Addition of the api

api.py

@@ -0,0 +1,55 @@
+from fastapi import FastAPI, HTTPException, Query
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import pandas as pd
+import json
+from typing import Dict, Any
+import dill as pickle
+import sys
+
+from Stopwords import filter_review
+
+app = FastAPI()
+
+#Allow CORS for communication with Angular frontend
+app.add_middleware(
+    CORSMiddleware,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load the model and the vectorizer
+try:
+    with open('./model_1_en.pkl', 'rb') as fin:
+        model_loaded = pickle.load(fin)
+except FileNotFoundError:
+    print("Error: 'model_1_en.pkl' not found.")
+
+try:
+    with open('./vectorizer.pkl', 'rb') as fin:
+        vectorizer = pickle.load(fin)
+except FileNotFoundError:
+    print("Error: 'vectorizer.pkl' not found.")
+
+class Review(BaseModel):
+    review: str
+    
+# Helper function to filter and predict review
+def get_prediction(text):
+    processed_text = filter_review(text)
+    vectorized_text = vectorizer.transform([processed_text])
+    prediction = model_loaded.predict(vectorized_text)[0]
+    return int(prediction)  # Convert to int for JSON serialization 
+
+# Endpoint to add a new review
+
+@app.post("/review/predict", response_model=Dict[str, Any])
+def add_review(review: Review):
+    try:
+        # Get the prediction for the new review
+        prediction = get_prediction(review.review)
+
+        return {"status": "success", "review": review, "prediction": prediction}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))