Upload 2 files
Browse files- capx.html +175 -0
- capybara_dating_app_api.py +156 -0
capx.html
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| 1 |
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<!DOCTYPE html>
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| 2 |
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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>Capybara Dating App - Personality Quiz</title>
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<link href="https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600&display=swap" rel="stylesheet">
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<style>
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body {
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font-family: 'Poppins', sans-serif;
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max-width: 800px;
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margin: 0 auto;
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padding: 20px;
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background-color: #f0e6d2;
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color: #5a3921;
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line-height: 1.6;
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}
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.container {
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background-color: #fff;
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border-radius: 15px;
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padding: 30px;
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box-shadow: 0 4px 6px rgba(0,0,0,0.1);
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}
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h1 {
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color: #ff6b35;
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text-align: center;
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font-size: 2.5em;
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margin-bottom: 20px;
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}
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#question, #result {
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font-size: 1.2em;
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| 32 |
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margin-bottom: 20px;
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background-color: #fffaf0;
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padding: 20px;
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border-radius: 8px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.1);
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}
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#answer {
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width: 100%;
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padding: 15px;
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margin-bottom: 20px;
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| 42 |
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border: 2px solid #ff6b35;
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| 43 |
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border-radius: 8px;
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| 44 |
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font-size: 1em;
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transition: border-color 0.3s ease;
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}
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#answer:focus {
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outline: none;
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border-color: #ff4500;
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}
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button {
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padding: 12px 24px;
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font-size: 1em;
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background-color: #ff6b35;
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color: white;
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border: none;
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border-radius: 25px;
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cursor: pointer;
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transition: background-color 0.3s ease, transform 0.1s ease;
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}
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button:hover {
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background-color: #ff4500;
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transform: translateY(-2px);
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}
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button:active {
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transform: translateY(0);
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}
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#history {
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margin-top: 30px;
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}
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.history-item {
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background-color: #fffaf0;
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padding: 15px;
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margin-bottom: 15px;
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border-radius: 8px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.1);
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}
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.capybara-img {
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max-width: 200px;
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display: block;
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margin: 0 auto 20px;
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border-radius: 50%;
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}
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.quiz-progress {
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text-align: center;
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margin-bottom: 20px;
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font-weight: 600;
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color: #5a3921;
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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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<img src="https://placekitten.com/200/200" alt="Cute Capybara" class="capybara-img">
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<h1>Capybara Dating App Personality Quiz</h1>
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<div id="quiz">
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<div class="quiz-progress">Question <span id="current-question">1</span> of 5</div>
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<div id="question"></div>
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<textarea id="answer" rows="4" placeholder="Type your answer here... Be as honest as a capybara!"></textarea>
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<button id="submit">Next Question</button>
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</div>
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<div id="result" style="display: none;"></div>
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<div id="history"></div>
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</div>
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<script>
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const API_URL = 'http://localhost:5000';
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let questions = [];
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let answers = [];
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let currentQuestion = '';
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async function getNextQuestion() {
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const response = await fetch(`${API_URL}/generate_question`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({ previous_questions: questions, previous_answers: answers }),
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});
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const data = await response.json();
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return data.question;
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}
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async function classifyPersonality() {
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const response = await fetch(`${API_URL}/classify_personality`, {
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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({ questions, answers }),
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});
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const data = await response.json();
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return data;
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}
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async function startQuiz() {
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currentQuestion = await getNextQuestion();
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document.getElementById('question').textContent = currentQuestion;
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}
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document.getElementById('submit').addEventListener('click', async () => {
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const answer = document.getElementById('answer').value.trim();
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if (answer) {
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questions.push(currentQuestion);
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answers.push(answer);
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document.getElementById('history').innerHTML += `
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<div class="history-item">
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<p><strong>Q: ${currentQuestion}</strong></p>
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<p>A: ${answer}</p>
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</div>
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`;
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document.getElementById('answer').value = '';
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if (questions.length < 5) {
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currentQuestion = await getNextQuestion();
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document.getElementById('question').textContent = currentQuestion;
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document.getElementById('current-question').textContent = questions.length + 1;
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} else {
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const result = await classifyPersonality();
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document.getElementById('quiz').style.display = 'none';
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document.getElementById('result').style.display = 'block';
