app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer
from contextlib import asynccontextmanager
import json
import logging
import os
import asyncio

# Set up logging to stdout only
logging.basicConfig(
    level=logging.DEBUG,
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    handlers=[
        logging.StreamHandler()  # Log to stdout
    ]
)
logger = logging.getLogger(__name__)

# Global variables for model and tokenizer
model = None
tokenizer = None
model_load_status = "not_loaded"

# Define model path and fallback
model_path = "/app/fine-tuned-construction-llm"
fallback_model = "distilgpt2"

# Asynchronous function to load model in the background
async def load_model_background():
    global model, tokenizer, model_load_status
    try:
        if os.path.isdir(model_path):
            logger.info(f"Loading local model from {model_path}")
            model = AutoModelForCausalLM.from_pretrained(model_path, local_files_only=True)
            tokenizer = AutoTokenizer.from_pretrained(model_path, local_files_only=True)
            model_load_status = "local_model_loaded"
        else:
            logger.info(f"Model directory not found: {model_path}. Using pre-trained model: {fallback_model}")
            model = AutoModelForCausalLM.from_pretrained(fallback_model)
            tokenizer = AutoTokenizer.from_pretrained(fallback_model)
            model_load_status = "fallback_model_loaded"
        logger.info("Model and tokenizer loaded successfully")
    except Exception as e:
        logger.error(f"Failed to load model or tokenizer: {str(e)}")
        model_load_status = f"failed: {str(e)}"

# Lifespan event handler to manage startup and shutdown
@asynccontextmanager
async def lifespan(app: FastAPI):
    logger.debug("FastAPI application starting")
    # Start the background task for model loading
    asyncio.create_task(load_model_background())
    yield
    logger.debug("FastAPI application shutting down")

# Initialize FastAPI app with lifespan handler
app = FastAPI(lifespan=lifespan)

# Define input model for validation
class CoachingInput(BaseModel):
    role: str
    project_id: str
    milestones: str
    reflection_log: str

@app.get("/")
async def root():
    logger.debug("Root endpoint accessed")
    return {"message": "Supervisor AI Coach is running"}

@app.get("/health")
async def health_check():
    logger.debug("Health endpoint accessed")
    return {
        "status": "healthy" if model_load_status in ["local_model_loaded", "fallback_model_loaded"] else "starting",
        "model_load_status": model_load_status
    }

@app.post("/generate_coaching")
async def generate_coaching(data: CoachingInput):
    logger.debug("Generate coaching endpoint accessed")
    if model is None or tokenizer is None:
        logger.warning("Model or tokenizer not loaded")
        # Return a static response if the model isn't loaded yet
        response_json = {
            "checklist": ["Inspect safety equipment", "Review milestone progress"],
            "tips": ["Prioritize team communication", "Check weather updates"],
            "quote": "Every step forward counts!"
        }
        return response_json

    try:
        # Prepare input text
        input_text = (
            f"Role: {data.role}, Project: {data.project_id}, "
            f"Milestones: {data.milestones}, Reflection: {data.reflection_log}"
        )
        
        # Tokenize input
        inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
        
        # Generate output
        outputs = model.generate(
            inputs["input_ids"],
            max_length=200,
            num_return_sequences=1,
            no_repeat_ngram_size=2,
            do_sample=True,
            temperature=0.7
        )
        
        # Decode and parse response
        response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # Since distilgpt2 may not output JSON, parse the response manually or use fallback
        if not response_text.startswith("{"):
            checklist = ["Inspect safety equipment", "Review milestone progress"]
            tips = ["Prioritize team communication", "Check weather updates"]
            quote = "Every step forward counts!"
            response_json = {"checklist": checklist, "tips": tips, "quote": quote}
            logger.warning("Model output is not JSON, using default response")
        else:
            try:
                response_json = json.loads(response_text)
            except json.JSONDecodeError:
                response_json = {
                    "checklist": ["Inspect safety equipment", "Review milestone progress"],
                    "tips": ["Prioritize team communication", "Check weather updates"],
                    "quote": "Every step forward counts!"
                }
                logger.warning("Failed to parse model output as JSON, using default response")
        
        return response_json
    
    except Exception as e:
        logger.error(f"Error generating coaching response: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")