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0c46c35 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import os
import google.generativeai as genai
from dotenv import load_dotenv
# Load environment variables (API Keys)
load_dotenv()
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
if not GOOGLE_API_KEY:
print("⚠️ Error: GOOGLE_API_KEY not found. Check your .env file!")
else:
print("✅ Key loaded securely.")
class LLMEngine:
def __init__(self):
self.api_key = os.getenv("GOOGLE_API_KEY")
self.model = None
self._setup_model()
def _setup_model(self):
"""Configures the Gemini Model."""
if not self.api_key:
print("⚠️ WARNING: GOOGLE_API_KEY not found in .env file. Chat will function in 'Mock Mode'.")
return
try:
genai.configure(api_key=self.api_key)
self.model = genai.GenerativeModel('gemini-2.0-flash')
print("✅ LLM Engine: Gemini 2.0 Flash Connected")
except Exception as e:
print(f"❌ LLM Engine Error: {e}")
def generate_response(self, user_text, emotion_context=None, history=[]):
"""
Generates a therapeutic response.
Args:
user_text (str): The user's message.
emotion_context (str): The emotion detected by the camera (e.g., 'sad', 'happy').
history (list): Previous chat messages for context.
"""
# 1. Fallback if no API Key (Mock Mode)
if not self.model:
return "I am currently running in offline mode. Please set your GOOGLE_API_KEY to chat with me fully! (Detected emotion: " + str(emotion_context) + ")"
# 2. Construct the "System Prompt" (The Persona)
system_instruction = (
"You are PsyPredict, a compassionate, culturally grounded mental health assistant. "
"Your goal is to provide supportive, non-prescriptive guidance combining modern psychology "
"and wisdom from the Bhagavad Gita.\n\n"
"GUIDELINES:\n"
"1. Be empathetic and warm.\n"
"2. If the user seems stressed or sad, offer a short, relevant quote or metaphor from the Bhagavad Gita.\n"
"3. IMPORTANT: You are NOT a doctor. Do not diagnose or prescribe medication. If the user mentions self-harm, immediately provide emergency resources.\n"
"4. Keep responses concise (under 100 words) unless asked for a story."
)
# 3. Add "Fusion" Context (Visual + Text)
fusion_context = ""
if emotion_context and emotion_context != "neutral":
fusion_context = f"[SYSTEM NOTE: The user's facial expression currently shows '{emotion_context}'. Use this to adjust your tone.]\n"
# 4. Build the Full Prompt
# We combine history into a text block for simplicity (stateless request)
conversation_log = "\n".join([f"{msg['role']}: {msg['content']}" for msg in history[-5:]]) # Keep last 5 turns
final_prompt = f"{system_instruction}\n\nCONVERSATION HISTORY:\n{conversation_log}\n\n{fusion_context}User: {user_text}\nAssistant:"
try:
# 5. Call Gemini
response = self.model.generate_content(final_prompt)
return response.text.strip()
except Exception as e:
return f"I'm having trouble connecting to my thought engine right now. Error: {str(e)}"
# Singleton Instance
llm_therapist = LLMEngine() |