app.py

import gradio as gr
import openai
import requests
import csv
import os
import langchain
import chromadb
import glob
import pickle
from datetime import datetime

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": "<div style='float: left;margin: 0px 5px 0px 5px;'><img src='https://na.weshareresearch.com/wp-content/uploads/2023/04/experts2.jpg' alt='needs expert image' style='width:70px;align:top;'></div>A combiation of all needs assessment experts."}


prompts_archive_url = "https://huggingface.co/datasets/ryanrwatkins/na_prompts_archive"
prompts_archive_file_name = "prompts_archive.txt"
prompts_archive_file = os.path.join("prompts_archive", prompts_archive_file_name)

HF_TOKEN = os.environ.get("HF_TOKEN")
repo = Repository(
    local_dir="data", clone_from=prompts_archive_url, use_auth_token=HF_TOKEN
)
repo.push_to_hub()


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]

    
    
    with open(DATA_FILE, "a") as csvfile:
            writer = csv.DictWriter(csvfile, fieldnames=["prompt", "time"])
            writer.writerow(
                {"prompt": str(prompt), "time": str(datetime.now())}
            )
    

    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')