import gradio as gr import openai import requests import csv import os import langchain import chromadb import glob import pickle from PyPDF2 import PdfReader from PyPDF2 import PdfWriter from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import CharacterTextSplitter from langchain.chains.question_answering import load_qa_chain from langchain.llms import OpenAI from langchain.embeddings.openai import OpenAIEmbeddings from langchain import OpenAI from langchain.chat_models import ChatOpenAI from langchain.document_loaders import PyPDFLoader from langchain.chains.question_answering import load_qa_chain openai.api_key = os.environ['openai_key'] os.environ["OPENAI_API_KEY"] = os.environ['openai_key'] prompt_templates = {"All Needs Experts": "Respond as if you are combination of all needs assessment experts."} actor_description = {"All Needs Experts": "
needs expert image
A combiation of all needs assessment experts."} def get_empty_state(): return { "messages": []} 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 actor_description[act] = description 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 on_prompt_template_change_description(prompt_template): if not isinstance(prompt_template, str): return return actor_description[prompt_template] def submit_message(prompt, prompt_template, temperature, max_tokens, context_length, state): 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)], 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: with open("embeddings.pkl", 'rb') as f: new_docsearch = pickle.load(f) query = str(system_prompt + history + [prompt_msg]) docs = new_docsearch.similarity_search(query) chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff") completion = chain.run(input_documents=docs, question=query) get_empty_state() state['content'] = completion #state.append(completion.copy()) completion = { "content": completion } #state['total_tokens'] += completion['usage']['total_tokens'] #except Exception as e: # history.append(prompt_msg.copy()) # error = { # "role": "system", # "content": f"Error: {e}" # } # history.append(error.copy()) #total_tokens_used_msg = f"Total tokens used: {state['total_tokens']}" chat_messages = [(prompt_msg['content'], completion['content'])] return '', chat_messages, state # total_tokens_used_msg, 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; min-height: 150px;} #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("""## Ask questions of *needs assessment* experts, ## get responses from a *needs assessment experts* version of ChatGPT. Ask questions of all of them, or pick your expert below.""" , 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", 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 an 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 AI, Lower = More Expert") max_tokens = gr.Slider(minimum=100, maximum=400, value=200, step=1, label="Length of Response.") context_length = gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Context Length", info="Number of previous questions you have asked.") btn_submit.click(submit_message, [ input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, state]) input_message.submit(submit_message, [ input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, state]) btn_clear_conversation.click(clear_conversation, [], [input_message, chatbot, state]) prompt_template.change(on_prompt_template_change_description, 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')