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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"
}
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