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import io
from functions import *
from PyPDF2 import PdfReader
import pandas as pd
from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from langchain_community.document_loaders import UnstructuredURLLoader
app = FastAPI(title = "ConversAI", root_path = "/api/v1")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@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):
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]
newCount = currentCount + len(text)
if newCount < 400000:
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("/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)
if newCount < 400000:
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("/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)
if newCount < 400000:
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 = chatbotName.split("-")[1], chatbotName.split("-")[2]
df = pd.DataFrame(client.table("ConversAI_ChatbotInfo").select("*").execute().data)
return {
"currentCount": df[(df['username'] == username) & (df['chatbotname'] == chatbotName)]['charactercount'].iloc[0]
} |