import numpy as np import gradio as gr import json import socket import os from datetime import datetime import pandas as pd from langchain.chains import RetrievalQA from langchain.llms import OpenAI from langchain.document_loaders import TextLoader from langchain.document_loaders import PyPDFLoader from langchain.indexes import VectorstoreIndexCreator from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.chains import ConversationalRetrievalChain import shutil demo = gr.Blocks() options_org=[] options_bot=['','',''] isExist = os.path.exists("Organizations") if(isExist==False): os.mkdir("Organizations") if(os.path.isfile('Organizationdetails.json')): #Getting organization name f = open('Organizationdetails.json', encoding='utf-8', errors='ignore') data = json.load(f,strict=False) for p_id, p_info in data.items(): options_org.append(p_id) f.close() if(os.path.isfile('Botdetails.json')): #Getting organization name f1 = open('Botdetails.json') data = json.load(f1) for p_id, p_info in data.items(): options_bot.append(p_id) f1.close() def Create_Organization(org_name, org_handle): o=org_handle path = o isExist = os.path.exists(path) hostname=socket.gethostname() now = datetime.now() tim=now.strftime("%d/%m/%Y %H:%M:%S") Organizationdetails={} Organization_required_details = ["Organizationame","OrganizationHandle" "Created_by", "Created_Time"] Organizationdetails[org_handle] = {} Organizationdetails[org_handle]['Organizationame']=org_name Organizationdetails[org_handle]['OrganizationHandle']=org_handle Organizationdetails[org_handle]['Created_by']=hostname Organizationdetails[org_handle]['Created_Time']=tim isfileE = os.path.isfile("Organizationdetails.json") if isfileE: #If file present "rb" and w with open('Organizationdetails.json', 'rb') as txtfile: d=json.load(txtfile) d.update(Organizationdetails) for p_id, p_info in d.items(): options_org.append(p_id) with open('Organizationdetails.json', 'w') as txtfile: json.dump(d, txtfile) if not isExist: #create folder for working os.makedirs(os.path.join('Organizations', org_handle)) else: #if file not present then create with 'w' with open('Organizationdetails.json', 'w') as txtfile: json.dump(Organizationdetails, txtfile) if not isExist: #create folder for working os.makedirs(os.path.join('Organizations', org_handle)) return "Organization Created : "+ o def clear(): return None, None, None def Create_Bot(Organizationame,Bot_Name,Bot_Handle_Name,Bot_Image,Tools,OpenAI_API_key, Initial_Message,Intro_Message,Rules): botim =Bot_Image.name print(Bot_Name) b=Bot_Handle_Name bo=Organizationame hostname=socket.gethostname() now = datetime.now() tim=now.strftime("%d/%m/%Y %H:%M:%S") Botdetails = { } Bot_required_details = ["Bot_Name", "Organizationame", "Created_by", "Created_Time","Bot_Handle_Name","Bot_Image","Tools","OpenAI_API_key", "Initial_Message","Intro_Message","Rules"] Botdetails[Bot_Handle_Name] = {} Botdetails[Bot_Handle_Name]['Bot_Name']=Bot_Name Botdetails[Bot_Handle_Name]['Organizationame']=Organizationame Botdetails[Bot_Handle_Name]['Created_by']=hostname Botdetails[Bot_Handle_Name]['Created_Time']=tim Botdetails[Bot_Handle_Name]['Bot_Handle_Name']=Bot_Handle_Name Botdetails[Bot_Handle_Name]['Bot_Image']=botim Botdetails[Bot_Handle_Name]['Tools']=Tools Botdetails[Bot_Handle_Name]['OpenAI_API_key']=OpenAI_API_key Botdetails[Bot_Handle_Name]['Initial_Message']=Initial_Message Botdetails[Bot_Handle_Name]['Intro_Message']=Intro_Message Botdetails[Bot_Handle_Name]['Rules']=Rules path = os.path.join(os.getcwd()+'\\Organizations\\', Organizationame,Bot_Name) Inputpath = os.path.join(os.getcwd()+'\\Organizations\\', Organizationame,Bot_Name,'Inputs') Outputpath = os.path.join(os.getcwd()+'\\Organizations\\', Organizationame,Bot_Name,'Outputs') isExist = os.path.exists(path) pp=os.path.join('Organizations',Organizationame+'\\Botdetails.json') isfileE = os.path.isfile(pp) if isfileE: #If file present "rb" and 'w' with open(pp, 'rb') as txtfile: d=json.load(txtfile) d.update(Botdetails) with open(pp, 'w') as txtfile: json.dump(d, txtfile) if not isExist: os.makedirs(path) os.makedirs(Inputpath) os.makedirs(Outputpath) else: #if file not present then create with 'w' with open(pp, 'w') as txtfile: json.dump(Botdetails, txtfile) if not isExist: os.makedirs(path) os.makedirs(Inputpath) os.makedirs(Outputpath) return "Bot Created : " + b +" in "+bo +" Organization " def loadbotdata(SelectOrganizationame): new=[] if(os.path.isfile(os.getcwd()+'\\Organizations\\'+SelectOrganizationame+'\\Botdetails.json')): fd = open(os.getcwd()+'\\Organizations\\'+SelectOrganizationame+'\\Botdetails.json') data = json.load(fd) for p_id, p_info in data.items(): new.append(p_id) #return new return gr.update(choices=new, value=new[0]) def loadbotdataasdf(SelectOrganizationame): df=[] new=[] if(os.path.isfile(os.getcwd()+'\\Organizations\\'+SelectOrganizationame+'\\Botdetails.json')): fl = open(os.getcwd()+'\\Organizations\\'+SelectOrganizationame+'\\Botdetails.json') data = json.load(fl) df = pd.DataFrame.from_dict(data, orient='columns') print (df) for p_id, p_info in data.items(): new.append(p_id) return df,gr.update(choices=new, value=new[0]) def upload_file(org, bot , files): file_paths = [file.name for file in files] allfiles=file_paths path = os.path.join(os.getcwd()+'\\Organizations\\', org,bot,'Inputs') os.makedirs(path, exist_ok=True) for file_path in file_paths: destination_path = os.path.join(path, file_path) if not os.path.exists(destination_path): shutil.copy(file_path, destination_path) return file_paths def train(files): for file in files: print(file.name) if file.name.endswith(".pdf"): loader = PyPDFLoader(file.name) documents = loader.load() return "Training Done" chat_history = [] def construct_index(directory_path): file_paths=[] for root, directories, files in os.walk(directory_path): for file_name in files: file_path = os.path.join(root, file_name) file_paths.append(file_path) for file in file_paths: if file.endswith(".pdf"): loader = PyPDFLoader(file) documents = loader.load() return documents data_file_path = "deployment_archive_data.json" if not os.path.exists(data_file_path): with open(data_file_path, "w") as file: json.dump([], file) def deployment_or_archive(action_type): timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") # Load existing data from the JSON file with open(data_file_path, "r") as file: data = json.load(file) # Add the new action to the data list data.append({"action": action_type, "timestamp": timestamp}) # Write back the updated data to the JSON file with open(data_file_path, "w") as file: json.dump(data, file) def deploy_bot(org_name, bot_name): deployment_path = os.path.join(os.getcwd(), "Deployment") os.makedirs(deployment_path, exist_ok=True) org_details_path = os.path.join(os.getcwd(), "Organizationdetails.json") org_deploy_path = os.path.join(deployment_path, "Organizationdetails.json") shutil.copy(org_details_path, org_deploy_path) org_path = os.path.join(os.getcwd(), "Organizations", org_name) bot_details_path = os.path.join(org_path, "Botdetails.json") bot_deploy_path = os.path.join(deployment_path, "Botdetails.json") shutil.copy(bot_details_path, bot_deploy_path) bot_path = os.path.join(org_path, bot_name) model_files = os.listdir(bot_path) for file_name in model_files: file_path = os.path.join(bot_path, file_name) deploy_file_path = os.path.join(deployment_path, file_name) shutil.copy(file_path, deploy_file_path) return "Bot deployed successfully to the Deployment directory." def chatbot(input_text): global chat_history query = input_text text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() db = Chroma.from_documents(texts, embeddings) retriever = db.as_retriever(search_type="similarity", search_kwargs={"k":2}) vectordbkwargs = {"search_distance": 0.9} qa = ConversationalRetrievalChain.from_llm(OpenAI(), retriever,return_source_documents=True) print(chat_history) if chat_history==[]: result = qa({"question": query,"chat_history": chat_history, "vectordbkwargs": {"search_distance": 0.9}}) else: result = qa({"question": query, "chat_history": chat_history, "vectordbkwargs": {"search_distance": 0.9}}) chat_history = [(query, result["answer"])] return result["answer"] with demo: gr.Markdown("BotRite") with gr.Tabs() : with gr.TabItem("ChatBot"): with gr.Row(): SelectOrg = gr.Dropdown(options_org ,label="Select Organization" ) with gr.Row(): Selectbot = gr.Dropdown( label="Select Bot" ,choices=options_bot, value=options_bot[0], interactive=True) # Selectbot = gr.Radio(options_bot ,label="Select Bot") with gr.Column(): query_input = gr.Textbox(lines=7, label="Enter your text") ask_button = gr.Button("Ask") with gr.TabItem("Settings"): with gr.Tabs(): with gr.TabItem("Create Organization:"): with