from fastapi import FastAPI
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

app = FastAPI()

# Load the Mongolian Llama model and tokenizer
model_name = "Dorjzodovsuren/Mongolian_Llama3-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

class UserInput(BaseModel):
    text: str

@app.post("/generate/")
def generate_response(user_input: UserInput):
    # Tokenize the input text
    inputs = tokenizer(user_input.text, return_tensors="pt")
    
    # Generate response
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_length=100,  # Adjust for desired response length
            num_return_sequences=1,
            temperature=0.7,  # Adjust for creativity
            top_p=0.9        # Adjust for response diversity
        )
    
    # Decode the generated text
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return {"response": response}