from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
import logging

# Initialize FastAPI app
app = FastAPI()

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Load the GPT-2 model and tokenizer
model_id = "gpt2"  # Use GPT-2
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Create a text generation pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Define the pre-prompt
PRE_PROMPT = "You are a helpful virtual assistant. Answer the user's question clearly and concisely."

# Define request body schema
class TextGenerationRequest(BaseModel):
    prompt: str
    max_new_tokens: int = 50  # Reduce this for faster responses
    temperature: float = 0.7  # Lower for more deterministic outputs
    top_k: int = 50
    top_p: float = 0.9
    do_sample: bool = True

# Define API endpoint
@app.post("/generate-text")
async def generate_text(request: TextGenerationRequest):
    try:
        logger.info("Generating text...")
        
        # Combine the pre-prompt and user's prompt
        combined_input = f"{PRE_PROMPT} {request.prompt}"
        
        # Generate text using the pipeline
        outputs = pipe(
            combined_input,  # Use the combined input
            max_new_tokens=request.max_new_tokens,
            temperature=request.temperature,
            top_k=request.top_k,
            top_p=request.top_p,
            do_sample=request.do_sample,
            return_full_text=False  # Exclude the input prompt from the output
        )
        return {"generated_text": outputs[0]["generated_text"]}
    except Exception as e:
        logger.error(f"Error generating text: {e}")
        raise HTTPException(status_code=500, detail=str(e))

# Add a root endpoint for health checks
@app.get("/test")
async def root():
    return {"message": "API is running!"}