gr.Row(): org_name = gr.Textbox(label="Name",info="Your name / Nickname",placeholder="Enter you organization full name") org_handle = gr.Textbox(label="Handle Name",info="Your unique organization name", placeholder="Enter Organization handle name") output_org =gr.Textbox(label='Status') with gr.Row(): Createorg_button = gr.Button("Create Organization") Clearorg_button = gr.Button("Clear", variant="stop") with gr.TabItem("Bot Details"): SelectOrganizationame = gr.Dropdown(options_org, label="Select Organization") with gr.Tabs(): with gr.TabItem("Your Bots:"): with gr.Row(): with gr.Column(): #SelectOrganizationame = gr.components.Dropdown(options_org, label="Select Organization") botdf=gr.Dataframe(headers=["Bot_Name", "Organizationame", "Created_by", "Created_Time","Bot_Handle_Name","Bot_Image","Tools","OpenAI_API_key", "Initial_Message","Intro_Message","Rules"], label="Bot Details") with gr.TabItem("Create Bot:"): Organizationame =SelectOrganizationame botname = gr.Textbox(label="Bot Name",info="Your bot name / Nickname", placeholder="Enter bot full name") bothandle = gr.Textbox(label="Bot Handle Name",info="Your unique bot name" ,placeholder="Enter bot handle name") image_button = gr.File(label="Select bot image") botllm = gr.components.CheckboxGroup(['OpenAI', 'Dolly', 'Q&A Model'],label="Tools") # model = gr.components.Dropdown(Options1, label="Model") openai_key = gr.Textbox(label="You OpenAI API key", type="password" , info="Add your OpenAi Key click the link to create new or copy exsisting key from your openai account https://platform.openai.com/account/api-keys") initailsmsg = gr.Textbox(label="Initial Message", placeholder="This message will be shared by bot as intro​" , info="This message will be shared by bot as intro​") intromsg = gr.Textbox(label="Intro Message", placeholder="This message will be sent to bot as prefix to first message​", info="This message will be sent to bot as prefix to first message​") rules = gr.Textbox(label="Rules", placeholder="These rules will be sent to bot as prefix to first message (after introduction)​", info="These rules will be sent to bot as prefix to first message (after introduction)​") output_bot =gr.Textbox(label='Status') Createbot_button = gr.Button("Create Bot") with gr.TabItem("Bot Configuration"): Selectbotconfig = gr.Dropdown(label="Select Bot",choices=options_bot, value=options_bot[0], interactive=True) with gr.TabItem("Load Data"): with gr.Row(): with gr.Column(): Train_Fileselect_button = gr.UploadButton("Upload PDF Files", file_types=[".pdf"], file_count="multiple") file_output = gr.File() with gr.Column(): Train_button = gr.Button("Train Data") Train_output =gr.Textbox(label='Status') with gr.TabItem("Chat with your bot"): with gr.Row(): with gr.Column(): query_input = gr.Textbox(lines=7, label="Enter your text") ask_button = gr.Button("Ask") with gr.Column(): text_output=gr.Text(label="Your Bot Answer") with gr.TabItem("Deploy"): with gr.Row(): archive_button = gr.Button("Archive") deploy_button = gr.Button("Deploy") deploy_archive_output = gr.Textbox(label='Status') archive_output = gr.Textbox(label='Status') publish_button = gr.Button("Publish") with gr.TabItem("Logs"): with gr.Column(): Selectlog = gr.Dropdown( label="Select Log") logview = gr.Text(label="Log") #def Dropdown_Org(x): SelectOrg.change(fn=loadbotdata, inputs=SelectOrg,outputs=Selectbot ) SelectOrganizationame.change(fn=loadbotdataasdf, inputs=SelectOrganizationame,outputs=[botdf,Selectbotconfig]) Createorg_button.click(fn=Create_Organization,inputs=[org_name, org_handle], outputs=output_org) archive_button.click(fn=deploy_bot, inputs=[SelectOrganizationame, Selectbotconfig], outputs=archive_output) deploy_button.click(lambda: deployment_or_archive("deploy"), outputs=deploy_archive_output) archive_button.click(lambda: deployment_or_archive("archive"), outputs=deploy_archive_output) Clearorg_button.click(lambda : [None,None,None], inputs=None, outputs=[org_name,org_handle,output_org]) Createbot_button.click(fn=Create_Bot,inputs=[Organizationame, botname, bothandle,image_button,botllm,openai_key,initailsmsg,intromsg,rules], outputs=output_bot) Train_Fileselect_button.upload(upload_file,inputs=[SelectOrganizationame, Selectbotconfig,Train_Fileselect_button], outputs=[file_output]) Train_button.click(fn=train,inputs=Train_Fileselect_button, outputs=Train_output) demo.launch()