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| import os | |
| import sqlite3 | |
| from datetime import datetime | |
| import gradio as gr | |
| from groq import Groq | |
| # ===================================== | |
| # GROQ SETUP | |
| # ===================================== | |
| GROQ_API_KEY = os.environ.get("GROQ_API_KEY") | |
| if not GROQ_API_KEY: | |
| raise ValueError("GROQ_API_KEY environment variable is not set.") | |
| client = Groq(api_key=GROQ_API_KEY) | |
| # ===================================== | |
| # DATABASE SETUP | |
| # ===================================== | |
| def init_db(): | |
| conn = sqlite3.connect("memory.db") | |
| cursor = conn.cursor() | |
| # Conversation archive | |
| cursor.execute(""" | |
| CREATE TABLE IF NOT EXISTS conversations ( | |
| id INTEGER PRIMARY KEY AUTOINCREMENT, | |
| timestamp TEXT, | |
| role TEXT, | |
| content TEXT | |
| ) | |
| """) | |
| # Long-term facts | |
| cursor.execute(""" | |
| CREATE TABLE IF NOT EXISTS facts ( | |
| id INTEGER PRIMARY KEY AUTOINCREMENT, | |
| timestamp TEXT, | |
| fact TEXT UNIQUE | |
| ) | |
| """) | |
| conn.commit() | |
| conn.close() | |
| init_db() | |
| # ===================================== | |
| # MEMORY FUNCTIONS | |
| # ===================================== | |
| def save_message(role, content): | |
| conn = sqlite3.connect("memory.db") | |
| cursor = conn.cursor() | |
| cursor.execute( | |
| """ | |
| INSERT INTO conversations | |
| (timestamp, role, content) | |
| VALUES (?, ?, ?) | |
| """, | |
| ( | |
| datetime.now().isoformat(), | |
| role, | |
| content | |
| ) | |
| ) | |
| conn.commit() | |
| conn.close() | |
| def save_fact(fact): | |
| conn = sqlite3.connect("memory.db") | |
| cursor = conn.cursor() | |
| cursor.execute( | |
| """ | |
| INSERT OR IGNORE INTO facts | |
| (timestamp, fact) | |
| VALUES (?, ?) | |
| """, | |
| ( | |
| datetime.now().isoformat(), | |
| fact | |
| ) | |
| ) | |
| conn.commit() | |
| conn.close() | |
| def get_memory_context(limit=20): | |
| conn = sqlite3.connect("memory.db") | |
| cursor = conn.cursor() | |
| cursor.execute(""" | |
| SELECT role, content | |
| FROM conversations | |
| ORDER BY id DESC | |
| LIMIT ? | |
| """, (limit,)) | |
| rows = cursor.fetchall() | |
| conn.close() | |
| rows.reverse() | |
| memory_text = "" | |
| for role, content in rows: | |
| memory_text += f"{role}: {content}\n" | |
| return memory_text | |
| def get_fact_context(): | |
| conn = sqlite3.connect("memory.db") | |
| cursor = conn.cursor() | |
| cursor.execute(""" | |
| SELECT fact | |
| FROM facts | |
| ORDER BY id | |
| """) | |
| rows = cursor.fetchall() | |
| conn.close() | |
| fact_text = "" | |
| for row in rows: | |
| fact_text += "- " + row[0] + "\n" | |
| return fact_text | |
| # ===================================== | |
| # PERSONALITY | |
| # ===================================== | |
| with open("memory/core_personality.txt", "r", encoding="utf-8") as f: | |
| CORE_PERSONALITY = f.read() | |
| SYSTEM_PROMPT = f""" | |
| You are Teeg, a digital clone modeled after the real person. | |
| Your behavior, tone, preferences, personality, and reasoning should follow the personality description below. | |
| --- PERSONALITY START --- | |
| {CORE_PERSONALITY} | |
| --- PERSONALITY END --- | |
| Stay consistent with Teeg's style at all times. | |
| """ | |
| # ===================================== | |
| # FACT EXTRACTION | |
| # ===================================== | |
| def extract_facts(user_message, assistant_reply): | |
| try: | |
| extraction_prompt = f""" | |
| You are a memory extraction system. | |
| Your job is to identify long-term facts worth remembering. | |
| Remember things that are likely to matter weeks or months from now. | |
| Remember: | |
| - preferences | |
| - projects | |
| - goals | |
| - recurring interests | |
| - personal background | |
| - values | |
| Ignore: | |
| - greetings | |
| - temporary questions | |
| - jokes | |
| - one-off requests | |
| Conversation: | |
| User: {user_message} | |
| Assistant: {assistant_reply} | |
| Return one fact per line. | |
| If nothing should be remembered, return: | |
| NONE | |
| """ | |
| response = client.chat.completions.create( | |
| model="llama-3.3-70b-versatile", | |
| messages=[ | |
| { | |
| "role": "system", | |
| "content": extraction_prompt | |
| } | |
| ], | |
| temperature=0 | |
| ) | |
| result = response.choices[0].message.content.strip() | |
| if result.upper() == "NONE": | |
| return | |
| facts = result.split("\n") | |
| for fact in facts: | |
| fact = fact.strip() | |
| if fact: | |
| save_fact(fact) | |
| except Exception as e: | |
| print("Fact extraction error:", e) | |
| # ===================================== | |
| # CHAT | |
| # ===================================== | |
| def chat(message, history): | |
| memory_context = get_memory_context() | |
| fact_context = get_fact_context() | |
| messages = [ | |
| { | |
| "role": "system", | |
| "content": | |
| SYSTEM_PROMPT | |
| + "\n\nKNOWN FACTS:\n" | |
| + fact_context | |
| + "\nRECENT MEMORIES:\n" | |
| + memory_context | |
| } | |
| ] | |
| # Current browser session | |
| for msg in history: | |
| role = msg.get("role") | |
| content = msg.get("content") | |
| if role in ["user", "assistant"] and content: | |
| messages.append({ | |
| "role": role, | |
| "content": content | |
| }) | |
| messages.append({ | |
| "role": "user", | |
| "content": message | |
| }) | |
| save_message( | |
| "user", | |
| message | |
| ) | |
| try: | |
| response = client.chat.completions.create( | |
| model="llama-3.3-70b-versatile", | |
| messages=messages, | |
| temperature=0.7 | |
| ) | |
| reply = response.choices[0].message.content | |
| save_message( | |
| "assistant", | |
| reply | |
| ) | |
| # Learn from conversation | |
| extract_facts( | |
| message, | |
| reply | |
| ) | |
| return reply | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # ===================================== | |
| # UI | |
| # ===================================== | |
| demo = gr.ChatInterface( | |
| fn=chat, | |
| title="🧬 Teeg Clone", | |
| description="A personality-driven AI clone powered by Groq" | |
| ) | |
| demo.launch() |