Update app.py

app.py

@@ -1,71 +1,43 @@
 import torch
 from transformers import GPT2LMHeadModel, GPT2TokenizerFast, GPT2Config
-from fastapi import FastAPI, HTTPException, Header
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
-import asyncio
-from concurrent.futures import ThreadPoolExecutor
 from contextlib import asynccontextmanager
-from dotenv import dotenv_values
 
-# FastAPI instance
+# FastAPI app instance
 app = FastAPI()
-executor = ThreadPoolExecutor(max_workers=20)
 
-# Load .env file
-env = dotenv_values(".env")
-EXPECTED_TOKEN = env.get("SECRET_TOKEN")
-
-# Global variables for model and tokenizer
+# Global model and tokenizer
 model, tokenizer = None, None
 
-# Function to verify token
-
-
-def verify_token(auth: str):
-    if auth != f"Bearer {EXPECTED_TOKEN}":
-        raise HTTPException(status_code=403, detail="Unauthorized")
-
-
 # Function to load model and tokenizer
-
 def load_model():
     model_path = "./Ai-Text-Detector/model"
     weights_path = "./Ai-Text-Detector/model_weights.pth"
 
-    # Load tokenizer and config from your custom model path
     tokenizer = GPT2TokenizerFast.from_pretrained(model_path)
     config = GPT2Config.from_pretrained(model_path)
-
-    # Initialize model from config (don't load any weights from Hugging Face)
     model = GPT2LMHeadModel(config)
-
-    # Load your saved PyTorch weights
     model.load_state_dict(torch.load(weights_path, map_location=torch.device("cpu")))
-
-    model.eval()  # Set to evaluation mode
+    model.eval()
     return model, tokenizer
 
+# Load on app startup
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     global model, tokenizer
     model, tokenizer = load_model()
     yield
 
-
-# Attach the lifespan context manager
+# Attach startup loader
 app = FastAPI(lifespan=lifespan)
 
-# Request body for input data
-
-
+# Input schema
 class TextInput(BaseModel):
     text: str
 
-
-# Sync function to classify text
-
-
-def classify_text_sync(sentence: str):
+# Sync text classification
+def classify_text(sentence: str):
     inputs = tokenizer(sentence, return_tensors="pt", truncation=True, padding=True)
     input_ids = inputs["input_ids"]
     attention_mask = inputs["attention_mask"]
@@ -84,44 +56,25 @@ def classify_text_sync(sentence: str):
 
     return result, perplexity
 
-
-# Async wrapper for text classification
-
-
-async def classify_text(sentence: str):
-    loop = asyncio.get_event_loop()
-    return await loop.run_in_executor(executor, classify_text_sync, sentence)
-
-
 # POST route to analyze text
-
-
 @app.post("/analyze")
-async def analyze_text(data: TextInput, authorization: str = Header(default="")):
-    verify_token(authorization)  # Token verification
+async def analyze_text(data: TextInput):
     user_input = data.text.strip()
-
     if not user_input:
         raise HTTPException(status_code=400, detail="Text cannot be empty")
 
-    result, perplexity = await classify_text(user_input)
-
+    result, perplexity = classify_text(user_input)
     return {
         "result": result,
         "perplexity": round(perplexity, 2),
     }
 
-
 # Health check route
-
-
 @app.get("/health")
 async def health_check():
     return {"status": "ok"}
 
-
 # Simple index route
-
 @app.get("/")
 def index():
     return {
@@ -129,4 +82,3 @@ def index():
         "try": "/docs to test the API.",
         "status": "OK"
     }
-