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document.getElementById('result').innerHTML = `
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<h2>Your Capybara Dating Personality:</h2>
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<p>${result.personality}</p>
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<p>Your personality summary has been saved to: ${result.summary_file}</p>
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<button onclick="location.reload()">Take Quiz Again</button>
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`;
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}
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}
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});
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startQuiz();
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| 173 |
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</script>
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</body>
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</html>
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capybara_dating_app_api.py
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| 1 |
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from flask import Flask, request, jsonify
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| 2 |
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from flask_cors import CORS
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| 3 |
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import logging
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from typing import List
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| 5 |
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import math
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| 6 |
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import faiss
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| 7 |
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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| 8 |
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from langchain_experimental.generative_agents import GenerativeAgent, GenerativeAgentMemory
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from langchain.retrievers import TimeWeightedVectorStoreRetriever
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| 10 |
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from langchain_community.vectorstores import FAISS
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from langchain_community.docstore.in_memory import InMemoryDocstore
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| 12 |
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from datetime import datetime
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| 13 |
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import os
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| 14 |
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app = Flask(__name__)
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CORS(app)
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# Set up logging and LLM
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logging.basicConfig(level=logging.ERROR)
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LLM = ChatOpenAI(max_tokens=1500)
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def relevance_score_fn(score: float) -> float:
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| 23 |
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return 1.0 - score / math.sqrt(2)
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| 24 |
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| 25 |
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def create_new_memory_retriever():
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| 26 |
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embeddings_model = OpenAIEmbeddings()
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| 27 |
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embedding_size = 1536
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| 28 |
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index = faiss.IndexFlatL2(embedding_size)
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| 29 |
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vectorstore = FAISS(
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| 30 |
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embeddings_model.embed_query,
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| 31 |
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index,
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| 32 |
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InMemoryDocstore({}),
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| 33 |
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{},
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| 34 |
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relevance_score_fn=relevance_score_fn,
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| 35 |
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)
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| 36 |
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return TimeWeightedVectorStoreRetriever(
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| 37 |
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vectorstore=vectorstore, other_score_keys=["importance"], k=15
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| 38 |
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)
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| 39 |
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| 40 |
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PERSONALITY_TYPES = {
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| 41 |
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"Romantic Idealist": "Seeks deep emotional connections and believes in soulmates",
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| 42 |
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"Adventure Seeker": "Thrives on new experiences and loves to explore",
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| 43 |
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"Intellectual Companion": "Values deep conversations and mental stimulation",
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| 44 |
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"Social Butterfly": "Energized by social interactions and meeting new people",
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| 45 |
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"Nurturing Partner": "Caring, supportive, and focused on emotional well-being",
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| 46 |
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"Ambitious Go-Getter": "Driven by goals and seeks a partner with similar ambitions",
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| 47 |
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"Creative Spirit": "Expresses themselves through art and values originality",
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| 48 |
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"Steady Reliable": "Consistent, dependable, and values stability in relationships"
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| 49 |
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}
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| 50 |
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| 51 |
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QUESTION_TEMPLATES = [
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| 52 |
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"What's your idea of a perfect date?",
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| 53 |
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"How do you handle conflicts in a relationship?",
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| 54 |
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"What's the most spontaneous thing you've ever done?",
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| 55 |
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"How important is personal space to you in a relationship?",
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| 56 |
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"What's a deal-breaker for you in a potential partner?",
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| 57 |
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"How do you show affection to someone you care about?",
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| 58 |
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"What role does humor play in your relationships?",
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| 59 |
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"How do you balance your personal goals with a romantic relationship?",
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| 60 |
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"What's the most important quality you look for in a partner?",
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| 61 |
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"How do you envision your ideal future with a partner?"
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| 62 |
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]
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| 63 |
+
|
| 64 |
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class QuestionGeneratorAgent(GenerativeAgent):
|
| 65 |
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def __init__(self, name: str, age: int, traits: str):
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| 66 |
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memory = GenerativeAgentMemory(
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| 67 |
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llm=LLM,
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| 68 |
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memory_retriever=create_new_memory_retriever(),
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| 69 |
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verbose=False,
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| 70 |
+
reflection_threshold=5
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| 71 |
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)
|
| 72 |
+
super().__init__(name=name, age=age, traits=traits, status="active", memory=memory, llm=LLM)
|
| 73 |
+
|
| 74 |
+
def generate_question(self, previous_questions: List[str], previous_answers: List[str]) -> str:
|
| 75 |
+
context = "\n".join([f"Q: {q}\nA: {a}" for q, a in zip(previous_questions, previous_answers)])
|
| 76 |
+
templates = "\n".join([f"- {template}" for template in QUESTION_TEMPLATES])
|
| 77 |
+
prompt = f"""As a dating app personality quiz, generate a new, interesting question based on the following context and question templates:
|
| 78 |
+
|
| 79 |
+
Previous Questions and Answers:
|
| 80 |
+
{context}
|
| 81 |
+
|
| 82 |
+
Question Templates:
|
| 83 |
+
{templates}
|
| 84 |
+
|
| 85 |
+
Create a unique question inspired by these templates, but don't repeat them exactly. The question should be engaging, thought-provoking, and reveal aspects of the person's dating personality. Ensure it's different from the previous questions.
