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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load the pre-trained model and tokenizer from Hugging Face
model_name = "tajuarAkash/test2" # Replace with your Hugging Face model path
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Title of the web app
st.title("Fraud Detection in Health Insurance Claims")
# Description of the app
st.write("This app predicts whether a health insurance claim is fraudulent based on the input data.")
# Create a text box for the user to input the generated sentence (feature for prediction)
input_text = st.text_area("Enter the claim description")
# Create a button to make predictions
if st.button('Predict Fraud'):
if input_text:
# Tokenize the input text
inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
# Get model predictions
with torch.no_grad():
logits = model(**inputs).logits
predicted_class = torch.argmax(logits, dim=-1).item()
# Display the result
if predicted_class == 1:
st.write("This claim is predicted to be fraudulent.")
else:
st.write("This claim is predicted to be legitimate.")
else:
st.write("Please enter a claim description.")
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