feat: added the rate limiter

app.py

@@ -1,27 +1,38 @@
-# app.py
-
 from fastapi import FastAPI, Request
 from contextlib import asynccontextmanager
 from features.text_classifier.routes import router as text_classifier_router
 from features.text_classifier.model_loader import warmup
-import nltk
 from slowapi import Limiter, _rate_limit_exceeded_handler
 from slowapi.util import get_remote_address
 from slowapi.errors import RateLimitExceeded
+from slowapi.middleware import SlowAPIMiddleware
+from config import ACCESS_RATE  # E.g., "100/minute"
+import nltk
 import requests
+
+# Warmup logic
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     warmup()
     yield
 
-limiter = Limiter(key_func=get_remote_address)
+# Create the limiter with a global rate from config
+limiter = Limiter(key_func=get_remote_address, default_limits=[ACCESS_RATE])
 
+# Initialize FastAPI with lifespan
 app = FastAPI(lifespan=lifespan)
+
+# Set limiter and exception handler
 app.state.limiter = limiter
 app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)
 
+# Add SlowAPI middleware globally
+app.add_middleware(SlowAPIMiddleware)
+
+# Register routers
 app.include_router(text_classifier_router, prefix="/text")
 
+# Root route
 @app.get("/")
 def index():
     return {
@@ -29,8 +40,8 @@ def index():
         "Try": "/text/analyse or /text/analyse-sentences"
     }
 
+# Example protected route
 @app.get("/home")
-@limiter.limit("5/minute")
 async def homepage(request: Request):
     return {"msg": "This is a good message"}
 

config.py

@@ -1 +1,2 @@
-ACCESS_RATE=10
+ACCESS_RATE = "100/minute"
+

features/text_classifier/controller.py

@@ -38,7 +38,7 @@ async def handle_file_sentance(file: UploadFile):
             return {"message": "File contains more than 10,000 characters."}
         cleaned_text = file_contents.replace("\n", " ").replace("\t", " ").strip()
         if not cleaned_text:
-            raise HTTPException(status_code=400, detail="The file is empty or only contains whitespace.")
+            raise HTTPException(status_code=404, detail="The file is empty or only contains whitespace.")
         result = await handle_sentence_level_analysis(cleaned_text)
         return {"content": file_contents, **result}
     except Exception as e:
@@ -53,7 +53,7 @@ async def handle_file_upload(file: UploadFile):
             return {"message": "File contains more than 10,000 characters."}
         cleaned_text = file_contents.replace("\n", " ").replace("\t", " ").strip()
         if not cleaned_text:
-            raise HTTPException(status_code=400, detail="The file is empty or only contains whitespace.")
+            raise HTTPException(status_code=404, detail="The file is empty or only contains whitespace.")
         label, perplexity, ai_likelihood = await asyncio.to_thread(classify_text, cleaned_text)
         return {
             "content": file_contents,
@@ -77,7 +77,7 @@ async def extract_file_contents(file: UploadFile):
         return parse_txt(file_stream)
     else:
         raise HTTPException(
-            status_code=400,
+            status_code=404,
             detail="Invalid file type. Only .docx, .pdf, and .txt are allowed."
         )
 
@@ -85,10 +85,10 @@ async def extract_file_contents(file: UploadFile):
 async def handle_sentence_level_analysis(text: str):
     text = text.strip()
     if not text or len(text.split()) < 2:
-        raise HTTPException(status_code=400, detail="Text must contain at least two words")
+        raise HTTPException(status_code=413, detail="Text must contain at least two words")
 
     if len(text) > 10000:
-        raise HTTPException(status_code=400, detail="Text must be less than 10,000 characters.")
+        raise HTTPException(status_code=413, detail="Text must be less than 10,000 characters.")
 
     sentences = sent_tokenize(text, language="english")
     results = []