Capx
/

Capybara

Model card Files Files and versions Community

Alyosha11 commited on Jul 23

Commit

7b449d7

•

1 Parent(s): 4b70306

Upload 2 files

Browse files

Files changed (2) hide show

capx.html +175 -0
capybara_dating_app_api.py +156 -0

capx.html ADDED Viewed

	@@ -0,0 +1,175 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Capybara Dating App - Personality Quiz</title>
+    <link href="https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600&display=swap" rel="stylesheet">
+    <style>
+        body {
+            font-family: 'Poppins', sans-serif;
+            max-width: 800px;
+            margin: 0 auto;
+            padding: 20px;
+            background-color: #f0e6d2;
+            color: #5a3921;
+            line-height: 1.6;
+        }
+        .container {
+            background-color: #fff;
+            border-radius: 15px;
+            padding: 30px;
+            box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+        }
+        h1 {
+            color: #ff6b35;
+            text-align: center;
+            font-size: 2.5em;
+            margin-bottom: 20px;
+        }
+        #question, #result {
+            font-size: 1.2em;
+            margin-bottom: 20px;
+            background-color: #fffaf0;
+            padding: 20px;
+            border-radius: 8px;
+            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+        }
+        #answer {
+            width: 100%;
+            padding: 15px;
+            margin-bottom: 20px;
+            border: 2px solid #ff6b35;
+            border-radius: 8px;
+            font-size: 1em;
+            transition: border-color 0.3s ease;
+        }
+        #answer:focus {
+            outline: none;
+            border-color: #ff4500;
+        }
+        button {
+            padding: 12px 24px;
+            font-size: 1em;
+            background-color: #ff6b35;
+            color: white;
+            border: none;
+            border-radius: 25px;
+            cursor: pointer;
+            transition: background-color 0.3s ease, transform 0.1s ease;
+        }
+        button:hover {
+            background-color: #ff4500;
+            transform: translateY(-2px);
+        }
+        button:active {
+            transform: translateY(0);
+        }
+        #history {
+            margin-top: 30px;
+        }
+        .history-item {
+            background-color: #fffaf0;
+            padding: 15px;
+            margin-bottom: 15px;
+            border-radius: 8px;
+            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+        }
+        .capybara-img {
+            max-width: 200px;
+            display: block;
+            margin: 0 auto 20px;
+            border-radius: 50%;
+        }
+        .quiz-progress {
+            text-align: center;
+            margin-bottom: 20px;
+            font-weight: 600;
+            color: #5a3921;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <img src="https://placekitten.com/200/200" alt="Cute Capybara" class="capybara-img">
+        <h1>Capybara Dating App Personality Quiz</h1>
+        <div id="quiz">
+            <div class="quiz-progress">Question <span id="current-question">1</span> of 5</div>
+            <div id="question"></div>
+            <textarea id="answer" rows="4" placeholder="Type your answer here... Be as honest as a capybara!"></textarea>
+            <button id="submit">Next Question</button>
+        </div>
+        <div id="result" style="display: none;"></div>
+        <div id="history"></div>
+    </div>
+    <script>
+        const API_URL = 'http://localhost:5000';
+        let questions = [];
+        let answers = [];
+        let currentQuestion = '';
+        async function getNextQuestion() {
+            const response = await fetch(`${API_URL}/generate_question`, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({ previous_questions: questions, previous_answers: answers }),
+            });
+            const data = await response.json();
+            return data.question;
+        }
+        async function classifyPersonality() {
+            const response = await fetch(`${API_URL}/classify_personality`, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({ questions, answers }),
+            });
+            const data = await response.json();
+            return data;
+        }
+        async function startQuiz() {
+            currentQuestion = await getNextQuestion();
+            document.getElementById('question').textContent = currentQuestion;
+        }
+        document.getElementById('submit').addEventListener('click', async () => {
+            const answer = document.getElementById('answer').value.trim();
+            if (answer) {
+                questions.push(currentQuestion);
+                answers.push(answer);
+                document.getElementById('history').innerHTML += `
+                    <div class="history-item">
+                        <p><strong>Q: ${currentQuestion}</strong></p>
+                        <p>A: ${answer}</p>
+                    </div>
+                `;
+                document.getElementById('answer').value = '';
+                if (questions.length < 5) {
+                    currentQuestion = await getNextQuestion();
+                    document.getElementById('question').textContent = currentQuestion;
+                    document.getElementById('current-question').textContent = questions.length + 1;
+                } else {
+                    const result = await classifyPersonality();
+                    document.getElementById('quiz').style.display = 'none';
+                    document.getElementById('result').style.display = 'block';
+                    document.getElementById('result').innerHTML = `
+                        <h2>Your Capybara Dating Personality:</h2>
+                        <p>${result.personality}</p>
+                        <p>Your personality summary has been saved to: ${result.summary_file}</p>
+                        <button onclick="location.reload()">Take Quiz Again</button>
+                    `;
+                }
+            }
+        });
+        startQuiz();
+    </script>
+</body>
+</html>

