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srinivasansaravanamuthusrinivasansaravanamuthu
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Add project files.
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Diff for: ‎Templates/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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<title>Chatbot</title>
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<style>
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/* General styles */
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body {
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font-family: Arial, sans-serif;
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background-color: #f4f4f9;
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margin: 0;
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padding: 0;
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display: flex;
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justify-content: center;
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align-items: center;
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height: 100vh;
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}
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/* Container for the entire chat application */
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#chat-container {
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width: 400px;
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background-color: #fff;
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box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
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border-radius: 8px;
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overflow: hidden;
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}
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/* Chat window where messages are displayed */
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#chat-window {
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height: 400px;
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border-bottom: 1px solid #ccc;
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overflow-y: scroll;
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padding: 20px;
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}
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/* Input field and send button container */
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#input-container {
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display: flex;
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padding: 10px;
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background-color: #f4f4f9;
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}
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/* Input field for user messages */
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#user-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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font-size: 16px;
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}
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/* Send button */
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#send-button {
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padding: 10px 20px;
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background-color: #007bff;
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border: none;
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color: white;
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border-radius: 4px;
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cursor: pointer;
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font-size: 16px;
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}
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#send-button:hover {
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background-color: #0056b3;
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}
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/* Styling for each chat message */
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.chat-message {
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margin-bottom: 10px;
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clear: both;
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}
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/* Styling for user messages */
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.chat-message.user {
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text-align: right;
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}
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.chat-message.user .message {
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background-color: #007bff;
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color: white;
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border-radius: 15px 15px 0 15px;
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}
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/* Styling for bot messages */
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.chat-message.bot {
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text-align: left;
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}
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.chat-message.bot .message {
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background-color: #f1f1f1;
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color: black;
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border-radius: 15px 15px 15px 0;
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}
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/* Common styling for message text */
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.message {
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display: inline-block;
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padding: 10px 15px;
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max-width: 80%;
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word-wrap: break-word;
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}
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/* Styling for response time */
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.chat-message em {
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display: block;
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font-size: 0.8em;
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color: #888; /* Lighter color for less emphasis */
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}
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</style>
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</head>
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<body>
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<!-- Container for the chat interface -->
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<div id="chat-container">
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<!-- Window where chat messages appear -->
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<div id="chat-window"></div>
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<!-- Input field and send button container -->
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<div id="input-container">
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<input type="text" id="user-input" placeholder="Type a message...">
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<button id="send-button">Send</button>
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</div>
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</div>
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<script>
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// Function to append a message to the chat window
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function appendMessage(sender, text, responseTime = null) {
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const chatWindow = document.getElementById('chat-window');
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const messageElement = document.createElement('div');
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messageElement.classList.add('chat-message', sender);
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const messageText = document.createElement('div');
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messageText.classList.add('message');
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messageText.textContent = text;
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messageElement.appendChild(messageText);
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if (responseTime) {
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const timeElement = document.createElement('em');
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timeElement.textContent = `Query took: ${responseTime.toFixed(2)} seconds`;
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messageElement.appendChild(timeElement);
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}
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chatWindow.appendChild(messageElement);
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chatWindow.scrollTop = chatWindow.scrollHeight; // Auto-scroll to the bottom
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}
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document.getElementById('send-button').addEventListener('click', async () => {
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const userInput = document.getElementById('user-input').value;
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if (!userInput.trim()) return; // Do not send empty messages
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document.getElementById('user-input').value = '';
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appendMessage('user', userInput); // Append user's message
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// Send user input to the backend
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const response = await fetch('/get', {
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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({ msg: userInput }) // Convert input to JSON
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});
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// Get response data from the backend
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const data = await response.json();
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if (data.error) {
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appendMessage('bot', data.error); // Append bot's error message
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} else {
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appendMessage('bot', data.response, data.response_time); // Append bot's response and response time
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}
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});
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// Allow pressing Enter key to send message
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document.getElementById('user-input').addEventListener('keypress', (e) => {
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if (e.key === 'Enter') {
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document.getElementById('send-button').click();
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}
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});
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</script>
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</body>
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</html>

