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

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

app = FastAPI()

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

# Define model path (absolute path in the container)
model_path = "/app/fine-tuned-construction-llm"

# Verify the model directory exists
if not os.path.isdir(model_path):
    logger.error(f"Model directory not found: {model_path}")
    raise Exception(f"Model directory not found: {model_path}")

# Load model and tokenizer
try:
    model = AutoModelForCausalLM.from_pretrained(model_path, local_files_only=True)
    tokenizer = AutoTokenizer.from_pretrained(model_path, local_files_only=True)
    logger.info("Model and tokenizer loaded successfully")
except Exception as e:
    logger.error(f"Failed to load model or tokenizer: {str(e)}")
    raise Exception(f"Model loading failed: {str(e)}")

@app.post("/generate_coaching")
async def generate_coaching(data: CoachingInput):
    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)
        
        # Simulate structured output (replace with actual parsing logic based on model output)
        # This assumes the model outputs a JSON-like string; adjust based on fine-tuning
        try:
            response_json = json.loads(response_text)
        except json.JSONDecodeError:
            # Fallback: Construct a default response if parsing fails
            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)}")

@app.get("/health")
async def health_check():
    return {"status": "healthy"}