capybara_dating_app_api.py ADDED Viewed

	@@ -0,0 +1,156 @@

+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import logging
+from typing import List
+import math
+import faiss
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_experimental.generative_agents import GenerativeAgent, GenerativeAgentMemory
+from langchain.retrievers import TimeWeightedVectorStoreRetriever
+from langchain_community.vectorstores import FAISS
+from langchain_community.docstore.in_memory import InMemoryDocstore
+from datetime import datetime
+import os
+app = Flask(__name__)
+CORS(app)
+# Set up logging and LLM
+logging.basicConfig(level=logging.ERROR)
+LLM = ChatOpenAI(max_tokens=1500)
+def relevance_score_fn(score: float) -> float:
+    return 1.0 - score / math.sqrt(2)
+def create_new_memory_retriever():
+    embeddings_model = OpenAIEmbeddings()
+    embedding_size = 1536
+    index = faiss.IndexFlatL2(embedding_size)
+    vectorstore = FAISS(
+        embeddings_model.embed_query,
+        index,
+        InMemoryDocstore({}),
+        {},
+        relevance_score_fn=relevance_score_fn,
+    )
+    return TimeWeightedVectorStoreRetriever(
+        vectorstore=vectorstore, other_score_keys=["importance"], k=15
+    )
+PERSONALITY_TYPES = {
+    "Romantic Idealist": "Seeks deep emotional connections and believes in soulmates",
+    "Adventure Seeker": "Thrives on new experiences and loves to explore",
+    "Intellectual Companion": "Values deep conversations and mental stimulation",
+    "Social Butterfly": "Energized by social interactions and meeting new people",
+    "Nurturing Partner": "Caring, supportive, and focused on emotional well-being",
+    "Ambitious Go-Getter": "Driven by goals and seeks a partner with similar ambitions",
+    "Creative Spirit": "Expresses themselves through art and values originality",
+    "Steady Reliable": "Consistent, dependable, and values stability in relationships"
+}
+QUESTION_TEMPLATES = [
+    "What's your idea of a perfect date?",
+    "How do you handle conflicts in a relationship?",
+    "What's the most spontaneous thing you've ever done?",
+    "How important is personal space to you in a relationship?",
+    "What's a deal-breaker for you in a potential partner?",
+    "How do you show affection to someone you care about?",
+    "What role does humor play in your relationships?",
+    "How do you balance your personal goals with a romantic relationship?",
+    "What's the most important quality you look for in a partner?",
+    "How do you envision your ideal future with a partner?"
+]
+class QuestionGeneratorAgent(GenerativeAgent):
+    def __init__(self, name: str, age: int, traits: str):
+        memory = GenerativeAgentMemory(
+            llm=LLM,
+            memory_retriever=create_new_memory_retriever(),
+            verbose=False,
+            reflection_threshold=5
+        )
+        super().__init__(name=name, age=age, traits=traits, status="active", memory=memory, llm=LLM)
+    def generate_question(self, previous_questions: List[str], previous_answers: List[str]) -> str:
+        context = "\n".join([f"Q: {q}\nA: {a}" for q, a in zip(previous_questions, previous_answers)])
+        templates = "\n".join([f"- {template}" for template in QUESTION_TEMPLATES])
+        prompt = f"""As a dating app personality quiz, generate a new, interesting question based on the following context and question templates:
+Previous Questions and Answers:
+{context}
+Question Templates:
+{templates}
+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.
+Generate only the question, nothing else."""
+        response = self.generate_dialogue_response(prompt)
+        return response[1].strip('"')  # Remove quotation marks from the response
+class PersonalityClassifierAgent(GenerativeAgent):
+    def __init__(self, name: str, age: int, traits: str):
+        memory = GenerativeAgentMemory(
+            llm=LLM,
+            memory_retriever=create_new_memory_retriever(),
+            verbose=False,
+            reflection_threshold=5
+        )
+        super().__init__(name=name, age=age, traits=traits, status="active", memory=memory, llm=LLM)
+    def classify_personality(self, questions: List[str], answers: List[str]) -> dict:
+        context = "\n".join([f"Q: {q}\nA: {a}" for q, a in zip(questions, answers)])
+        personality_types = "\n".join([f"- {type}: {desc}" for type, desc in PERSONALITY_TYPES.items()])
+        prompt = f"""As a dating app personality classifier, categorize the person's dating personality based on these questions and answers:
+{personality_types}
+Questions and Answers:
+{context}
+Provide the personality type and a brief explanation for your choice. Also, suggest potential compatible personality types for dating. Format your response as:
+Personality Type: [chosen type]
+Explanation: [your explanation]
+Compatible Types: [list of compatible types]
+Dating Advice: [brief advice based on their personality type]
+"""
+        response = self.generate_dialogue_response(prompt)
+        personality_summary = response[1]
+        # Save the personality summary to a text file
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"personality_summary_{timestamp}.txt"
+        with open(filename, "w") as f:
+            f.write("Capybara Dating App - Personality Summary\n")
+            f.write("=========================================\n\n")
+            f.write("Questions and Answers:\n")
+            for q, a in zip(questions, answers):
+                f.write(f"Q: {q}\n")
+                f.write(f"A: {a}\n\n")
+            f.write("Personality Classification:\n")
+            f.write(personality_summary)
+        return {"personality": personality_summary, "summary_file": filename}
+question_agent = QuestionGeneratorAgent("CapybaraQuestionBot", 25, "curious, romantic, empathetic")
+classifier_agent = PersonalityClassifierAgent("CapybaraMatchBot", 30, "insightful, compassionate, intuitive")
+@app.route('/generate_question', methods=['POST'])
+def generate_question():
+    data = request.json
+    previous_questions = data.get('previous_questions', [])
+    previous_answers = data.get('previous_answers', [])
+    question = question_agent.generate_question(previous_questions, previous_answers)
+    return jsonify({'question': question})
+@app.route('/classify_personality', methods=['POST'])
+def classify_personality():
+    data = request.json
+    questions = data.get('questions', [])
+    answers = data.get('answers', [])
+    result = classifier_agent.classify_personality(questions, answers)
+    return jsonify(result)
+if __name__ == '__main__':
+    app.run(debug=True)