init template
Browse files- Dockerfile +32 -0
- main.py +33 -0
- requirements.txt +8 -0
- static/index.html +22 -0
- static/script.js +52 -0
- static/style.css +48 -0
Dockerfile
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FROM python:3.8
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WORKDIR /code
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# Upgrade pip
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RUN pip install --no-cache-dir --upgrade pip
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COPY ./requirements.txt /code/requirements.txt
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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# download model from transformer
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COPY . .
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# Set up a new user named "user" with user ID 1000
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RUN useradd -m -u 1000 user
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# Switch to the "user" user
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USER user
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# Set home to the user's home directory
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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# Set the working directory to the user's home directory
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WORKDIR $HOME/app
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME/app
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
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main.py
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from fastapi import FastAPI, Depends, HTTPException, Query
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from typing import List
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from fastapi.responses import HTMLResponse
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from fastapi.staticfiles import StaticFiles
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app = FastAPI()
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app.mount("/static", StaticFiles(directory="static"), name="static")
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@app.get("/", response_class=HTMLResponse)
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async def read_root():
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with open("static/index.html", "r") as f:
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content = f.read()
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return HTMLResponse(content=content)
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# Initialize model and tokenizer
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# tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-14B-Chat-int4")
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# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-14B-Chat-int4").eval()
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@app.post("/chat/")
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def chat(user_input: str, api_key: str):
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# Here you can validate the API key, e.g., check if it exists in your database
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# If the API key is not valid, raise an HTTPException
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# if not validate_api_key(api_key):
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# raise HTTPException(status_code=400, detail="Invalid API key")
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# Tokenize the user input and get model's response
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# input_ids = tokenizer.encode(user_input, return_tensors="pt")
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# output = model.generate(input_ids)
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# response = tokenizer.decode(output[0], skip_special_tokens=True)
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return {"response": f"user input: {user_input}, api_key: {api_key}"}
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requirements.txt
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openai==0.28.0
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pandas==2.0.3
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tqdm~=4.66.1
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annoy==1.17.3
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fastapi
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uvicorn
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transformers
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torch
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static/index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<link rel="stylesheet" href="/static/style.css">
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<script src="/static/script.js" defer></script>
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<title>ASK-ANRG</title>
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</head>
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<body>
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<div class="container">
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<h1>ASK-ANRG</h1>
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<label for="api-key-input">API Key:</label>
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<input type="text" id="api-key-input" placeholder="Enter your API key...">
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<div class="chat-box" id="chat-box">
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<!-- Chat messages will be displayed here -->
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</div>
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<input type="text" id="user-input" placeholder="Type your message...">
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<button id="send-btn">Send</button>
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</div>
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</body>
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</html>
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static/script.js
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const sendBtn = document.getElementById('send-btn');
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const userInput = document.getElementById('user-input');
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const apiKeyInput = document.getElementById('api-key-input');
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const chatBox = document.getElementById('chat-box');
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sendBtn.addEventListener('click', () => {
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const message = userInput.value.trim();
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const apiKey = apiKeyInput.value.trim();
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console.log("message: ", message)
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console.log("apiKey: ", apiKey)
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if (message && apiKey) {
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// Display user's message immediately
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const userMessageDiv = document.createElement('div');
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userMessageDiv.className = 'user-message';
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userMessageDiv.innerText = message;
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chatBox.appendChild(userMessageDiv);
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scrollToBottom();
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// Call FastAPI backend to get response
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fetch('/chat/', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json'
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},
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body: JSON.stringify({user_input: message, api_key: apiKey})
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})
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.then(response => {
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if (!response.ok) {
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throw new Error('Network response was not ok');
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}
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return response.json();
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})
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.then(data => {
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// Display chatbot's response
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const botMessageDiv = document.createElement('div');
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botMessageDiv.className = 'bot-message';
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botMessageDiv.innerText = data.response;
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chatBox.appendChild(botMessageDiv);
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scrollToBottom();
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})
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.catch(error => {
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console.log('There was a problem with the fetch operation:', error.message);
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});
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userInput.value = ''; // Clear the input field
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}
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});
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function scrollToBottom() {
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chatBox.scrollTop = chatBox.scrollHeight;
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}
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static/style.css
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body {
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font-family: Arial, sans-serif;
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background-color: #f4f4f4;
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margin: 0;
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padding: 0;
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}
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.container {
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max-width: 600px;
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margin: 50px auto;
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background-color: #fff;
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padding: 20px;
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border-radius: 5px;
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}
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.chat-box {
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border: 1px solid #e0e0e0;
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padding: 20px;
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height: 300px;
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overflow-y: scroll;
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margin-bottom: 10px;
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}
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button {
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background-color: #007BFF;
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color: #fff;
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border: none;
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padding: 10px 15px;
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border-radius: 5px;
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cursor: pointer;
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}
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.user-message, .bot-message {
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margin: 10px 0;
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padding: 10px;
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border-radius: 5px;
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}
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.user-message {
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background-color: #e0e0e0;
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align-self: flex-end;
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}
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.bot-message {
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background-color: #007BFF;
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color: white;
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align-self: flex-start;
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}
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