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| import os | |
| from openai import AsyncOpenAI # importing openai for API usage | |
| import chainlit as cl # importing chainlit for our app | |
| from chainlit.prompt import Prompt, PromptMessage # importing prompt tools | |
| from chainlit.playground.providers import ChatOpenAI # importing ChatOpenAI tools | |
| from dotenv import load_dotenv | |
| import asyncio | |
| import textract # importing textract for document processing | |
| # Load OpenAI API key from environment variables | |
| api_key = os.getenv("OPENAI_API_KEY") | |
| # Templates for ChatOpenAI interaction | |
| system_template = """You are a helpful assistant who always speaks in a pleasant tone and answers based on the provided document! | |
| """ | |
| user_template = """{input} | |
| Use the document content to respond to the user query step by step. | |
| """ | |
| # Function to extract text from uploaded documents | |
| async def extract_text_from_file(file): | |
| # Extract text from the uploaded file using textract | |
| return textract.process(file).decode('utf-8') | |
| # Function to start the chat session | |
| async def start_chat(): | |
| settings = { | |
| "model": "gpt-3.5-turbo", | |
| "temperature": 0, | |
| "max_tokens": 500, | |
| "top_p": 1, | |
| "frequency_penalty": 0, | |
| "presence_penalty": 0, | |
| } | |
| cl.user_session.set("settings", settings) | |
| # Welcome message with file upload option | |
| await cl.Message(content="Welcome! Please upload a document to begin.").send() | |
| # Function to handle uploaded files | |
| async def on_file_upload(files: list): | |
| # Handle the uploaded files, assuming it's the first file | |
| if files: | |
| file = files[0] | |
| file_content = await extract_text_from_file(file.path) | |
| # Save document content in session for later use | |
| cl.user_session.set("document_content", file_content) | |
| # Inform the user that the document was successfully uploaded | |
| await cl.Message(content=f"Document '{file.name}' uploaded successfully! You can now ask questions based on the document content.").send() | |
| # Function to handle user messages | |
| async def main(message: cl.Message): | |
| settings = cl.user_session.get("settings") | |
| document_content = cl.user_session.get("document_content", "") | |
| if not document_content: | |
| # If no document is uploaded, prompt the user to upload one | |
| await cl.Message(content="Please upload a document first.").send() | |
| return | |
| client = AsyncOpenAI() | |
| prompt = Prompt( | |
| provider=ChatOpenAI.id, | |
| messages=[ | |
| PromptMessage( | |
| role="system", | |
| template=system_template, | |
| formatted=system_template, | |
| ), | |
| PromptMessage( | |
| role="user", | |
| template=user_template, | |
| formatted=user_template.format(input=message.content), | |
| ), | |
| ], | |
| inputs={"input": message.content, "document": document_content}, | |
| settings=settings, | |
| ) | |
| msg = cl.Message(content="") | |
| # Call OpenAI | |
| async for stream_resp in await client.chat.completions.create( | |
| messages=[m.to_openai() for m in prompt.messages], stream=True, **settings | |
| ): | |
| token = stream_resp.choices[0].delta.content | |
| if not token: | |
| token = "" | |
| await msg.stream_token(token) | |
| # Update the prompt object with the completion | |
| prompt.completion = msg.content | |
| msg.prompt = prompt | |
| # Send and close the message stream | |
| await msg.send() | |