Update app.py

app.py

@@ -1,46 +1,34 @@
 from fastapi import FastAPI, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from contextlib import asynccontextmanager
 import base64
 import os
+from contextlib import asynccontextmanager
 
-# Create the FastAPI app
+# Define the lifespan context manager
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    # Load LoRA weights during app startup
+    lora_weights = None
     lora_path = "./lora_file.pth"
     if os.path.exists(lora_path):
         with open(lora_path, "rb") as f:
-            # Base64 encode the LoRA weights for easy JSON transmission
-            app.state.lora_weights = base64.b64encode(f.read()).decode("utf-8")
-            print("LoRA weights loaded and preprocessed successfully.")
+            lora_weights = base64.b64encode(f.read()).decode("utf-8")
+        print("LoRA weights loaded and preprocessed successfully.")
     else:
-        print("LoRA file not found during startup.")
-        raise RuntimeError("LoRA file not found.")
-
-    # Yield control to the application
+        raise HTTPException(status_code=500, detail="LoRA file not found.")
+    
+    # Yield control back to the FastAPI app during runtime
     yield
+    
+    # Cleanup or shutdown logic here if necessary
+    print("App is shutting down.")
 
-    # Perform any cleanup (if needed) here
-    print("Application shutting down.")
-
+# Create the FastAPI app with the lifespan context manager
 app = FastAPI(lifespan=lifespan)
 
-# Middleware to handle CORS (optional, but useful for cross-origin requests)
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Adjust for security
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
 @app.post("/modify-prompt")
 async def modify_prompt(prompt: str):
-    lora_weights = getattr(app.state, "lora_weights", None)
+    global lora_weights
     if lora_weights is None:
         raise HTTPException(status_code=500, detail="LoRA weights not loaded.")
-    
     # Combine prompt with preprocessed LoRA data
     extended_prompt = {
         "prompt": prompt,