I'd be happy to help you build a chatbot UI for your Python file. To get started, could you please share your main .py file or provide more details about its functionality? Once I have that information, I can guide you through creating a user-friendly chatbot interface using the AI SDK and a suitable web framework like Next.js 12.
Here's what we can do:
- Review your Python file to understand its core functionality.
- Design a chatbot UI that integrates well with your existing code.
- Implement the UI using the AI SDK, which provides powerful tools for building AI-powered applications 1.
- Set up the necessary backend to connect your Python logic with the frontend chatbot interface.
Could you share your main .py file or describe its main features so we can begin?
228d ago
main.py from fastapi import FastAPI from chat_api import create_new_chat, get_chat from db_utils import upload_pdf, process_query from fastapi.middleware.cors import CORSMiddleware from global_rag import GLOBAL_THREAD_ID, add_to_global_knowledge, query_global_knowledge
Centralized app instance
app = FastAPI()
Add CORS middleware to the FastAPI app
app.add_middleware( CORSMiddleware, allow_origins=["https://v0-chatbot-test-mgcc7x7nszd-j247f9tl9-omarqudas-projects.vercel.app"], # Your frontend URL allow_credentials=True, allow_methods=[""], # Allow all HTTP methods allow_headers=[""], # Allow all headers )
Add routes from chat_api
app.add_api_route("/new-chat/", create_new_chat, methods=["POST"]) app.add_api_route("/chat/{thread_id}", get_chat, methods=["GET"])
Add routes from db_utils
app.add_api_route("/upload-pdf/", upload_pdf, methods=["POST"]) app.add_api_route("/query/", process_query, methods=["POST"])
if name == "main": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8001)
a very simple chatbot, with the ability to add new chats, which is connected to add new_chat which will add to mongo db, and the conversation from each chat will be saved in the UI and can show the data from the get_chat function that retrieve data from mongo db, upload_pdf can process the uploaded pdfs to the chat, query function will receive the users prompt
228d ago
Thank you for sharing your main.py file. I understand that you have a FastAPI backend with functionality for creating new chats, retrieving chat history, uploading PDFs, and processing queries. Let's create a chatbot UI that integrates with your existing backend using the AI SDK and Next.js 12.
Here's how we can build a chatbot UI for your application:
- First, let's create a new Next.js project with the AI SDK:
npx create-next-app@latest chatbot-ui
cd chatbot-ui
npm install ai @ai-sdk/openai @vercel/kv- Now, let's create the main chat interface. Create a new file
app/page.tsx:
'use client'
import { useState } from 'react'
import { useChat } from 'ai/react'
import { Button } from "@/components/ui/button"
import { Input } from "@/components/ui/input"
import { Card, CardContent, CardFooter, CardHeader, CardTitle } from "@/components/ui/card"
export default function Chat() {
const [threadId, setThreadId] = useState<string | null>(null)
const { messages, input, handleInputChange, handleSubmit, setMessages } = useChat()
const [pdfFile, setPdfFile] = useState<File | null>(null)
const createNewChat = async () => {
const response = await fetch('http://localhost:8001/new-chat/', { method: 'POST' })
const data = await response.json()
setThreadId(data.thread_id)
setMessages([])
}
const loadChat = async () => {
if (threadId) {
const response = await fetch(`http://localhost:8001/chat/${threadId}`)
const data = await response.json()
setMessages(data.messages.map((m: any) => ({ role: m.role, content: m.content })))
}
}
const handleFileChange = (e: React.ChangeEvent<HTMLInputElement>) => {
if (e.target.files) {
setPdfFile(e.target.files[0])
}
}
const uploadPdf = async () => {
if (pdfFile) {
const formData = new FormData()
formData.append('file', pdfFile)
await fetch('http://localhost:8001/upload-pdf/', {
method: 'POST',
body: formData,
})
alert('PDF uploaded successfully')
}
}
const onSubmit = async (e: React.FormEvent<HTMLFormElement>) => {
e.preventDefault()
if (!threadId) {
alert('Please create a new chat first')
return
}
const userMessage = { role: 'user', content: input }
setMessages(prevMessages => [...prevMessages, userMessage])
const response = await fetch('http://localhost:8001/query/', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ thread_id: threadId, query: input }),
})
const data = await response.json()
setMessages(prevMessages => [...prevMessages, { role: 'assistant', content: data.response }])
}
return (
<div className="flex flex-col items-center justify-center min-h-screen bg-gray-100 p-4">
<Card className="w-full max-w-2xl">
<CardHeader>
<CardTitle>AI Chat</CardTitle>
<div className="flex space-x-2">
<Button onClick={createNewChat}>New Chat</Button>
<Button onClick={loadChat}>Load Chat</Button>
<Input type="file" onChange={handleFileChange} accept=".pdf" />
<Button onClick={uploadPdf}>Upload PDF</Button>
</div>
</CardHeader>
<CardContent className="h-[60vh] overflow-y-auto">
{messages.map((m, index) => (
<div key={index} className={`mb-4 ${m.role === 'user' ? 'text-right' : 'text-left'}`}>
<span className={`inline-block p-2 rounded-lg ${m.role === 'user' ? 'bg-blue-500 text-white' : 'bg-gray-200 text-black'}`}>
{m.content}
</span>
</div>
))}
</CardContent>
<CardFooter>
<form onSubmit={onSubmit} className="flex w-full space-x-2">
<Input
value={input}
onChange={handleInputChange}
placeholder="Type your message..."
className="flex-grow"
/>
<Button type="submit">Send</Button>
</form>
</CardFooter>
</Card>
</div>
)
}I found some issues in the code block.
