Create app.py

app.py

@@ -0,0 +1,48 @@
+from fastapi import FastAPI, Form
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from twilio.twiml.messaging_response import MessagingResponse
+
+# --- Load model ---
+model_id = "ST-THOMAS-OF-AQUINAS/SCAM"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+model.eval()
+
+label_map = {0: "author1", 1: "author2"}
+
+# --- FastAPI app ---
+app = FastAPI(title="Scam Detector API with Twilio")
+
+# --- Helper 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)
+
+# --- 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."
+
+    resp.message(reply)
+    return str(resp)
+
+# --- Simple test endpoint ---
+@app.get("/predict")
+async def predict(text: str):
+    author, confidence = predict_author(text)
+    return {"prediction": author, "confidence": confidence}
+