Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+import os
+
+# Make Streamlit write configs locally instead of root
+os.environ["STREAMLIT_HOME"] = os.path.join(os.getcwd(), ".streamlit")
+
+# --- Load the model ---
+script_dir = os.path.dirname(__file__)
+model_path = os.path.join(script_dir, "salary_model.pkl")  # your saved model
+
+st.title("💼 AI Salary Prediction App")
+st.write("This tool predicts a developer's estimated salary based on their background and experience.")
+
+try:
+    model_pipeline = joblib.load(model_path)
+    st.success("✅ Model loaded successfully!")
+except Exception as e:
+    st.error(f"❌ Error loading the model: {e}")
+    st.stop()
+
+# --- User input section ---
+st.sidebar.header("Input your details")
+
+age = st.sidebar.slider("Age", 18, 65, 30)
+years_code_pro = st.sidebar.slider("Years of professional coding experience", 0, 40, 5)
+country = st.sidebar.selectbox("Country", ["Denmark", "Germany", "Croatia", "Portugal", "Italy", "Netherlands"])
+education = st.sidebar.selectbox("Education level", [
+    "Bachelor’s degree",
+    "Master’s degree (M.A., M.S., M.Eng., MBA, etc.)",
+    "Doctoral degree",
+    "Less than Bachelor’s"
+])
+remote = st.sidebar.selectbox("Work arrangement", ["Remote", "Hybrid", "On-site"])
+
+# --- Create a DataFrame for prediction ---
+input_data = pd.DataFrame({
+    "age_group": [age],
+    "years_code_pro": [years_code_pro],
+    "country": [country],
+    "ed_level": [education],
+    "remote_work": [remote]
+})
+
+# --- Predict salary ---
+if st.button("Predict Salary"):
+    try:
+        predicted_salary = model_pipeline.predict(input_data)[0]
+        st.subheader(f"💰 Predicted Salary: €{predicted_salary:,.0f}")
+    except Exception as e:
+        st.error(f"Error making prediction: {e}")
+