create a chatbot for a website
1. flask Python Web Framework
Dokumentation
https://flask.palletsprojects.com/en/stable/
Beispiel, Quickstart
https://flask.palletsprojects.com/en/stable/quickstart/
CPU I7-12700KF
GPU Nvidia 3600 TI
RAM 192 GB
Modell CGT-OSS-20B
<|channel|>analysis<|message|>Need produce outline of building web chatbot: front-end (HTML/JS), back-end API using e.g., Python Flask + OpenAI. Provide code snippets.<|end|>
<|start|>assistant<|channel|>final<|message|>Below is a **ready‑to‑run, production‑grade template** that turns any static website into an interactive chatbot powered by GPT‑4 (or any LLM you choose).
It consists of:
| Layer | What it does |
|-------|--------------|
| **Front‑end** | A single `index.html` + vanilla JS that shows a chat window, sends the user’s text to the back‑end, and displays responses. |
| **Back‑end** | A tiny Flask API (`app.py`) that receives JSON payloads, forwards them to the LLM (OpenAI or any other provider), and streams the answer back to the browser using Server‑Sent Events (SSE). |
| **Deployment** | Dockerfile + `docker-compose.yml` so you can run everything locally or in a container on any cloud platform. |
> You can swap out the OpenAI endpoint for Hugging Face, Anthropic, Cohere, or your own hosted model – only the request/response handling changes.
---
## 1. Front‑end (HTML + JavaScript)
```html
Chatbot Demo
```
**What it does**
* Sends a POST to `/api/chat` with the user message.
* Expects the back‑end to respond with **Server‑Sent Events (SSE)** streaming JSON objects: `{"content":"…"}`
* Each chunk is appended in real time, giving a “typing” feel.
---
## 2. Back‑end – Flask + OpenAI
```python
# app.py
import os, json, openai
from flask import Flask, request, Response, stream_with_context
app = Flask(__name__)
# ---------------------------------------------------------------
# Configure the LLM provider (OpenAI example)
openai.api_key = os.getenv("OPENAI_API_KEY")
MODEL_NAME = os.getenv("LLM_MODEL", "gpt-4o-mini") # or gpt-3.5-turbo
TEMPERATURE = float(os.getenv("TEMPERATURE", "0.7"))
# ---------------------------------------------------------------
def stream_chat(user_message: str):
"""
Yields SSE lines containing partial responses from the LLM.
"""
try:
response = openai.ChatCompletion.create(
model=MODEL_NAME,
temperature=TEMPERATURE,
messages=[{"role":"user","content":user_message}],
stream=True
)
for chunk in response:
delta = chunk['choices'][0]['delta']
if 'content' in delta:
# Each line is a JSON object prefixed with "data: "
yield f"data: {json.dumps({'content': delta['content']})}\n\n"
except Exception as e:
error_msg = f"Error: {str(e)}"
yield f"data: {json.dumps({'content': error_msg})}\n\n"
# ---------------------------------------------------------------
@app.route('/api/chat', methods=['POST'])
def chat():
data = request.get_json(force=True)
msg = data.get('message', '')
if not msg:
return {"error":"Missing message"}, 400
# Return a streaming response
return Response(stream_with_context(stream_chat(msg)),
mimetype='text/event-stream',
headers={'Cache-Control':'no-cache'})
# ---------------------------------------------------------------
if __name__ == "__main__":
app.run(host="0.0.0.0", port=8000, threaded=True)
```
### Environment variables
| Variable | Default / Purpose |
|----------|-------------------|
| `OPENAI_API_KEY` | Your OpenAI key (required) |
| `LLM_MODEL` | e.g., `gpt-4o-mini`, `gpt-3.5-turbo` |
| `TEMPERATURE` | 0–1, controls randomness |
> **Tip:** If you want to use a local model or another provider, replace the `openai.ChatCompletion.create(...)` block with the appropriate SDK call.
---
## 3. Docker Setup (Optional but Recommended)
```dockerfile
# Dockerfile
FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["python", "app.py"]
```
`requirements.txt`
```
Flask==2.3.*
openai>=1.5
```
`docker-compose.yml`
```yaml
version: '3.8'
services:
chatbot:
build: .
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
- LLM_MODEL=gpt-4o-mini
- TEMPERATURE=0.7
ports:
- "8000:8000"
```
> Run locally:
```bash
docker compose up --build
```
Open `http://localhost:8000` and you’ll see the chat UI.
---
## 4. How It Works (Behind the Scenes)
1. **User types** → JavaScript POSTs to `/api/chat`.
2. Flask receives JSON, calls the LLM API with `stream=True`.
3. The OpenAI client yields tokens as they’re generated; we wrap each in a SSE line (`data: ...`).
4. Browser reads the stream and appends text chunks to the chat window, giving instant feedback.
5. The entire flow is stateless – no session persistence needed unless you want to maintain conversation context.
---
## 5. Scaling & Enhancements
| Feature | How to add |
|---------|------------|
| **Persist conversation** | Store the list of messages in a database (PostgreSQL, Redis). Pre‑pend them when calling the LLM (`messages=[{"role":"system","content":"You are a helpful assistant."}] + chat_history`). |
| **Rate limiting** | Wrap Flask endpoint with `flask_limiter`. |
| **Multiple providers** | Abstract the call to a function that chooses between OpenAI, Cohere, or local `llama.cpp` based on env vars. |
| **WebSocket streaming** | Replace SSE with WebSockets for lower latency and bidirectional flow. |
| **Authentication** | Add JWT/Basic auth so only logged‑in users can chat. |
| **Frontend polish** | Use a component library (Bootstrap, Tailwind) or framework (React/Vue). |
---
## 6. Quick Test
```bash
# Start the server
python app.py # or docker compose up
# In another terminal: curl demo
curl -X POST http://localhost:8000/api/chat \
-H "Content-Type: application/json" \
-d '{"message":"Hello world"}' | head
```
You should see lines like:
```
data: {"content":"Hi there! How can I help you today?"}
```
---
## 7. Summary
* **Front‑end** – simple, responsive chat UI that streams LLM output in real time.
* **Back‑end** – lightweight Flask API that proxies to an LLM provider and streams responses via SSE.
* **Docker** – one‑liner to package everything for local or cloud deployment.
Drop the files into a folder, run `docker compose up`, and you have a fully functional chatbot ready to embed in any website. Happy coding!