exceeded
    if num_tokens > 2096:
        messages = [initial_message]
        answer_count = 0
    else:
        # Increment the answer counter
        answer_count += 1
    
    # Generate the system message using the OpenAI API
    system_message = openai.Completion.create(
        engine="text-davinci-002",
        prompt=[{"text": f"{message['role']}: {message['content']}\n\n"} for message in messages],
        temperature=0.7,
        max_tokens=2000,
        n=1,
        stop=None,
    )[0]["text"]
    
    # Add the system message to the messages list
    messages.append({"role": "system", "content": system_message})


audio_input = Audio(source="microphone", type="filepath", label="Record your message")
text_input = Textbox(label="Type your message", max_length=4096)

output_text = gr.outputs.Textbox(label="Response")

iface = gr.Interface(
    fn=transcribe,
    inputs=[audio_input, text_input],
    outputs="text",
    title="YENA",
    description="Tutor YENA")

# Launch Gradio interface
iface.launch()