Final update

app/assets/{email_preprocessor_20250506_203148.joblib → multinomial_nb_email_preprocessor.joblib}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:279f139d98042e89d2d46a30c37a0ea32e1aaddae7ae247920476474af43a26a
-size 639092
+oid sha256:453890297bdcb067543815f71d347942e84c893111029823940fd2b59fc81ac9
+size 638994

app/assets/{phishing_nb_model_20250506_203148.joblib → trained_multinomial_nb_model.joblib}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5480ff6d4f84e518148e2c415164f50e25e1f1312733ed38717a8a36186b9497
+oid sha256:bd44dafe90588c8ca8a4a3c02890b23596dba3224a9404e1b4b2e44f564d9027
 size 544791

app/main.py

@@ -2,12 +2,26 @@ from fastapi import FastAPI
 from pydantic import BaseModel
 import os
 import subprocess
-from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
 from typing import List, Tuple, Optional 
 from .ml import get_model_prediction, check_model_status
 
 app = FastAPI(title="AI-Powered Phishing Email Detection System")
 
+# Define allowed origins for CORS
+origins = [
+    "https://ai-powered-phishing-email-detection-system.vercel.app",
+    "http://localhost:3000",
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,  # Allows specific origins
+    allow_credentials=True, # Allows cookies to be included in requests
+    allow_methods=["*"],  # Allows all methods (GET, POST, etc.)
+    allow_headers=["*"],  # Allows all headers
+)
+
 # Input data model
 class EmailInput(BaseModel):
     subject: Optional[str] = ""

app/ml/__init__.py

@@ -21,7 +21,6 @@ def get_model_prediction(subject: str, sender: str, body: str, model_choice: str
         return {"error": f"Invalid model_choice: '{model_choice}'. Choose 'nb' or 'bert-mini'.",
                 "prediction": "Error", "label": -1, "confidence": 0.0, "explanation": []}
 
-# You can also add a health check function here if needed
 def check_model_status():
     status = {
         "naive_bayes": {

app/ml/bert_mini_model.py

@@ -69,7 +69,6 @@ def get_prediction_and_explanation_bert_mini(subject: str, sender: str, body: st
     cleaned_body = simple_text_clean(body)
     
     combined_text_for_prediction = f"{cleaned_sender} {cleaned_subject} {cleaned_body}"
-    text_for_lime_explanation = combined_text_for_prediction
 
 
     try:

app/ml/nb_model.py

@@ -6,8 +6,8 @@ from lime.lime_text import LimeTextExplainer
 from .common import simple_text_clean, CLASS_NAMES 
 
 ASSETS_DIR = os.path.join(os.path.dirname(__file__), '..', 'assets') 
-PREPROCESSOR_FILENAME = "email_preprocessor_20250506_203148.joblib"
-MODEL_FILENAME = "phishing_nb_model_20250506_203148.joblib"
+PREPROCESSOR_FILENAME = "multinomial_nb_email_preprocessor.joblib"
+MODEL_FILENAME = "trained_multinomial_nb_model.joblib"
 PREPROCESSOR_PATH = os.path.join(ASSETS_DIR, PREPROCESSOR_FILENAME)
 MODEL_PATH = os.path.join(ASSETS_DIR, MODEL_FILENAME)