import gradio as gr import openai import tiktoken from multiprocessing.pool import ThreadPool enc = tiktoken.get_encoding("cl100k_base") MODES = { "Short summary": "Succintly summarize the following meeting transcript in a single paragraph.", "Detailed summary": "Summarize the following meeting transcript. The summary should include all the important points discussed in the meeting.", "Action points": "Summarize the following meeting transcript in form of action points.", "Further actions": "Who and what should be done next? Summarize the following meeting transcript in form of action points.", "Custom": "", } SUMMARY_PROMPT = "Summarize the following meeting in very great detail, in English. The summary should include all the important points discussed in the meeting." def summarize_part(text, api_key): response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ { "role": "system", "content": f"You are a meeting organizer. You want to succintly summarize a meeting. {SUMMARY_PROMPT}" }, { "role": "user", "content": "Summarize the following transcript in English: " + text }, ], api_key=api_key, ) return response["choices"][0]["message"]["content"] def shorten_text(text, api_key): """ Split text into chunks of 3000 tokens and summarize each chunk. """ chunks = [] words = text.split() for i in range(0, len(words), 1500): chunk = "" while len(enc.encode(chunk)) < 3000 and i < len(words): chunk += words[i] + " " i += 1 chunks.append(chunk) with ThreadPool(4) as pool: shortened = pool.starmap(summarize_part, zip(chunks, [api_key]*len(chunks))) return ". ".join(shortened) def modify_text(text, api_key, command, custom_command=None): if command == "Custom": prompt = custom_command else: prompt = MODES[command] if len(enc.encode(text)) < 4096: response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ { "role": "system", "content": f"You are a meeting organizer. You want to summarize a meeting. You are given the following transcript of the meeting. {prompt}" }, { "role": "user", "content": text }, ], api_key=api_key, ) return response["choices"][0]["message"]["content"] else: prompt = prompt.replace("meeting transcript", "meeting parts") shortened = text while len(enc.encode(shortened)) > 4096: shortened = shorten_text(shortened, api_key) response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=[ { "role": "system", "content": f"You are a meeting organizer. You want to summarize a meeting. You are given the following summary of the meeting parts. {prompt}" }, { "role": "user", "content": shortened }, ], api_key=api_key, ) return response["choices"][0]["message"]["content"] with gr.Blocks() as demo: gr.Markdown("# Meeting Summary") gr.Markdown("This app uses OpenAI's GPT-3 to summarize a meeting transcript. You can either use the default commands or enter your own one. The app will automatically split the transcript into parts if it is too long for GPT-3 to handle.") with gr.Row(): with gr.Column(): api_key = gr.Textbox(lines=1, label="OpenAI API Key") input_text = gr.Textbox(lines=15, label="Meeting Transcript") with gr.Column(): command = gr.Dropdown(list(MODES.keys()), label="Command", value="Short summary") custom_command = gr.Textbox(lines=2, label="Custom command", visible=False, value="Summarize the following meeting transcript in a single paragraph. The summary should include all the important points discussed in the meeting.") output_text = gr.Textbox(lines=10, label="Summary") def show_command(command): if command == "Custom": return {custom_command: gr.update(visible=True)} else: return {custom_command: gr.update(visible=False)} command.change(show_command, command, custom_command) button = gr.Button(label="Process") button.click(modify_text, [input_text, api_key, command, custom_command], output_text) demo.title = "Meeting Summary" demo.launch()