Create app.py
Browse files
app.py
ADDED
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from fastapi import FastAPI
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from fastapi.responses import HTMLResponse
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from transformers import AutoTokenizer
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from pydantic import BaseModel
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class Message(BaseModel):
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content: str
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token: int
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class System(BaseModel):
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sys_prompt: str
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app = FastAPI()
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@app.get("/, response_class=HTMLResponse")
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def greet_json():
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return '''<!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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<title>FastAPI Chatbot</title>
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<style>
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body {
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font-family: Arial, sans-serif;
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margin: 0;
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padding: 0;
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background-color: #f4f4f9;
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display: flex;
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flex-direction: column;
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align-items: center;
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}
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.container {
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margin-top: 60px;
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width: 90%;
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margin-bottom: 20px;
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}
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.system-prompt {
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display: flex;
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justify-content: space-between;
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margin-bottom: 20px;
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}
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.system-prompt input {
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width: 70%;
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padding: 10px;
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border: 1px solid #ccc;
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border-radius: 4px;
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}
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.system-prompt button {
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padding: 10px 20px;
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border: none;
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background-color: #007bff;
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color: white;
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border-radius: 4px;
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cursor: pointer;
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}
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.system-prompt button:hover {
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background-color: #0056b3;
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}
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.chatbox {
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background-color: #fff;
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border-radius: 8px;
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box-shadow: 0 2px 5px rgba(0,0,0,0.1);
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padding: 20px;
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height: 400px;
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overflow-y: auto;
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}
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.message {
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margin-bottom: 10px;
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}
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.user {
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text-align: right;
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color: #007bff;
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}
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.assistant {
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text-align: left;
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color: #333;
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}
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.input-section {
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display: flex;
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width: 100%;
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margin-top: 20px;
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}
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.input-section input {
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flex: 1;
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padding: 10px;
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border: 1px solid #ccc;
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border-radius: 4px;
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margin-right: 10px;
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}
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.input-section input:focus {
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outline: none;
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border-color: #007bff;
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}
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.input-section button {
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padding: 10px 20px;
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border: none;
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background-color: #28a745;
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color: white;
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border-radius: 4px;
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cursor: pointer;
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}
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.input-section button:hover {
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background-color: #218838;
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}
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.token-input {
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width: 100px;
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margin-left: 10px;
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}
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</style>
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</head>
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<body>
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<div class="container">
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<div class="system-prompt">
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<input type="text" id="systemPrompt" placeholder="Enter System Prompt">
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<button onclick="setSystemPromptAndClearHistory()">Set prompt and clear history</button>
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</div>
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<div class="chatbox" id="chatbox"></div>
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<div class="input-section">
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<input type="text" id="userInput" placeholder="Type your message here...">
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<input type="number" id="tokenLength" class="token-input" value="50" placeholder="Tokens">
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<button onclick="sendMessage()">Send</button>
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</div>
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</div>
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<script>
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async function setSystemPromptAndClearHistory() {
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const systemPrompt = document.getElementById('systemPrompt').value;
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const response = await fetch('/setSystemPrompt', {
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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({ sys_prompt: systemPrompt })
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});
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if (response.ok) {
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document.getElementById('chatbox').innerHTML = '';
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alert('System prompt set and history cleared.');
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} else {
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alert('Failed to set system prompt.');
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}
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}
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async function sendMessage() {
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const userInput = document.getElementById('userInput').value;
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const tokenLength = parseInt(document.getElementById('tokenLength').value);
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if (!userInput || isNaN(tokenLength)) {
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alert('Please enter a valid message and token length.');
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return;
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}
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const chatbox = document.getElementById('chatbox');
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const userMessage = document.createElement('div');
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userMessage.className = 'message user';
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userMessage.textContent = userInput;
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chatbox.appendChild(userMessage);
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const response = await 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({ content: userInput, token: tokenLength })
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});
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if (response.ok) {
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const data = await response.json();
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const assistantMessage = document.createElement('div');
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assistantMessage.className = 'message assistant';
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assistantMessage.textContent = data.response;
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chatbox.appendChild(assistantMessage);
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document.getElementById('userInput').value = '';
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} else {
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alert('Failed to get response from server.');
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}
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chatbox.scrollTop = chatbox.scrollHeight;
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}
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</script>
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</body>
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</html>'''
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llm = Llama.from_pretrained(
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repo_id="Qwen/Qwen2.5-1.5B-Instruct-GGUF",
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filename="qwen2.5-1.5b-instruct-q8_0.gguf",
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verbose=False
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)
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
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messages = []
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@app.post("/chat")
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def chat(req: Message):
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messages.append({"role": "user", "content": req.content})
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text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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output = llm(text,max_tokens=req.token,echo=False)
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response = output['choices'][0]['text']
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messages.append({"role": "assistant", "content": response})
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return {"response": response_text}
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@app.post("/setSystemPrompt")
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def chat(req: System):
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messages.append({"role": "user", "content": req.sys_prompt})
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return {"response": "System has been set"}
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@app.post("/clear_chat")
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def clear_chat():
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global conversation_history
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conversation_history = []
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return {"message": "Chat history cleared"}
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