app.py

import os
import time
import gradio as gr
import openai

from langdetect import detect 
from gtts import gTTS
from pdfminer.high_level import extract_text

#any vector server should work, trying pinecone first
import pinecone

#langchain part
import spacy
import tiktoken
from langchain.llms import OpenAI
from langchain.text_splitter import SpacyTextSplitter
from langchain.document_loaders import TextLoader
from langchain.document_loaders import DirectoryLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
import markdown


openai.api_key = os.environ['OPENAI_API_KEY']
pinecone_key = os.environ['PINECONE_API_KEY_AMD']
pinecone_environment='us-west1-gcp-free'


user_db = {os.environ['username1']: os.environ['password1'], os.environ['username2']: os.environ['password2'], os.environ['username3']: os.environ['password3']}

messages = [{"role": "system", "content": 'You are a helpful assistant.'}]

#load up spacy

nlp = spacy.load("en_core_web_sm")



def init_pinecone():
    pinecone.init(api_key=pinecone_key, environment=pinecone_environment)
    return




def process_file(index_name, dir):

    init_pinecone()

    #using openai embedding hence dim = 1536
    pinecone.create_index(index_name, dimension=1536, metric="cosine")
    #time.sleep(5)
    
    embeddings = OpenAIEmbeddings(openai_api_key=os.environ['OPENAI_API_KEY'])
    splter = SpacyTextSplitter(chunk_size=1000,chunk_overlap=200)

    for doc in dir:
        loader = TextLoader(doc.name , encoding='utf8')
        content = loader.load()
        split_text = splter.split_documents(content)
        for text in split_text:
        	Pinecone.from_documents([text], embeddings, index_name=index_name)

    #pipeline='zh_core_web_sm'
    

    return 


def list_pinecone():
    init_pinecone()
    return pinecone.list_indexes()


def show_pinecone(index_name):
    init_pinecone()
    #return pinecone.describe_index(index_name)
    index = pinecone.Index(index_name)
    stats = index.describe_index_stats()
    return stats



def delete_pinecone(index_name):
    init_pinecone()
    pinecone.delete_index(index_name)
    return








def roleChoice(role):
    global messages
    messages = [{"role": "system", "content": role}]
    return "role:" + role






def talk2file(index_name, text):
    #disable the global message
    #global messages
    messages = [{"role": "system", "content": 'You are a helpful assistant.'}]
    
    #same as filesearch
    init_pinecone()
    embeddings = OpenAIEmbeddings(openai_api_key=os.environ['OPENAI_API_KEY'])
    docsearch = Pinecone.from_existing_index(index_name, embeddings)
    docs = docsearch.similarity_search(text)

    
    prompt = text + ", based on the following context: \n\n" 
    qwcontext = prompt + docs[0].page_content
    messages.append({"role": "user", "content": qwcontext})

    response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages)

    system_message = response["choices"][0]["message"]
    #messages.append(system_message)

    #chats = ""
    #for msg in messages:
    #    if msg['role'] != 'system':
    #        chats += msg['role'] + ": " + msg['content'] + "\n\n"

    #Title1 = '<h2 style="background-color: yellow;"><b>User Question: </b></h2>'
    #User_Question = f'<div style="background-color: yellow; display: inline-block; word-wrap: break-word;">{prompt}</div>'
    Title2 = '<h2><b>Context Found: </b></h2>'
    context = docs[0].page_content
    #context = f'<span style="background-color: #ADD8E6; display: inline-block; word-wrap: break-word;">{context2html}</span>'
    #Title3 = '<h2 style="background-color: #90EE90;"><b>Ansewr: </b></h2>'
    answer = system_message["content"]


    
    return [context, answer]





def fileSearch(index_name, prompt):
    global messages

    init_pinecone()
    embeddings = OpenAIEmbeddings(openai_api_key=os.environ['OPENAI_API_KEY'])
    docsearch = Pinecone.from_existing_index(index_name, embeddings)
    docs = docsearch.similarity_search(prompt)

    return ["#Top1 context:" + docs[0].page_content, "#Top2 context:" + docs[1].page_content, "#Top3 context:" + docs[2].page_content]



def clear():
    global messages
    messages = [{"role": "system", "content": 'You are a helpful technology assistant.'}]
    return
    
def show():
    global messages
    chats = ""
    for msg in messages:
        if msg['role'] != 'system':
            chats += msg['role'] + ": " + msg['content'] + "\n\n"

    return chats


with gr.Blocks() as chatHistory:
    gr.Markdown("Click the Clear button below to remove all the chat history.")
    clear_btn = gr.Button("Clear")
    clear_btn.click(fn=clear, inputs=None, outputs=None, queue=False)

    gr.Markdown("Click the Display button below to show all the chat history.")
    show_out = gr.Textbox()
    show_btn = gr.Button("Display")
    show_btn.click(fn=show, inputs=None, outputs=show_out, queue=False)


#pinecone tools
with gr.Blocks() as pinecone_tools: 
    pinecone_list = gr.Textbox()
    list = gr.Button(value="List") 
    list.click(fn=list_pinecone, inputs=None, outputs=pinecone_list, queue=False)

    pinecone_delete_name = gr.Textbox()
    delete = gr.Button(value="Delete") 
    delete.click(fn=delete_pinecone, inputs=pinecone_delete_name, outputs=None, queue=False)

    pinecone_show_name = gr.Textbox()
    pinecone_info = gr.Textbox()
    show = gr.Button(value="Show") 
    show.click(fn=show_pinecone, inputs=pinecone_show_name, outputs=pinecone_info, queue=False)



    


textbox = gr.inputs.Textbox(label="Vector Server Index Name: ", default="amd")
textbox2 = gr.inputs.Textbox(label="Vector Server Index Name: ", default="amd")
answerbox = gr.inputs.Textbox(label="Assistant answer")
contextbox = gr.Markdown(label="Context found")


contextbox1 = gr.Markdown(label="Top1 context")
contextbox2 = gr.Markdown(label="Top2 context")
contextbox3 = gr.Markdown(label="Top3 context")

role = gr.Interface(fn=roleChoice, inputs="text", outputs="text", description = "Choose your GPT roles, e.g. You are a helpful technology assistant.")
text = gr.Interface(fn=talk2file, inputs=[textbox, "text"], outputs=[contextbox, answerbox])

vector_server = gr.Interface(fn=process_file, inputs=["text", gr.inputs.File(file_count="directory")], outputs="text")

#audio = gr.Interface(fn=audioGPT, inputs=gr.Audio(source="microphone", type="filepath"), outputs="text")
#siri = gr.Interface(fn=siriGPT, inputs=gr.Audio(source="microphone", type="filepath"), outputs = "audio")
file = gr.Interface(fn=fileSearch, inputs=[textbox2, "text"], outputs=[contextbox1, contextbox2, contextbox3], description = "This tab shows the top three most related contexts in the repository.")
#demo = gr.TabbedInterface([role, text, file, vector_server, pinecone_tools, chatHistory], [ "roleChoice", "Talk2File", "FileSearch", "VectorServer", "PineconeTools", "ChatHistory"])

demo = gr.TabbedInterface([text, file, chatHistory], [ "ROCm Usage Tutor", "Top 3 Context", "ChatHistory"])

if __name__ == "__main__":
    demo.launch(enable_queue=False, auth=lambda u, p: user_db.get(u) == p,
        auth_message="This is not designed to be used publicly as it links to a personal openAI API. However, you can copy my code and create your own multi-functional ChatGPT with your unique ID and password by utilizing the 'Repository secrets' feature in huggingface.")
    #demo.launch()