Update app.py

app.py

@@ -3,11 +3,12 @@ from transformers import GPT2LMHeadModel, GPT2TokenizerFast, GPT2Config
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from contextlib import asynccontextmanager
+import asyncio
 
 # FastAPI app instance
 app = FastAPI()
 
-# Global model and tokenizer
+# Global model and tokenizer variables
 model, tokenizer = None, None
 
 # Function to load model and tokenizer
@@ -15,14 +16,18 @@ def load_model():
     model_path = "./Ai-Text-Detector/model"
     weights_path = "./Ai-Text-Detector/model_weights.pth"
 
-    tokenizer = GPT2TokenizerFast.from_pretrained(model_path)
-    config = GPT2Config.from_pretrained(model_path)
-    model = GPT2LMHeadModel(config)
-    model.load_state_dict(torch.load(weights_path, map_location=torch.device("cpu")))
-    model.eval()
+    try:
+        tokenizer = GPT2TokenizerFast.from_pretrained(model_path)
+        config = GPT2Config.from_pretrained(model_path)
+        model = GPT2LMHeadModel(config)
+        model.load_state_dict(torch.load(weights_path, map_location=torch.device("cpu")))
+        model.eval()  # Set model to evaluation mode
+    except Exception as e:
+        raise RuntimeError(f"Error loading model: {str(e)}")
+
     return model, tokenizer
 
-# Load on app startup
+# Load model on app startup
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     global model, tokenizer
@@ -48,11 +53,11 @@ def classify_text(sentence: str):
         perplexity = torch.exp(loss).item()
 
     if perplexity < 60:
-        result = "AI-generated*"
+        result = "AI-generated"
     elif perplexity < 80:
-        result = "Probably AI-generated*"
+        result = "Probably AI-generated"
     else:
-        result = "Human-written*"
+        result = "Human-written"
 
     return result, perplexity
 
@@ -63,7 +68,9 @@ async def analyze_text(data: TextInput):
     if not user_input:
         raise HTTPException(status_code=400, detail="Text cannot be empty")
 
-    result, perplexity = classify_text(user_input)
+    # Run classification asynchronously to prevent blocking
+    result, perplexity = await asyncio.to_thread(classify_text, user_input)
+    
     return {
         "result": result,
         "perplexity": round(perplexity, 2),