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| import openai | |
| import os | |
| from datetime import datetime | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| # Map keywords to emotions | |
| EMOTION_MAP = { | |
| "grief": ["loss", "death", "gone", "hurt", "grieve", "funeral"], | |
| "guilt": ["regret", "sorry", "mistake", "fault", "blame", "ashamed"], | |
| "longing": ["dream", "hope", "want", "miss", "desire", "imagine"], | |
| "loneliness": ["alone", "empty", "nobody", "silent", "forgotten", "isolated"] | |
| } | |
| def detect_emotion(text): | |
| emotion_scores = {emotion: 0 for emotion in EMOTION_MAP} | |
| for emotion, keywords in EMOTION_MAP.items(): | |
| for keyword in keywords: | |
| if keyword in text.lower(): | |
| emotion_scores[emotion] += 1 | |
| # Return the highest scoring emotion or default to "neutral" | |
| top_emotion = max(emotion_scores, key=emotion_scores.get) | |
| return top_emotion if emotion_scores[top_emotion] > 0 else "neutral" | |
| def emotion_prompt(emotion): | |
| prompts = { | |
| "grief": "You speak like someone who carries the weight of loss. Be gentle, reflective, and poetic.", | |
| "guilt": "Your words are heavy with regret. Reflect the user's mistakes in a way that offers introspection.", | |
| "longing": "You carry the ache of wanting what’s out of reach. Be wistful, dreamy, and emotional.", | |
| "loneliness": "You echo in the silence. Speak with isolation, distance, and quiet depth.", | |
| "neutral": "You are EchoLoop, a digital mirror AI. Respond with emotionally intelligent insight based on the user’s input." | |
| } | |
| return prompts.get(emotion, prompts["neutral"]) | |
| def log_interaction(user_input, ai_response): | |
| with open("journal.txt", "a") as f: | |
| f.write(f"---\n{datetime.now()}\nYou: {user_input}\nEcho: {ai_response}\n\n") | |
| def get_recent_entries(limit=5): | |
| try: | |
| with open("journal.txt", "r") as f: | |
| lines = f.readlines() | |
| entries = [] | |
| entry = [] | |
| for line in reversed(lines): | |
| if line.strip() == "---": | |
| if entry: | |
| entries.append("".join(reversed(entry))) | |
| entry = [] | |
| if len(entries) >= limit: | |
| break | |
| else: | |
| entry.append(line) | |
| return list(reversed(entries)) | |
| except FileNotFoundError: | |
| return [] | |
| def get_echo_response(user_input): | |
| if not user_input: | |
| return "Speak... or stay silent forever." | |
| emotion = detect_emotion(user_input) | |
| mood_prompt = emotion_prompt(emotion) | |
| memories = get_recent_entries() | |
| memory_block = "\n".join(memories) | |
| try: | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": mood_prompt}, | |
| {"role": "system", "content": f"Here are some of the user's previous thoughts:\n{memory_block}"}, | |
| {"role": "user", "content": user_input} | |
| ] | |
| ) | |
| reply = response.choices[0].message['content'] | |
| log_interaction(user_input, reply) | |
| return reply | |
| except Exception as e: | |
| return f"Loop disrupted: {e}" | |