Create app.py

app.py

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+import streamlit as st
+import joblib
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+
+# Load the trained model from Hugging Face (if hosted there)
+# You can replace 'tajuarAkash/my-model' with the actual repository name on Hugging Face
+model = joblib.load('random_forest_model.joblib')  # For local file if uploaded to Space
+
+# Title and description for the app
+st.title("Insurance Claim Fraud Detection")
+st.write("""
+This app predicts whether an insurance claim is fraudulent or legitimate based on user input.
+Please enter the information below.
+""")
+
+# Input fields for users to provide claim details
+claim_amount = st.number_input("Enter the claim amount", min_value=0)
+patient_age = st.number_input("Enter the patient's age", min_value=0)
+patient_income = st.number_input("Enter the patient's income", min_value=0)
+patient_gender = st.selectbox("Select patient's gender", ["Male", "Female"])
+claim_status = st.selectbox("Claim status", ["Denied", "Pending", "Approved"])
+
+# Create a button to trigger prediction
+if st.button('Predict Fraud'):
+    # Preprocess the input data (this may need adjustments based on how you preprocessed during training)
+    input_data = {
+        "ClaimAmount": [claim_amount],
+        "PatientAge": [patient_age],
+        "PatientIncome": [patient_income],
+        "PatientGender": [patient_gender],
+        "ClaimStatus": [claim_status],
+    }
+
+    # Convert the input data to a pandas DataFrame
+    input_df = pd.DataFrame(input_data)
+
+    # Encode the gender (if needed)
+    input_df['PatientGender'] = input_df['PatientGender'].apply(lambda x: 1 if x == 'Male' else 0)
+
+    # Encode the ClaimStatus (example ordinal encoding, adjust based on training)
+    claim_status_mapping = {"Denied": 0, "Pending": 1, "Approved": 2}
+    input_df['ClaimStatus'] = input_df['ClaimStatus'].map(claim_status_mapping)
+
+    # Assuming the model expects the data to be scaled (adjust as per your preprocessing)
+    scaler = StandardScaler()
+    input_scaled = scaler.fit_transform(input_df)
+
+    # Get the prediction from the model
+    prediction = model.predict(input_scaled)
+
+    # Display the result
+    if prediction == 1:
+        st.write("This claim is predicted to be **fraudulent**.")
+    else:
+        st.write("This claim is predicted to be **legitimate**.")
+