import gradio as gr import openai import requests import csv import os import langchain import chromadb import glob from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.text_splitter import TokenTextSplitter #from langchain.llms import OpenAI from langchain.chat_models import ChatOpenAI #from langchain.chains import ChatVectorDBChain from langchain.chains import RetrievalQA from langchain.document_loaders import PyPDFLoader from langchain.chains.question_answering import load_qa_chain # Use Chroma in Colab to create vector embeddings, I then saved them to HuggingFace so now I have to set it use them here. #from chromadb.config import Settings #client = chromadb.Client(Settings( ## chroma_db_impl="duckdb+parquet", # persist_directory="./embeddings" # Optional, defaults to .chromadb/ in the current directory #)) def get_empty_state(): return {"total_tokens": 0, "messages": []} #Initial prompt template, others added below from TXT file prompt_templates = {"All Needs Experts": "I want you to act as a needs assessment expert."} def download_prompt_templates(): url = "https://huggingface.co/spaces/ryanrwatkins/needs/raw/main/gurus.txt" try: response = requests.get(url) reader = csv.reader(response.text.splitlines()) next(reader) # skip the header row for row in reader: if len(row) >= 2: act = row[0].strip('"') prompt = row[1].strip('"') # description = row[2].strip('"') prompt_templates[act] = prompt except requests.exceptions.RequestException as e: print(f"An error occurred while downloading prompt templates: {e}") return choices = list(prompt_templates.keys()) choices = choices[:1] + sorted(choices[1:]) return gr.update(value=choices[0], choices=choices) def on_prompt_template_change(prompt_template): if not isinstance(prompt_template, str): return return prompt_templates[prompt_template] def submit_message(prompt, prompt_template, temperature, max_tokens, context_length, state): openai.api_key = os.environ['openai_key'] os.environ["OPENAI_API_KEY"] = os.environ['openai_key'] # load in all the files path = './files' #pdf_files = glob.glob(os.path.join(path, "*.pdf")) pdf_files = glob.glob(os.path.join(path, "*.pdf")) for file in pdf_files: loader = PyPDFLoader(file) pages = loader.load_and_split() text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=0) split_pages = text_splitter.split_documents(pages) persist_directory = "./embeddings" embeddings = OpenAIEmbeddings() vectordb = Chroma.from_documents(split_pages, embeddings, persist_directory=persist_directory) vectordb.persist() history = state['messages'] if not prompt: return gr.update(value=''), [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: {state['total_tokens']}", state prompt_template = prompt_templates[prompt_template] system_prompt = [] if prompt_template: system_prompt = [{ "role": "system", "content": prompt_template }] prompt_msg = { "role": "user", "content": prompt } try: #completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens) # completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens) completion_chain = load_qa_chain(OpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff" ) completion = RetrievalQA(combine_documents_chain=completion_chain, retriever=vectordb.as_retriever()) query = str(system_prompt + history[-context_length*2:] + [prompt_msg]) completion = completion.run(query) # from https://blog.devgenius.io/chat-with-document-s-using-openai-chatgpt-api-and-text-embedding-6a0ce3dc8bc8 history.append(prompt_msg) history.append(completion.choices[0].message.to_dict()) state['total_tokens'] += completion['usage']['total_tokens'] except Exception as e: history.append(prompt_msg) history.append({ "role": "system", "content": f"Error: {e}" }) total_tokens_used_msg = f"Total tokens used: {state['total_tokens']}" chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)] return '', chat_messages, total_tokens_used_msg, state def clear_conversation(): return gr.update(value=None, visible=True), None, "", get_empty_state() css = """ #col-container {max-width: 80%; margin-left: auto; margin-right: auto;} #chatbox {min-height: 400px;} #header {text-align: center;} #prompt_template_preview {padding: 1em; border-width: 1px; border-style: solid; border-color: #e0e0e0; border-radius: 4px;} #total_tokens_str {text-align: right; font-size: 0.8em; color: #666;} #label {font-size: 0.8em; padding: 0.5em; margin: 0;} .message { font-size: 1.2em; } """ with gr.Blocks(css=css) as demo: state = gr.State(get_empty_state()) with gr.Column(elem_id="col-container"): gr.Markdown("""# Chat with Needs Assessment Experts (Past and Present) ## Ask questions of experts on needs assessments, get responses from *needs assessment* version of ChatGPT. Ask questions of all of them, or pick your expert.""", elem_id="header") with gr.Row(): with gr.Column(): chatbot = gr.Chatbot(elem_id="chatbox") input_message = gr.Textbox(show_label=False, placeholder="Enter your needs assessment question and press enter", visible=True).style(container=False) btn_submit = gr.Button("Submit") total_tokens_str = gr.Markdown(elem_id="total_tokens_str") btn_clear_conversation = gr.Button("Start New Conversation") with gr.Column(): prompt_template = gr.Dropdown(label="Choose a expert:", choices=list(prompt_templates.keys())) prompt_template_preview = gr.Markdown(elem_id="prompt_template_preview") with gr.Accordion("Advanced parameters", open=False): temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, label="Flexibility", info="Higher = more creative/chaotic, Lower = just the guru") max_tokens = gr.Slider(minimum=100, maximum=400, value=200, step=1, label="Max tokens per response") context_length = gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Context length", info="Number of previous questions you have asked. Be careful with high values, it can blow up the token budget quickly.") btn_submit.click(submit_message, [ input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state]) input_message.submit(submit_message, [ input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state]) btn_clear_conversation.click(clear_conversation, [], [input_message, chatbot, total_tokens_str, state]) prompt_template.change(on_prompt_template_change, inputs=[prompt_template], outputs=[prompt_template_preview]) demo.load(download_prompt_templates, inputs=None, outputs=[prompt_template], queur=False) demo.queue(concurrency_count=10) demo.launch(height='800px')