Create app.py

app.py

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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.")