Diff for: ‎pllm.py

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import os
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import time
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from flask import Flask, render_template, request, jsonify
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
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# Initialize Flask app
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app = Flask(__name__)
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# Load the model and tokenizer once
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model_name = "EleutherAI/gpt-neo-2.7B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Add pad_token if it doesn't exist
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if tokenizer.pad_token is None:
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tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})
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# Print to verify the pad_token has been set
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print("Pad token:", tokenizer.pad_token)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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model.resize_token_embeddings(len(tokenizer)) # Resize embeddings to include the new pad_token
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# Set device to -1 to ensure it runs on CPU
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=-1)
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print("Model EleutherAI/gpt-neo-2.7B loaded. Tokenizer and Pipeline created.")
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def format_prompt(user_text):
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"""
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Format the user input to provide more context for the model.
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"""
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return f"Please provide a professional and factual answer without adding personal opinions to the following question:\nQ: {user_text}\nA:"
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def post_process_response(response):
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"""
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Post-process the model's response to ensure it is concise and appropriate.
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"""
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# Ensure the response ends conclusively and truncate to 200 characters
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if "A:" in response:
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response = response.split("A:")[1].strip()
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response = response.split("Q:")[0].strip() # Remove any additional questions
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response = response.split(". ")[0] + '.' # Take only the first sentence
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return response[:200] # Truncate to 200 characters
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#return response
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@app.route('/')
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def home():
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"""
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Render the home page.
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"""
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try:
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return render_template('index.html')
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except Exception as e:
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return str(e)
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@app.route('/get', methods=['POST'])
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def get_bot_response():
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"""
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Get the bot's response to the user's input.
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"""
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user_text = request.get_json().get('msg')
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formatted_prompt = format_prompt(user_text)
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print("User input:", user_text)
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print("Formatted prompt:", formatted_prompt)
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start_time = time.time() # Start timing the response generation
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try:
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# Tokenize the input with truncation and padding
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inputs = tokenizer(formatted_prompt, return_tensors="pt", truncation=True, padding=True, max_length=1024)
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prompt_length = inputs.input_ids.shape[1]
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# Log the length of the prompt
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print(f"Prompt length: {prompt_length}")
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# Calculate the maximum response length to stay within the 2048 token limit
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max_response_length = min(200, 2048 - prompt_length)
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if max_response_length <= 0:
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return jsonify({"error": "Input exceeds the maximum token limit."})
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# Log the max response length
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print(f"Max response length: {max_response_length}")
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# Generate the response
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response = model.generate(
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inputs['input_ids'],
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attention_mask=inputs['attention_mask'],
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max_length=prompt_length + max_response_length, # Ensure the total length doesn't exceed 2048 tokens
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min_length=prompt_length + 50, # Ensure the response is meaningful
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pad_token_id=tokenizer.pad_token_id, # Set pad token id
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no_repeat_ngram_size=2,
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num_return_sequences=1,
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temperature=0.50, # Adjusted for more coherent output
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top_p=0.80 # Adjust top_p for more relevant results
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)
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# Log the generated response
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print("Response generated")
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generated_text = tokenizer.decode(response[0], skip_special_tokens=True)
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processed_response = post_process_response(generated_text)
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end_time = time.time() # End timing the response generation
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response_time = end_time - start_time
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print(f"Time taken for response: {response_time} seconds")
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return jsonify({"response": processed_response, "response_time": response_time})
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except Exception as e:
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print("Error during model generation:", str(e))
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return jsonify({"error": str(e)})
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if __name__ == "__main__":
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app.run(debug=False)
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Diff for: ‎pllm.pyproj

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<Project DefaultTargets="Build" xmlns="http://schemas.microsoft.com/developer/msbuild/2003" ToolsVersion="4.0">
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<PropertyGroup>
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<Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
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<SchemaVersion>2.0</SchemaVersion>
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<ProjectGuid>fcf85714-8a47-4b38-a5b1-7f76a00ca3f7</ProjectGuid>
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<ProjectHome>.</ProjectHome>
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<StartupFile>pllm.py</StartupFile>
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<SearchPath>
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</SearchPath>
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<WorkingDirectory>.</WorkingDirectory>
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<OutputPath>.</OutputPath>
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<Name>pllm</Name>
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<RootNamespace>pllm</RootNamespace>
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</PropertyGroup>
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<PropertyGroup Condition=" '$(Configuration)' == 'Debug' ">
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<DebugSymbols>true</DebugSymbols>
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<EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
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</PropertyGroup>
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<PropertyGroup Condition=" '$(Configuration)' == 'Release' ">
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<DebugSymbols>true</DebugSymbols>
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<EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
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</PropertyGroup>
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<ItemGroup>
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<Compile Include="pllm.py" />
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</ItemGroup>
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<Import Project="$(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v$(VisualStudioVersion)\Python Tools\Microsoft.PythonTools.targets" />
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<!-- Uncomment the CoreCompile target to enable the Build command in
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Visual Studio and specify your pre- and post-build commands in
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the BeforeBuild and AfterBuild targets below. -->
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<!--<Target Name="CoreCompile" />-->
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<Target Name="BeforeBuild">
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</Target>
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<Target Name="AfterBuild">
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</Target>
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</Project>

Diff for: ‎pllm.sln

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Microsoft Visual Studio Solution File, Format Version 12.00
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# Visual Studio Version 17
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VisualStudioVersion = 17.6.33829.357
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MinimumVisualStudioVersion = 10.0.40219.1
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Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "pllm", "pllm.pyproj", "{FCF85714-8A47-4B38-A5B1-7F76A00CA3F7}"
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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Debug|Any CPU = Debug|Any CPU
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Release|Any CPU = Release|Any CPU
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EndGlobalSection
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GlobalSection(ProjectConfigurationPlatforms) = postSolution
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{FCF85714-8A47-4B38-A5B1-7F76A00CA3F7}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{FCF85714-8A47-4B38-A5B1-7F76A00CA3F7}.Release|Any CPU.ActiveCfg = Release|Any CPU
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE
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EndGlobalSection
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GlobalSection(ExtensibilityGlobals) = postSolution
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SolutionGuid = {CA255654-3A25-472C-81C8-592C2C724D29}
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EndGlobalSection
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EndGlobal

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