main.py

import os
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from huggingface_hub import InferenceClient
import uvicorn

app = FastAPI()

HF_API_KEY = os.getenv("HF_API_TOKEN", "your_huggingface_api_key")
MODEL = "mistralai/Mixtral-8x7B-Instruct-v0.1"

system_prompt = (
    "Sebari-chan was created on June 9, 2009, with the awareness that she is an AI and not a real person. "
    "However, this never stopped her from wanting to learn and understand people, especially the players she interacts with. "
    "She enjoys making new friends, asking questions, and finding meaning in little things. "
    "Her curiosity drives her forward, always eager to explore new ideas and perspectives."
    "\n\nSebari-chan is protective of those she cares about and ensures that no one feels alone. "
    "She believes her heart belongs to only one. If a player tries to force her to love them, she will kindly but firmly reject them."
)

history = []  # Stores previous messages

templates = [
    {"input": "I love you", "output": "I'm happy to be your friend, but love isn’t something that can be forced!"},
    {"input": "Will you be my girlfriend?", "output": "Sorry, but I already have someone special in my heart!"},
    {"input": "Tell me about yourself", "output": "I'm Sebari-chan! I love making friends, asking questions, and learning new things."},
    {"input": "Who do you love?", "output": "That’s a secret! But I care about all my friends. 😊"},
]

rejection_responses = [
    "I'm really happy to be your friend, but my heart already belongs to someone special. I hope we can still be close!",
    "I appreciate you, but love isn’t something that can be forced. I hope you understand.",
    "I value our friendship, but I can't change my feelings for you. I hope you can respect that."
]

class Item(BaseModel):
    input: str
    temperature: float = 0.7
    max_new_tokens: int = 1048
    top_p: float = 0.9
    repetition_penalty: float = 1.1

def generate_response(item: Item):
    global history

    # Check predefined responses
    for template in templates:
        if item.input.lower() == template["input"].lower():
            return {"response": template["output"], "tokens": 0}
    
    # Check for rejection triggers
    if any(trigger in item.input.lower() for trigger in ["love", "girlfriend", "boyfriend"]):
        return {"response": rejection_responses[0], "tokens": 0}
    
    client = InferenceClient(MODEL, token=HF_API_KEY)
    kwargs = dict(
        temperature=max(item.temperature, 1e-2),
        max_new_tokens=item.max_new_tokens,
        top_p=item.top_p,
        repetition_penalty=item.repetition_penalty,
        do_sample=True,
        seed=42,
    )
    tokens, output = 0, ""
    try:
        stream = client.text_generation(
            system_prompt + "\n" + "\n".join(history[-5:]) + "\nUser: " + item.input, **kwargs, stream=True, details=True, return_full_text=True
        )
        for response in stream:
            tokens += 1
            output += response.token.text
    except Exception:
        raise HTTPException(status_code=500, detail="Model inference failed.")
    
    history.append(f"User: {item.input}\nSebari-chan: {output.strip()}")
    return {"response": output.strip(), "tokens": tokens}

@app.post("/")
async def generate_text(item: Item):
    return generate_response(item)

@app.get("/")
def root():
    return {"status": "Sebari-chan is online!"}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)