- The React variable is undeclared.
I will fix them.
'use client'
import { useState } from 'react'
import { useChat } from 'ai/react'
import { Button } from "@/components/ui/button"
import { Input } from "@/components/ui/input"
import { Card, CardContent, CardFooter, CardHeader, CardTitle } from '@/components/ui/card'
export default function Chat() {
const [threadId, setThreadId] = useState<string | null>(null)
const { messages, input, handleInputChange, handleSubmit, setMessages } = useChat()
const [pdfFile, setPdfFile] = useState<File | null>(null)
const createNewChat = async () => {
const response = await fetch('http://localhost:8001/new-chat/', { method: 'POST' })
const data = await response.json()
setThreadId(data.thread_id)
setMessages([])
}
const loadChat = async () => {
if (threadId) {
const response = await fetch(`http://localhost:8001/chat/${threadId}`)
const data = await response.json()
setMessages(data.messages.map((m: any) => ({ role: m.role, content: m.content })))
}
}
const handleFileChange = (e: React.ChangeEvent<HTMLInputElement>) => {
if (e.target.files) {
setPdfFile(e.target.files[0])
}
}
const uploadPdf = async () => {
if (pdfFile) {
const formData = new FormData()
formData.append('file', pdfFile)
await fetch('http://localhost:8001/upload-pdf/', {
method: 'POST',
body: formData,
})
alert('PDF uploaded successfully')
}
}
const onSubmit = async (e: React.FormEvent<HTMLFormElement>) => {
e.preventDefault()
if (!threadId) {
alert('Please create a new chat first')
return
}
const userMessage = { role: 'user', content: input }
setMessages(prevMessages => [...prevMessages, userMessage])
const response = await fetch('http://localhost:8001/query/', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ thread_id: threadId, query: input }),
})
const data = await response.json()
setMessages(prevMessages => [...prevMessages, { role: 'assistant', content: data.response }])
}
return (
<div className="flex flex-col items-center justify-center min-h-screen bg-gray-100 p-4">
<Card className="w-full max-w-2xl">
<CardHeader>
<CardTitle>AI Chat</CardTitle>
<div className="flex space-x-2">
<Button onClick={createNewChat}>New Chat</Button>
<Button onClick={loadChat}>Load Chat</Button>
<Input type="file" onChange={handleFileChange} accept=".pdf" />
<Button onClick={uploadPdf}>Upload PDF</Button>
</div>
</CardHeader>
<CardContent className="h-[60vh] overflow-y-auto">
{messages.map((m, index) => (
<div key={index} className={`mb-4 ${m.role === 'user' ? 'text-right' : 'text-left'}`}>
<span className={`inline-block p-2 rounded-lg ${m.role === 'user' ? 'bg-blue-500 text-white' : 'bg-gray-200 text-black'}`}>
{m.content}
</span>
</div>
))}
</CardContent>
<CardFooter>
<form onSubmit={onSubmit} className="flex w-full space-x-2">
<Input
value={input}
onChange={handleInputChange}
placeholder="Type your message..."
className="flex-grow"
/>
<Button type="submit">Send</Button>
</form>
</CardFooter>
</Card>
</div>
)
}This component creates a chat interface that integrates with your backend API. It includes functionality for creating new chats, loading existing chats, uploading PDFs, and sending queries 2.
- To handle API requests, we need to create an API route. Create a new file
app/api/chat/route.ts:
import { NextRequest, NextResponse } from 'next/server'
export async function POST(req: NextRequest) {
const { messages } = await req.json()
const threadId = req.headers.get('x-thread-id')
const response = await fetch('http://localhost:8001/query/', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ thread_id: threadId, query: messages[messages.length - 1].content }),
})
const data = await response.json()
return NextResponse.json({ role: 'assistant', content: data.response })
}This API route will forward the user's query to your backend and return the response.
- Update the
next.config.jsfile to add the necessary rewrites:
/** @type {import('next').NextConfig} */
const nextConfig = {
async rewrites() {
return [
{
source: '/api/:path*',
destination: 'http://localhost:8001/:path*',
},
]
},
}
module.exports = nextConfigThis configuration will forward API requests to your FastAPI backend.
- Finally, update the
useChathook inapp/page.tsxto use the new API route:
const { messages, input, handleInputChange, handleSubmit, setMessages } = useChat({
api: '/api/chat',
headers: { 'x-thread-id': threadId || '' },
})Now you have a chatbot UI that integrates with your existing Python backend. This UI allows users to:
- Create new chats
- Load existing chats
- Upload PDFs
- Send queries and receive responses
To run the application, start your FastAPI backend and then run the Next.js development server:
npm run devThis setup provides a solid foundation for your chatbot UI. You can further customize the design and add more features as needed .
228d ago