|
| 86 |
+
|
| 87 |
+
Generate only the question, nothing else."""
|
| 88 |
+
|
| 89 |
+
response = self.generate_dialogue_response(prompt)
|
| 90 |
+
return response[1].strip('"') # Remove quotation marks from the response
|
| 91 |
+
|
| 92 |
+
class PersonalityClassifierAgent(GenerativeAgent):
|
| 93 |
+
def __init__(self, name: str, age: int, traits: str):
|
| 94 |
+
memory = GenerativeAgentMemory(
|
| 95 |
+
llm=LLM,
|
| 96 |
+
memory_retriever=create_new_memory_retriever(),
|
| 97 |
+
verbose=False,
|
| 98 |
+
reflection_threshold=5
|
| 99 |
+
)
|
| 100 |
+
super().__init__(name=name, age=age, traits=traits, status="active", memory=memory, llm=LLM)
|
| 101 |
+
|
| 102 |
+
def classify_personality(self, questions: List[str], answers: List[str]) -> dict:
|
| 103 |
+
context = "\n".join([f"Q: {q}\nA: {a}" for q, a in zip(questions, answers)])
|
| 104 |
+
personality_types = "\n".join([f"- {type}: {desc}" for type, desc in PERSONALITY_TYPES.items()])
|
| 105 |
+
prompt = f"""As a dating app personality classifier, categorize the person's dating personality based on these questions and answers:
|
| 106 |
+
|
| 107 |
+
{personality_types}
|
| 108 |
+
|
| 109 |
+
Questions and Answers:
|
| 110 |
+
{context}
|
| 111 |
+
|
| 112 |
+
Provide the personality type and a brief explanation for your choice. Also, suggest potential compatible personality types for dating. Format your response as:
|
| 113 |
+
Personality Type: [chosen type]
|
| 114 |
+
Explanation: [your explanation]
|
| 115 |
+
Compatible Types: [list of compatible types]
|
| 116 |
+
Dating Advice: [brief advice based on their personality type]
|
| 117 |
+
"""
|
| 118 |
+
response = self.generate_dialogue_response(prompt)
|
| 119 |
+
personality_summary = response[1]
|
| 120 |
+
|
| 121 |
+
# Save the personality summary to a text file
|
| 122 |
+
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
| 123 |
+
filename = f"personality_summary_{timestamp}.txt"
|
| 124 |
+
with open(filename, "w") as f:
|
| 125 |
+
f.write("Capybara Dating App - Personality Summary\n")
|
| 126 |
+
f.write("=========================================\n\n")
|
| 127 |
+
f.write("Questions and Answers:\n")
|
| 128 |
+
for q, a in zip(questions, answers):
|
| 129 |
+
f.write(f"Q: {q}\n")
|
| 130 |
+
f.write(f"A: {a}\n\n")
|
| 131 |
+
f.write("Personality Classification:\n")
|
| 132 |
+
f.write(personality_summary)
|
| 133 |
+
|
| 134 |
+
return {"personality": personality_summary, "summary_file": filename}
|
| 135 |
+
|
| 136 |
+
question_agent = QuestionGeneratorAgent("CapybaraQuestionBot", 25, "curious, romantic, empathetic")
|
| 137 |
+
classifier_agent = PersonalityClassifierAgent("CapybaraMatchBot", 30, "insightful, compassionate, intuitive")
|
| 138 |
+
|
| 139 |
+
@app.route('/generate_question', methods=['POST'])
|
| 140 |
+
def generate_question():
|
| 141 |
+
data = request.json
|
| 142 |
+
previous_questions = data.get('previous_questions', [])
|
| 143 |
+
previous_answers = data.get('previous_answers', [])
|
| 144 |
+
question = question_agent.generate_question(previous_questions, previous_answers)
|
| 145 |
+
return jsonify({'question': question})
|
| 146 |
+
|
| 147 |
+
@app.route('/classify_personality', methods=['POST'])
|
| 148 |
+
def classify_personality():
|
| 149 |
+
data = request.json
|
| 150 |
+
questions = data.get('questions', [])
|
| 151 |
+
answers = data.get('answers', [])
|
| 152 |
+
result = classifier_agent.classify_personality(questions, answers)
|
| 153 |
+
return jsonify(result)
|
| 154 |
+
|
| 155 |
+
if __name__ == '__main__':
|
| 156 |
+
app.run(debug=True)
|