app.py

import io
from functions import *
from PyPDF2 import PdfReader
import pandas as pd
from fastapi import FastAPI, File, UploadFile
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from langchain_community.document_loaders import UnstructuredURLLoader
from src.api.speech_api import speech_translator_router

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 signup(username: str, password: str):
 response = createUser(username=username, password=password)
 return response


@app.post("/login")
async def login(username: str, password: str):
 response = matchPassword(username=username, password=password)
 return response


@app.post("/newChatbot")
async def newChatbot(chatbotName: str, username: str):
 currentBotCount = len(listTables(username=username)["output"])
 limit = client.table("ConversAI_UserConfig").select("chatbotLimit").eq("username", username).execute().data[0][
 "chatbotLimit"]
 if currentBotCount >= int(limit):
 return {
 "output": "CHATBOT LIMIT EXCEEDED"
 }
 client.table("ConversAI_ChatbotInfo").insert({"username": username, "chatbotname": chatbotName}).execute()
 chatbotName = f"convai-{username}-{chatbotName}"
 return createTable(tablename=chatbotName)


@app.post("/addPDF")
async def addPDFData(vectorstore: str, pdf: UploadFile = File(...)):
 pdf = await pdf.read()
 reader = PdfReader(io.BytesIO(pdf))
 text = ""
 for page in reader.pages:
 text += page.extract_text()
 username, chatbotname = vectorstore.split("-")[1], vectorstore.split("-")[2]
 df = pd.DataFrame(client.table("ConversAI_ChatbotInfo").select("*").execute().data)
 currentCount = df[(df["username"] == username) & (df["chatbotname"] == chatbotname)]["charactercount"].iloc[0]
 limit = client.table("ConversAI_UserConfig").select("tokenLimit").eq("username", username).execute().data[0][
 "tokenLimit"]
 newCount = currentCount + len(text)
 if newCount < int(limit):
 client.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("username", username).eq(
 "chatbotname", chatbotname).execute()
 return addDocuments(text=text, vectorstore=vectorstore)
 else:
 return {
 "output": "DOCUMENT EXCEEDING LIMITS, PLEASE TRY WITH A SMALLER DOCUMENT."
 }


@app.post("/scanAndReturnText")
async def returnText(pdf: UploadFile = File(...)):
 pdf = await pdf.read()
 text = getTextFromImagePDF(pdfBytes=pdf)
 return text


@app.post("/addText")
async def addText(vectorstore: str, text: str):
 username, chatbotname = vectorstore.split("-")[1], vectorstore.split("-")[2]
 df = pd.DataFrame(client.table("ConversAI_ChatbotInfo").select("*").execute().data)
 currentCount = df[(df["username"] == username) & (df["chatbotname"] == chatbotname)]["charactercount"].iloc[0]
 newCount = currentCount + len(text)
 limit = client.table("ConversAI_UserConfig").select("tokenLimit").eq("username", username).execute().data[0][
 "tokenLimit"]
 if newCount < int(limit):
 client.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("username", username).eq(
 "chatbotname", chatbotname).execute()
 return addDocuments(text=text, vectorstore=vectorstore)
 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 addText(addQaPair: AddQAPair):
 username, chatbotname = addQaPair.vectorstore.split("-")[1], addQaPair.vectorstore.split("-")[2]
 df = pd.DataFrame(client.table("ConversAI_ChatbotInfo").select("*").execute().data)
 currentCount = df[(df["username"] == username) & (df["chatbotname"] == chatbotname)]["charactercount"].iloc[0]
 qa = f"QUESTION: {addQaPair.question}\tANSWER: {addQaPair.answer}"
 newCount = currentCount + len(qa)
 limit = client.table("ConversAI_UserConfig").select("tokenLimit").eq("username", username).execute().data[0][
 "tokenLimit"]
 if newCount < int(limit):
 client.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("username", username).eq(
 "chatbotname", chatbotname).execute()
 return addDocuments(text=qa, vectorstore=addQaPair.vectorstore)
 else:
 return {
 "output": "WEBSITE EXCEEDING LIMITS, PLEASE TRY WITH A SMALLER DOCUMENT."
 }


@app.post("/addWebsite")
async def addWebsite(vectorstore: str, websiteUrls: list[str]):
 urls = websiteUrls
 loader = UnstructuredURLLoader(urls=urls)
 docs = loader.load()
 text = "\n\n".join(
 [f"Metadata:\n{docs[doc].metadata} \nPage Content:\n {docs[doc].page_content}" for doc in range(len(docs))])
 username, chatbotname = vectorstore.split("-")[1], vectorstore.split("-")[2]
 df = pd.DataFrame(client.table("ConversAI_ChatbotInfo").select("*").execute().data)
 currentCount = df[(df["username"] == username) & (df["chatbotname"] == chatbotname)]["charactercount"].iloc[0]
 newCount = currentCount + len(text)
 limit = client.table("ConversAI_UserConfig").select("tokenLimit").eq("username", username).execute().data[0][
 "tokenLimit"]
 if newCount < int(limit):
 client.table("ConversAI_ChatbotInfo").update({"charactercount": str(newCount)}).eq("username", username).eq(
 "chatbotname", chatbotname).execute()
 return addDocuments(text=text, vectorstore=vectorstore)
 else:
 return {
 "output": "WEBSITE EXCEEDING LIMITS, PLEASE TRY WITH A SMALLER DOCUMENT."
 }


@app.post("/answerQuery")
async def answerQuestion(query: str, vectorstore: str, llmModel: str = "llama3-70b-8192"):
 return answerQuery(query=query, vectorstore=vectorstore, llmModel=llmModel)


@app.post("/deleteChatbot")
async def delete(chatbotName: str):
 username, chatbotName = chatbotName.split("-")[1], chatbotName.split("-")[2]
 client.table('ConversAI_ChatbotInfo').delete().eq('username', 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)
 }


@app.post("/getCurrentCount")
async def getCount(vectorstore: str):
 username, chatbotName = vectorstore.split("-")[1], vectorstore.split("-")[2]
 df = pd.DataFrame(client.table("ConversAI_ChatbotInfo").select("*").execute().data)
 return {
 "currentCount": df[(df['username'] == username) & (df['chatbotname'] == chatbotName)]['charactercount'].iloc[0]
 }


@app.post("/getYoutubeTranscript")
async def getYTTranscript(urls: str):
 return getTranscript(urls=urls)


@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"
 }