| import gradio as gr |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import numpy as np |
| from sklearn.preprocessing import LabelEncoder |
|
|
| MODEL_ID = "ModSpecialization/distilbert-base-uncased-fraud-classifer" |
|
|
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
| model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID) |
|
|
| label_encoder = LabelEncoder() |
| label_encoder.classes_ = np.array(["No", "Yes"]) |
|
|
| def classify_fraud(customer_id, terminal_id, amount, timestamp): |
| text = f"{customer_id} | {terminal_id} | {amount} | {timestamp}" |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
| outputs = model(**inputs) |
| pred = np.argmax(outputs.logits.detach().numpy(), axis=1) |
| print(pred) |
| return f"Prediction: {label_encoder.inverse_transform(pred)[0]}" |
|
|
| demo = gr.Interface( |
| fn=classify_fraud, |
| inputs=[ |
| gr.Textbox(label="Customer ID"), |
| gr.Textbox(label="Terminal ID"), |
| gr.Number(label="Transaction Amount ($)"), |
| gr.Textbox(label="Transaction Time (YYYY-MM-DD HH:MM:SS)") |
| ], |
| outputs="text", |
| title="Credit Card Fraud Classifier", |
| description="Enter transaction details to predict if it's fraudulent" |
| ) |
|
|
| demo.launch() |
|
|
