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import openai_async
import asyncio
import nest_asyncio
import torch
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gpt2")
def count_tokens(text):
input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
return input_ids.shape[1]
def break_up_file_to_chunks(text, chunk_size=2000, overlap=100):
tokens = tokenizer.encode(text)
num_tokens = len(tokens)
chunks = []
for i in range(0, num_tokens, chunk_size - overlap):
chunk = tokens[i:i + chunk_size]
chunks.append(chunk)
return chunks
async def summarize_meeting(prompt, timeout, max_tokens):
#timeout = 30
temperature = 0.5
#max_tokens = 1000
top_p = 1
frequency_penalty = 0
presence_penalty = 0
# Call the OpenAI GPT-3 API
response = await openai_async.complete(
api_key = API_KEY,
timeout=timeout,
payload={
"model": "gpt-3.5-turbo",
"prompt": prompt,
"temperature": temperature,
"max_tokens": max_tokens,
"top_p": top_p,
"frequency_penalty": frequency_penalty,
"presence_penalty": presence_penalty
},
)
# Return the generated text
return response
def main_summarizer_meet(text, debug=False):
if debug:
return "This is a test summary function"
prompt_response = []
prompt_tokens = []
chunks = break_up_file_to_chunks(text)
for i, chunk in enumerate(chunks):
prompt_request = (
f"Summarize this meeting transcript: {tokenizer.decode(chunks[i])}"
)
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
response = loop.run_until_complete(summarize_meeting(prompt = prompt_request, timeout=30, max_tokens = 1000))
prompt_response.append(response.json()["choices"][0]["text"].strip())
prompt_tokens.append(response.json()["usage"]["total_tokens"])
prompt_request = f"Consoloidate these meeting summaries: {prompt_response}"
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
response = loop.run_until_complete(summarize_meeting(prompt = prompt_request, timeout=45, max_tokens = 1000))
return response.json()["choices"][0]["text"].strip()
# -----------------------------
def main_summarizer_action_items(text, debug=False):
if debug:
return "This is a test action items function"
action_response = []
action_tokens = []
chunks = break_up_file_to_chunks(text)
for i, chunk in enumerate(chunks):
prompt_request = f"Provide a list of action items with a due date from the provided meeting transcript text: {tokenizer.decode(chunks[i])}"
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
response = loop.run_until_complete(summarize_meeting(prompt = prompt_request, timeout=30, max_tokens = 1000))
action_response.append(response.json()["choices"][0]["text"].strip())
action_tokens.append(response.json()["usage"]["total_tokens"])
return '\n'.join(action_response) |