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import openai
import gradio as gr
from gradio.components import Audio, Textbox
import os
import re
import tiktoken
from transformers import GPT2Tokenizer
import whisper
import pandas as pd
from datetime import datetime, timezone, timedelta
import notion_df
import concurrent.futures
# Define the tokenizer and model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium')
model = openai.api_key = os.environ["OAPI_KEY"]
# Define the initial message and messages list
initial_message = {"role": "system", "content": 'You are a Renal System 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. \
You are going to keep answer and also challenge the student to learn renal phsysiology.'}
messages = [initial_message]
# Define the answer counter
answer_count = 0
# Define the Notion API key
API_KEY = os.environ["NAPI_KEY"]
def transcribe(audio, text):
global messages
global answer_count
transcript = {'text': ''}
input_text = []
# Transcribe the audio if provided
if audio is not None:
audio_file = open(audio, "rb")
transcript = openai.Audio.transcribe("whisper-1", audio_file, language="en")
# Tokenize the text input
if text is not None:
# Split the input text into sentences
sentences = re.split("(?<=[.!?]) +", text)
# Initialize a list to store the tokens
input_tokens = []
# Add each sentence to the input_tokens list
for sentence in sentences:
# Tokenize the sentence using the GPT-2 tokenizer
sentence_tokens = tokenizer.encode(sentence)
# Check if adding the sentence would exceed the token limit
if len(input_tokens) + len(sentence_tokens) < 1440:
# Add the sentence tokens to the input_tokens list
input_tokens.extend(sentence_tokens)
else:
# If adding the sentence would exceed the token limit, truncate it
sentence_tokens = sentence_tokens[:1440-len(input_tokens)]
input_tokens.extend(sentence_tokens)
break
# Decode the input tokens into text
input_text = tokenizer.decode(input_tokens)
# Add the input text to the messages list
# messages.append({"role": "user", "content": transcript["text"]+input_text})
# Check if the accumulated tokens have exceeded 2096
num_tokens = sum(len(tokenizer.encode(message["content"])) for message in messages)
if num_tokens > 2096:
# Concatenate the chat history
chat_transcript = "\n\n".join([f"[ANSWER {answer_count}]{message['role']}: {message['content']}" for message in messages if message['role'] != 'system'])
# Append the number of tokens used to the end of the chat transcript
chat_transcript += f"\n\nNumber 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/YENA-be569d0a40c940e7b6e0679318215790?pvs=4', title=str(published_date+'back_up'), api_key=API_KEY)
# 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)'
# Add the input text to the messages list
messages.append({"role": "user", "content": input_text})
else:
# Increment the answer counter
answer_count += 1
# Generate the system message using the OpenAI API
with concurrent.futures.ThreadPoolExecutor() as executor:
prompt = [{"text": f"{message['role']}: {message['content']}\n\n"} for message in messages]
system_message = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=messages,
max_tokens=2000
)["choices"][0]["message"]
# Wait for the completion of the OpenAI API call
# Add the system message to the messages list
# messages.append(system_message)
# Add the system message to the beginning of the messages list
messages.insert(0, system_message)
# Add the input text to the messages list
messages.insert(0, {"role": "user", "content": input_text + transcript["text"]})
# Concatenate the chat history
chat_transcript = "\n\n".join([f"[ANSWER {answer_count}]{message['role']}: {message['content']}" for message in messages if message['role'] != 'system'])
# chat_transcript_copy = chat_transcript
# Append the number of tokens used to the end of the chat transcript
chat_transcript += f"\n\nNumber of tokens used: {num_tokens}\n\n"
# Upload the chat transcript to Notion
now_et = datetime.now(timezone(timedelta(hours=-5)))
published_date = now_et.strftime('%m-%d-%y %H:%M')
df = pd.DataFrame([chat_transcript])
notion_df.upload(df, 'https://www.notion.so/YENA-be569d0a40c940e7b6e0679318215790?pvs=4', title=str(published_date), api_key=API_KEY)
# Return the chat transcript
return system_message['content']
# Define the input and output components for Gradio
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")
output_audio = Audio()
# Define the Gradio interface
iface = gr.Interface(
fn=transcribe,
inputs=[audio_input, text_input],
outputs=[output_text],
title="Hold On, Pain Ends (HOPE)",
description="Talk to Your Nephrology Tutor HOPE",
theme="compact",
layout="vertical",
allow_flagging=False
)
# Run the Gradio interface
iface.launch() |