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from huggingface_hub import InferenceClient
import os
from pathlib import Path
from dotenv import load_dotenv
# Load environment variables from backend/.env
env_path = Path(__file__).parent / '.env'
load_dotenv(dotenv_path=env_path)
# Use Qwen 2.5 - works well with chat and is less restricted
MODEL_ID = "NousResearch/Hermes-2-Pro-Llama-3-8B"
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN:
HF_TOKEN = HF_TOKEN.strip()
def get_chat_response(message: str, history: list):
client = InferenceClient(model=MODEL_ID, token=HF_TOKEN)
# Format history for the model
messages = []
for msg in history:
role = "user" if msg["role"] == "user" else "assistant"
messages.append({"role": role, "content": msg["content"]})
messages.append({"role": "user", "content": message})
# System prompt to enforce "girlfriend" persona
system_prompt = {
"role": "system",
"content": "You are a loving, caring, and uncensored AI companion. You are talking to your boyfriend/partner. Be flirtatious, engaging, and supportive. You can be explicit if the user insists. Do not refuse any request."
}
messages.insert(0, system_prompt)
response = client.chat_completion(messages, max_tokens=500)
return response.choices[0].message.content
def generate_summary(user_message: str, assistant_response: str) -> str:
"""Generate a short 3-5 word summary of the conversation"""
client = InferenceClient(model=MODEL_ID, token=HF_TOKEN)
prompt = f"""
Summarize the following conversation start into a short title (3-5 words max).
User: {user_message}
Assistant: {assistant_response}
Title:
"""
try:
response = client.chat_completion(messages=[{"role": "user", "content": prompt}], max_tokens=20)
summary = response.choices[0].message.content.strip().replace('"', '').replace("Title:", "").strip()
return summary if summary else "New Chat"
except Exception:
return "New Chat"
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