import io import tempfile import jwt import base64 from click import option from jwt import ExpiredSignatureError, InvalidTokenError from starlette import status from functions import * import pandas as pd from fastapi import FastAPI, File, UploadFile, HTTPException from pydantic import BaseModel from fastapi.middleware.cors import CORSMiddleware from src.api.speech_api import speech_translator_router from functions import client as supabase from urllib.parse import urlparse import nltk nltk.download('punkt_tab') app = FastAPI(title="ConversAI", root_path="/api/v1") app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) app.include_router(speech_translator_router, prefix="/speech") @app.post("/signup") async def sign_up(email, username, password): res, _ = supabase.auth.sign_up( {"email": email, "password": password, "role": "user"} ) user_id = res[1].id r_ = createUser(user_id=user_id, username=username, email=email) if r_.get('code') == 409: return r_ elif r_.get('code') == 200: response = { "status": "success", "code": 200, "message": "Please check you email address for email verification", } else: response = { "status": "failed", "code": 400, "message": "Failed to sign up please try again later", } return response @app.post("/session-check") async def check_session(user_id: str): res = supabase.auth.get_session() if res == None: try: supabase.table("Stores").delete().eq( "StoreID", user_id ).execute() resp = supabase.auth.sign_out() response = {"message": "success", "code": 200, "Session": res} return response except Exception as e: raise HTTPException(status_code=400, detail=str(e)) return res @app.post("/get-user") async def get_user(access_token): res = supabase.auth.get_user(jwt=access_token) return res @app.post("/referesh-token") async def refresh_token(refresh_token): res = supabase.auth.refresh_token(refresh_token) return res @app.post("/login") async def sign_in(email, password): try: res = supabase.auth.sign_in_with_password( {"email": email, "password": password} ) user_id = res.user.id access_token = res.session.access_token refresh_token = res.session.refresh_token store_session_check = supabase.table("Stores").select("*").filter("StoreID", "eq", user_id).execute() store_id = None if store_session_check and store_session_check.data: store_id = store_session_check.data[0].get("StoreID") userData = supabase.table("ConversAI_UserInfo").select("*").filter("user_id", "eq", user_id).execute().data username = userData[0]["username"] if not store_id: response = ( supabase.table("Stores").insert( { "AccessToken": access_token, "StoreID": user_id, "RefreshToken": refresh_token, "email": email } ).execute() ) message = { "message": "Success", "code": status.HTTP_200_OK, "username": username, "user_id": user_id, "access_token": access_token, "refresh_token": refresh_token, } return message elif store_id == user_id: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, detail="You are already signed in. Please sign out first to sign in again." ) else: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, detail="Failed to sign in. Please check your credentials." ) except HTTPException as http_exc: raise http_exc except Exception as e: raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"An unexpected error occurred during sign-in: {str(e)}" ) @app.post("/login_with_token") async def login_with_token(access_token: str, refresh_token: str): try: decoded_token = jwt.decode(access_token, options={"verify_signature": False}) user_id_oauth = decoded_token.get("sub") try: user_id = supabase.table("ConversAI_UserInfo").select("*").filter("user_id", "eq", user_id_oauth).execute() user_id = supabase.table("ConversAI_UserInfo").select("*").filter("email", "eq", user_id_oauth).execute() user_name = user_id.data[0]["username"] except: user_name = '' json = { "code": status.HTTP_200_OK, "user_id": decoded_token.get("sub"), "email": decoded_token.get("email"), "access_token": access_token, "refresh_token": refresh_token, "issued_at": decoded_token.get("iat"), "expires_at": decoded_token.get("exp"), "username": user_name } return json except (ExpiredSignatureError, InvalidTokenError) as e: raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail=str(e)) @app.post("/user_name") async def user_name_(username: str, user_id: str): user_data = supabase.table("Stores").select("*").filter("StoreID", "eq", user_id).execute() email = user_data.data[0].get("email") r_ = createUser(user_id=user_id, username=username, email=email) return r_ @app.post("/set-session-data") async def set_session_data(access_token, refresh_token, user_id): res = supabase.auth.set_session(access_token, refresh_token) store_session_check = supabase.table("Stores").select("*").filter("StoreID", "eq", user_id).execute() store_id = None if store_session_check and store_session_check.data: store_id = store_session_check.data[0].get("StoreID") if not store_id: response = ( supabase.table("Stores").insert( { "AccessToken": access_token, "StoreID": user_id, "RefreshToken": refresh_token, } ).execute() ) res = { "message": "success", "code": 200, "session_data": res, } return res @app.post("/logout") async def sign_out(user_id): try: supabase.table("Stores").delete().eq( "StoreID", user_id ).execute() res = supabase.auth.sign_out() response = {"message": "success"} return response except Exception as e: raise HTTPException(status_code=400, detail=str(e)) @app.post("/oauth") async def oauth(): res = supabase.auth.sign_in_with_oauth( {"provider": "google", "options": {"redirect_to": "https://convers-ai-lac.vercel.app/"}}) return res @app.post("/newChatbot") async def newChatbot(chatbotName: str, username: