app.py

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

import huggingface_hub
from huggingface_hub import Repository

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."}

#repo_url = create_repo(repo_id="prompts_archive")
#prompts_archive_url = "https://huggingface.co/datasets/ryanrwatkins/prompts_archive"
#prompts_archive_file_name = "prompts_archive.csv"
#prompts_archive_file = os.path.join("prompts_archive", prompts_archive_file_name)
#print(prompts_archive_file)




#HF_TOKEN = os.environ.get("HF_token_write")
#repo = Repository(
#    local_dir="prompts_archive", clone_from=repo_url, use_auth_token=HF_TOKEN, git_user="ryanrwatkins", git_email="rwatkins@gwu.edu"
#)



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(prompts_archive.csv, "a") as csvfile:
        writer = csv.DictWriter(csvfile, fieldnames=["prompt", "time"])
        writer.writerow(
            {"prompt": str(prompt), "time": str(datetime.now())}
        )
    
   # with open(prompts_archive_file, "a") as csvfile:
   #     writer = csv.DictWriter(csvfile, fieldnames=["prompt", "time"])
   #     writer.writerow(
   #         {"prompt": str(prompt), "time": str(datetime.now())}
   #     )
   # commit_url = repo.push_to_hub()
   # print(commit_url)

    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')