Add Bangla model API

app.py

@@ -2,37 +2,34 @@ from fastapi import FastAPI
 from pydantic import BaseModel
 import joblib
 
-app=FastAPI()
+app = FastAPI()
 
 class PredictRequest(BaseModel):
-    text:str
+    text: str
 
-#load model
-model=joblib.load('english_model.pkl')
-vectorizer=joblib.load('english_tfid.pkl')
-label_encoder=joblib.load('english_label_encoder.pkl')
+# Load model and vectorizer
+model = joblib.load("english_model.pkl")
+vectorizer = joblib.load("english_tfid.pkl")
+label_encoder = joblib.load("english_label_encoder.pkl")
 
 @app.get("/")
 def root():
-    return {"message":"welcome to the english model api end point"}
+    return {"message": "Welcome to the English model API endpoint"}
 
 @app.post("/predict")
-def predict(request:PredictRequest):
-    vect=vectorizer.transform([request.text])
-    pred=model.predict(vect)
-    label=label_encoder.inverse_transform(pred)
+def predict(request: PredictRequest):
+    vect = vectorizer.transform([request.text])
+    pred = model.predict(vect)                 # returns encoded int
+    label = label_encoder.inverse_transform(pred)  # convert back to string
 
-    if label[0]==0:
-        return {"prediction":"Hate regarding age"}
-    elif label[0]==1:
-        return {"prediction":"Hate regarding ethnicity"}
-    elif label[0]==2:
-        return {"prediction":"Hate regarding gender"}
-    elif label[0]==3:   
-        return {"prediction":"Not Hate"}
-    elif label[0]==4:
-        return {"prediction":"other hate"}
-    elif label[0]==5:
-        return {"prediction":"hate regarding religion"}
+    # Map label strings to friendly messages
+    mapping = {
+        "age": "Hate regarding age",
+        "ethnicity": "Hate regarding ethnicity",
+        "gender": "Hate regarding gender",
+        "not_cyberbullying": "Not Hate",
+        "other_cyberbullying": "Other hate",
+        "religion": "Hate regarding religion"
+    }
 
-     
+    return {"prediction": mapping[label[0]]}