Create app.py

app.py

@@ -0,0 +1,53 @@
+# app.py
+import gradio as gr
+from transformers import pipeline
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+import uvicorn
+
+# Load model
+model_id = "ST-THOMAS-OF-AQUINAS/SCAM"
+pipe = pipeline("text-classification", model=model_id)
+
+# Label map (adjust to your model)
+label_map = {0: "author1", 1: "author2"}
+
+def predict(text: str):
+    if not text.strip():
+        return "⚠️ No input text"
+
+    results = pipe(text)
+    label = results[0]["label"]
+    score = results[0]["score"]
+
+    if label.startswith("LABEL_"):
+        idx = int(label.split("_")[1])
+        label = label_map.get(idx, label)
+
+    return f"Prediction: {label}\nConfidence: {round(score * 100, 2)}%"
+
+
+# --- Gradio Interface ---
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(label="Enter WhatsApp Message"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="📲 WhatsApp Scam Detector",
+    description="Paste a WhatsApp message and the model will predict its author."
+)
+
+# --- FastAPI App (for Twilio Webhook) ---
+app = FastAPI()
+
+class Message(BaseModel):
+    Body: str
+
+@app.post("/predict")
+async def predict_api(msg: Message):
+    return {"reply": predict(msg.Body)}
+
+# Mount Gradio inside FastAPI
+app = gr.mount_gradio_app(app, iface, path="/")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)