Update app.py

app.py

@@ -1,46 +1,37 @@
 from fastapi import FastAPI, Form
-from pydantic import BaseModel
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
 from twilio.twiml.messaging_response import MessagingResponse
+from transformers import pipeline
 
-# --- Load model ---
-model_id = "ST-THOMAS-OF-AQUINAS/SCAM"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForSequenceClassification.from_pretrained(model_id)
-model.eval()
+# --- Hugging Face Inference API setup ---
+HF_MODEL_ID = "ST-THOMAS-OF-AQUINAS/SCAM"
 
-label_map = {0: "author1", 1: "author2"}
+
+classifier = pipeline(
+    "text-classification",
+    model=HF_MODEL_ID,
+  
+)
 
 # --- FastAPI app ---
 app = FastAPI(title="Scam Detector API with Twilio")
 
-# --- Helper prediction function ---
+# --- Prediction function ---
 def predict_author(text: str):
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-        pred = torch.argmax(probs, dim=1).item()
-        confidence = probs[0][pred].item()
-    predicted_author = label_map[pred]
-    return predicted_author, round(confidence * 100, 2)
+    if not text.strip():
+        return "No text", 0.0
+    result = classifier(text)[0]  # {'label': 'author1', 'score': 0.95}
+    return result['label'], round(result['score'] * 100, 2)
 
 # --- Twilio WhatsApp webhook ---
 @app.post("/whatsapp")
 async def whatsapp_reply(Body: str = Form(...)):
     resp = MessagingResponse()
-    
-    if Body.strip():
-        author, confidence = predict_author(Body)
-        reply = f"Prediction: {author}\nConfidence: {confidence}%"
-    else:
-        reply = "⚠️ No text detected."
-
+    author, confidence = predict_author(Body)
+    reply = f"Prediction: {author}\nConfidence: {confidence}%"
     resp.message(reply)
     return str(resp)
 
-# --- Simple test endpoint ---
+# --- Simple GET test endpoint ---
 @app.get("/predict")
 async def predict(text: str):
     author, confidence = predict_author(text)