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from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from transformers import AutoTokenizer
from pydantic import BaseModel
from llama_cpp import Llama
import time

class Message(BaseModel):
    content: str
    token: int

class System(BaseModel):
    sys_prompt: str
    

app = FastAPI()

@app.get("/", response_class=HTMLResponse)
def greet_json():
    return '''<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>FastAPI Chatbot</title>
    <style>
        body {
            font-family: Arial, sans-serif;
            margin: 0;
            padding: 0;
            background-color: #f4f4f9;
            display: flex;
            flex-direction: column;
            align-items: center;
        }
        .container {
            margin-top: 60px;
            width: 80%;
            margin-bottom: 20px;
        }
        .system-prompt {
            display: flex;
            justify-content: space-between;
            margin-bottom: 20px;
            padding: 16px;
            border: 1px solid #ccc;
            border-radius: 4px;
        }
        .system-prompt input {
            width: 70%;
            padding: 10px;
            border: 1px solid #ccc;
            border-radius: 4px;
        }
        .system-prompt button {
            padding: 10px 20px;
            border: none;
            background-color: #007bff;
            color: white;
            border-radius: 4px;
            cursor: pointer;
        }
        .system-prompt button:hover {
            background-color: #0056b3;
        }
        .chatbox {
            background-color: #fff;
            border-radius: 8px;
            box-shadow: 0 2px 5px rgba(0,0,0,0.1);
            padding: 20px;
            height: 300px;
            overflow-y: auto;
            border: 1px solid #ccc;
        }
        .message {
            margin-bottom: 10px;
        }
        .user {
            text-align: right;
            color: #050505;
        }
        .assistant {
            text-align: left;
            color: #333;
        }
        .input-section {
            display: flex;
            width: 100%;
            margin-top: 20px;
        }
        .input-section input {
            flex: 1;
            padding: 10px;
            border: 1px solid #ccc;
            border-radius: 4px;
            margin-right: 10px;
        }
        .input-section button {
            padding: 10px 20px;
            border: none;
            background-color: #283fa7;
            color: white;
            border-radius: 4px;
            cursor: pointer;
            margin-left: 50%;
            width: 150px;
        }
        .input-section button:hover {
            background-color: #112388;
        }
        .token-input {
            width: 25px;
            margin-left: 10px;
        }
        #userInput{
            height: 100px;
            width: 100%;
            margin-top: 6px;
        }
        #userInput:focus {
            outline: none;
            border-color: #007bff;
        }
    </style>
</head>
<body>
    <div class="container">
        <div class="system-prompt">
            <input type="text" id="systemPrompt" placeholder="Enter System Prompt">
            <button onclick="setSystemPromptAndClearHistory()">Set prompt and clear history</button>
        </div>
        <div class="chatbox" id="chatbox"></div>
        <textarea type="text" id="userInput" placeholder="Type your message here..."></textarea>

        <div class="input-section">
            <input type="number" id="tokenLength" class="token-input" value="50" placeholder="Tokens">
            <button onclick="sendMessage()">Send</button>
        </div>
    </div>

    <script>
        async function setSystemPromptAndClearHistory() {
            const systemPrompt = document.getElementById('systemPrompt').value;
            const response = await fetch('/setSystemPrompt', {
                method: 'POST',
                headers: {
                    'Content-Type': 'application/json'
                },
                body: JSON.stringify({ sys_prompt: systemPrompt })
            });
            if (response.ok) {
                document.getElementById('chatbox').innerHTML = '';
                alert('System prompt set and history cleared.');
            } else {
                alert('Failed to set system prompt.');
            }
        }

        async function sendMessage() {
            const userInput = document.getElementById('userInput').value;
            document.getElementById("userInput").reset();
            const tokenLength = parseInt(document.getElementById('tokenLength').value);
            if (!userInput || isNaN(tokenLength)) {
                alert('Please enter a valid message and token length.');
                return;
            }

            const chatbox = document.getElementById('chatbox');
            const userMessage = document.createElement('div');
            userMessage.className = 'message user';
            userMessage.textContent = userInput;
            chatbox.appendChild(userMessage);

            const response = await fetch('/chat', {
                method: 'POST',
                headers: {
                    'Content-Type': 'application/json'
                },
                body: JSON.stringify({ content: userInput, token: tokenLength })
            });

            if (response.ok) {
                const data = await response.json();
                const assistantMessage = document.createElement('div');
                assistantMessage.className = 'message assistant';
                assistantMessage.textContent = data.response;
                chatbox.appendChild(assistantMessage);
                document.getElementById('userInput').value = '';
            } else {
                alert('Failed to get response from server.');
            }

            chatbox.scrollTop = chatbox.scrollHeight;
        }
    </script>
</body>
</html>'''

llm = Llama.from_pretrained(
    repo_id="Qwen/Qwen2.5-1.5B-Instruct-GGUF",
    filename="qwen2.5-1.5b-instruct-q8_0.gguf",
    verbose=True,n_ctx=1024
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")

messages = []

@app.post("/chat")
def chat(req: Message):
    a = time.time()
    messages.append({"role": "user", "content": req.content})
    text = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    output = llm(text,max_tokens=req.token,echo=False)
    response = output['choices'][0]['text']
    messages.append({"role": "assistant", "content": response})
    b = time.time()
    return {"response": response, "time": b-a}


@app.post("/setSystemPrompt")
def chat(req: System):
    global conversation_history
    conversation_history = []
    messages.append({"role": "user", "content": req.sys_prompt})
    return {"response": "System has been set"}