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app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
+
+# Load tokenizer and model from Hugging Face Hub
+MODEL_NAME = "briangilbert/working"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+# Define labels
+id2label = {0: "NOT SCAM", 1: "SCAM"}
+
+# Streamlit UI
+st.title("💬 Fraud Detection in Text")
+st.write("Enter a dialogue and check if it's a **SCAM** or **NOT SCAM**.")
+
+# Text input
+user_input = st.text_area("Enter a message:")
+
+if st.button("Detect Fraud"):
+    if user_input:
+        # Tokenize input
+        inputs = tokenizer(user_input, return_tensors="pt", truncation=True)
+
+        # Get model prediction
+        model.eval()
+        with torch.no_grad():
+            outputs = model(**inputs)
+            predicted_class = torch.argmax(outputs.logits).item()
+
+        # Display result
+        st.success(f"🚨 Prediction: **{id2label[predicted_class]}**")
+    else:
+        st.warning("Please enter a dialogue.")

requirements.txt

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+streamlit
+torch
+transformers