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| import gradio as gr | |
| from openai import OpenAI | |
| import tiktoken | |
| from os import getenv as os_getenv | |
| from json import loads as json_loads | |
| from pathlib import Path | |
| import fitz | |
| MODEL = 'gpt-4-turbo' | |
| PRICE_PER_M = 10.00 | |
| LIMIT = 120000 # some space for answer | |
| api_key = os_getenv("OPENAI_APIKEY") | |
| client = OpenAI(api_key=api_key) | |
| def get_prompt(books, question = None): | |
| prompt = ( | |
| f"Read the following books.\n" + | |
| f"Each book may have some pages at the beggining with data about the book, an index, or table of content, etc. " + | |
| f"Pages may have a header and/or a footer. Consider all this maybe present." + | |
| f"Please answer, for each book, all below in the suggested format, in the language of the book:\n"+ | |
| f"**Title**: ...\n" | |
| f"**Author**: ...\n" | |
| f"**Chapter Names**: ...\n" | |
| f"**Characters**: \n" | |
| f"**Detailed Summary of the whole book**: \n" | |
| ) | |
| prompt += f"{books}\n" | |
| return prompt | |
| def chat(message, history, files): | |
| history_openai_format = [] | |
| if len(history) == 0: | |
| raise gr.Error("Primero hay que subir un libro") | |
| if len(history) == 1: | |
| if message: | |
| raise gr.Error("First message must be empty") | |
| message = history[0][0] | |
| else: | |
| for human, assistant in history: | |
| if human: | |
| history_openai_format.append({"role": "user", "content": human }) | |
| if assistant: | |
| history_openai_format.append({"role": "assistant", "content":assistant}) | |
| history_openai_format.append({"role": "user", "content": message}) | |
| response = client.chat.completions.create( | |
| model=MODEL, | |
| messages= history_openai_format, | |
| temperature=1.0, | |
| stream=True) | |
| partial_message = "" | |
| for chunk in response: | |
| if chunk.choices[0].delta.content is not None: | |
| partial_message = partial_message + chunk.choices[0].delta.content | |
| yield partial_message | |
| def get_text(filename): | |
| answer = "" | |
| suffix = Path(filename).suffix | |
| if suffix in [".pdf"]: | |
| for i,page in enumerate(fitz.open(filename)): | |
| answer += f"\n### Page #{i+1}\n{page.get_text()}\n" | |
| elif suffix in [".txt"]: | |
| answer = open(filename).read() | |
| return answer | |
| def files_ready(filenames): | |
| encoder = encoding = tiktoken.encoding_for_model('gpt-4-turbo') | |
| books = '' | |
| for i, name in enumerate(filenames): | |
| books += f"\n## Document #{i+1}\nName: {Path(name).name}\n" | |
| books += get_text(name) | |
| prompt = get_prompt(books) | |
| tokens = len(encoder.encode(prompt)) | |
| cost = tokens * PRICE_PER_M / 1000000 * 2 # * 2 is too much for an answer | |
| if tokens > LIMIT: | |
| raise gr.Error(f"Book is too long. It's {tokens} tokens long and can't be more than {LIMIT}.") | |
| return tokens, f"${cost}", [[prompt, None]] | |
| def files_changed(filenames): | |
| if filenames: | |
| return "-", "-" | |
| else: | |
| return 0, "$0" | |
| with gr.Blocks(title="Book summarization and more") as demo: | |
| with gr.Row(): | |
| files = gr.Files(file_types=["txt","doc","docx","pdf"] ) | |
| with gr.Column(): | |
| tokens = gr.Text("0", label="Tokens") | |
| cost = gr.Text("0", label="Cost") | |
| chat = gr.ChatInterface( | |
| fn=chat, | |
| title="Summarization and more", | |
| additional_inputs=[files], | |
| multimodal=False) | |
| other = gr.Button(interactive=False) | |
| files.upload(files_ready, [files], [tokens, cost, chat.chatbot_state]) | |
| files.change(files_changed, files, [tokens, cost]) | |
| auth=os_getenv("APP_USERS", "null") | |
| auth=json_loads(auth) | |
| demo.launch(auth=auth) | |