new structure

main.py

@@ -1,23 +1,23 @@
-from fastapi import FastAPI
+from fastapi import FastAPI, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from transformers import pipeline
-import os
+from pydantic import BaseModel
 import random
 
 app = FastAPI()
 
+# Configure CORS
 origins = [
     "https://bible-affirmations.vercel.app",  # Replace with your Vercel frontend URL
     "http://localhost:3000",  # For local testing
 ]
 
-# Allow all origins for CORS (adjust as per your security requirements)
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  # Allow requests from any origin
+    allow_origins=origins,
     allow_credentials=True,
-    allow_methods=["POST"],  # Allow only POST requests
-    allow_headers=["*"],  # Allow all headers
+    allow_methods=["POST"],
+    allow_headers=["*"],
 )
 
 # Initialize the sentiment analysis pipeline
@@ -35,6 +35,15 @@ bible_verses = {
     ]
 }
 
+class FeelingRequest(BaseModel):
+    feeling: str
+
+class AffirmationResponse(BaseModel):
+    label: str
+    score: float
+    verse: str
+    response_message: str
+
 def get_feeling_classification(feeling_text):
     # Use the sentiment analysis pipeline to classify the feeling
     result = sentiment_analyzer(feeling_text)[0]
@@ -64,12 +73,9 @@ def generate_response(emotion_label):
 async def home():
     return {"message": "Hello World"}
 
-@app.post("/api/home")
-async def home_post(feeling: str):
-    label, score = get_feeling_classification(feeling)
+@app.post("/api/home", response_model=AffirmationResponse)
+async def home_post(feeling_request: FeelingRequest):
+    label, score = get_feeling_classification(feeling_request.feeling)
     bible_verse = get_bible_verse(label)
     response_message = generate_response(label)
     return {"label": label, "score": score, "verse": bible_verse, "response_message": response_message}
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8080)

requirements.txt

@@ -3,5 +3,6 @@ fastapi[all]
 uvicorn
 transformers
 tf-keras
+tensorflow
 torch
-tensorflow
+pydantic