Update app.py

app.py

@@ -4,11 +4,10 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import json
 import logging
 import os
-import asyncio
 
 # Set up logging to both stdout and a file
 logging.basicConfig(
-    level=logging.INFO,
+    level=logging.DEBUG,
     format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
     handlers=[
         logging.StreamHandler(),  # Log to stdout
@@ -58,11 +57,17 @@ async def load_model_background():
 # Startup event to initiate model loading in the background
 @app.on_event("startup")
 async def startup_event(background_tasks: BackgroundTasks):
-    logger.info("FastAPI application started")
+    logger.debug("FastAPI application started")
     background_tasks.add_task(load_model_background)
 
+@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
@@ -70,9 +75,16 @@ async def health_check():
 
 @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.error("Model or tokenizer not loaded")
-        raise HTTPException(status_code=503, detail="Model not loaded yet. Please try again later.")
+        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