str): currentBotCount = len(listTables(username=username)["output"]) limit = supabase.table("ConversAI_UserConfig").select("chatbotLimit").eq("user_id", username).execute().data[0][ "chatbotLimit"] if currentBotCount >= int(limit): return { "output": "CHATBOT LIMIT EXCEEDED" } supabase.table("ConversAI_ChatbotInfo").insert({"user_id": username, "chatbotname": chatbotName}).execute() chatbotName = f"convai${username}${chatbotName}" return createTable(tablename=chatbotName) @app.post("/loadPDF") async def addPDFData(vectorstore: str, pdf: UploadFile = File(...)): source = pdf.filename pdf = await pdf.read() with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as temp_file: temp_file.write(pdf) temp_file_path = temp_file.name text = extractTextFromPdf(temp_file_path) os.remove(temp_file_path) return { "output": text, "source": source } @app.post("/loadImagePDF") async def returnText(pdf: UploadFile = File(...)): source = pdf.filename pdf = await pdf.read() text = getTextFromImagePDF(pdfBytes=pdf) return { "output": text, "source": source } class AddText(BaseModel): vectorstore: str text: str source: str = "Text" @app.post("/addText") async def addText(addTextConfig: AddText): vectorstore, text, source = addTextConfig.vectorstore, addTextConfig.text, addTextConfig.source text = base64.b64decode(text.encode("utf-8")).decode("utf-8") username, chatbotname = vectorstore.split("$")[1], vectorstore.split("$")[2] df = pd.DataFrame(supabase.table("ConversAI_ChatbotInfo").select("*").execute().data) currentCount = df[(df["user_id"] == username) & (df["chatbotname"] == chatbotname)]["charactercount"].iloc[0] newCount = currentCount + len(text) limit = supabase.table("ConversAI_UserConfig").select("tokenLimit").eq("user_id", username).execute().data[0][ "tokenLimit"] if newCount < int(limit): supabase.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("user_id", username).eq( "chatbotname", chatbotname).execute() output = addDocuments(text=text, source=source, vectorstore=vectorstore) return output else: return { "output": "WEBSITE EXCEEDING LIMITS, PLEASE TRY WITH A SMALLER DOCUMENT." } class AddQAPair(BaseModel): vectorstore: str question: str answer: str @app.post("/addQAPair") async def addQAPairData(addQaPair: AddQAPair): username, chatbotname = addQaPair.vectorstore.split("$")[1], addQaPair.vectorstore.split("$")[2] df = pd.DataFrame(supabase.table("ConversAI_ChatbotInfo").select("*").execute().data) currentCount = df[(df["user_id"] == username) & (df["chatbotname"] == chatbotname)]["charactercount"].iloc[0] qa = f"QUESTION: {addQaPair.question}\tANSWER: {addQaPair.answer}" newCount = currentCount + len(qa) limit = supabase.table("ConversAI_UserConfig").select("tokenLimit").eq("user_id", username).execute().data[0][ "tokenLimit"] if newCount < int(limit): supabase.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("user_id", username).eq( "chatbotname", chatbotname).execute() return addDocuments(text=qa, source="Q&A Pairs", vectorstore=addQaPair.vectorstore) else: return { "output": "WEBSITE EXCEEDING LIMITS, PLEASE TRY WITH A SMALLER DOCUMENT." } @app.post("/loadWebURLs") async def addWebsite(vectorstore: str, websiteUrls: list[str]): text = extractTextFromUrlList(urls=websiteUrls) return { "output": text } @app.post("/answerQuery") async def answerQuestion(query: str, vectorstore: str, llmModel: str = "llama3-70b-8192"): username, chatbotName = vectorstore.split("$")[1], vectorstore.split("$")[2] output = answerQuery(query=query, vectorstore=vectorstore, llmModel=llmModel) response = ( supabase.table("ConversAI_ChatHistory") .insert({"username": username, "chatbotName": chatbotName, "llmModel": llmModel, "question": query, "response": output["output"]}) .execute() ) return output @app.post("/deleteChatbot") async def delete(chatbotName: str): username, chatbotName = chatbotName.split("$")[1], chatbotName.split("$")[2] supabase.table('ConversAI_ChatbotInfo').delete().eq('user_id', username).eq('chatbotname', chatbotName).execute() return deleteTable(tableName=chatbotName) @app.post("/listChatbots") async def delete(username: str): return listTables(username=username) @app.post("/getLinks") async def crawlUrl(baseUrl: str): return { "urls": getLinks(url=baseUrl, timeout=30), "source": urlparse(baseUrl).netloc } @app.post("/getCurrentCount") async def getCount(vectorstore: str): username, chatbotName = vectorstore.split("$")[1], vectorstore.split("$")[2] df = pd.DataFrame(supabase.table("ConversAI_ChatbotInfo").select("*").execute().data) return { "currentCount": df[(df['user_id'] == username) & (df['chatbotname'] == chatbotName)]['charactercount'].iloc[0] } @app.post("/getYoutubeTranscript") async def getYTTranscript(urls: list[str]): return { "output": getTranscript(urls=urls), "source": "www.youtube.com" } @app.post("/analyzeData") async def analyzeAndAnswer(query: str, file: UploadFile = File(...)): extension = file.filename.split(".")[-1] try: if extension in ["xls", "xlsx", "xlsm", "xlsb"]: df = pd.read_excel(io.BytesIO(await file.read())) response = analyzeData(query=query, dataframe=df) elif extension == "csv": df = pd.read_csv(io.BytesIO(await file.read())) response = analyzeData(query=query, dataframe=df) else: response = "INVALID FILE TYPE" return { "output": response } except: return { "output": "UNABLE TO ANSWER QUERY" } @app.post("/getChatHistory") async def chatHistory(vectorstore: str): username, chatbotName = vectorstore.split("$")[1], vectorstore.split("$")[2] response = supabase.table("ConversAI_ChatHistory").select("timestamp", "question", "response").eq("username", username).eq( "chatbotName", chatbotName).execute().data return response