Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,103 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import joblib
+from datetime import datetime
+from huggingface_hub import hf_hub_download
+
+st.set_page_config(page_title="Credit Risk App", page_icon="💼", layout="centered")
+
+# ✅ Load model from Hugging Face Hub using huggingface_hub
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id="ZeeshanWattoo/random-forest-credit-model", 
+        filename="random_forest_model1.pkl"                   
+    )
+    return joblib.load(model_path)
+
+model = load_model()
+
+# Category mappings
+home_mapping = {"Own": 2, "Mortgage": 1, "Rent": 0}
+intent_mapping = {
+    "education": 0, "home_improvement": 1, "medical": 2,
+    "personal": 3, "venture": 4
+}
+default_mapping = {"Yes": 1, "No": 0}
 
-"""
-# Welcome to Streamlit!
+st.title("💼 Credit Risk Prediction App")
+st.markdown("Enter applicant details below to predict the likelihood of loan repayment or default.")
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+st.header("📝 Applicant Information")
+col1, col2 = st.columns(2)
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+with col1:
+    age = st.number_input("Age", min_value=18, max_value=100, value=30)
+    income = st.number_input("Annual Income", min_value=0.0, value=50000.0, step=1000.0)
+    emp_length = st.number_input("Employment Length (years)", min_value=0, max_value=50, value=5)
+    intent = st.selectbox("Loan Purpose", ["education", "home_improvement", "medical", "personal", "venture"])
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+with col2:
+    home = st.selectbox("Home Ownership", ["Own", "Mortgage", "Rent"])
+    amount = st.number_input("Loan Amount", min_value=0.0, value=10000.0, step=500.0)
+    rate = st.number_input("Interest Rate (%)", min_value=0.0, max_value=100.0, value=10.0, step=0.5)
+    default = st.selectbox("Previous Default", ["Yes", "No"])
+    cred_length = st.number_input("Credit History Length (years)", min_value=0, max_value=50, value=10)
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+if income == 0:
+    st.warning("Annual income must be greater than 0.")
+    st.stop()
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+# Derived feature
+percent_income = amount / income
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
+# Create input dataframe
+input_df = pd.DataFrame({
+    "Age": [age],
+    "Income": [income],
+    "Home": [home_mapping[home]],
+    "Emp_length": [emp_length],
+    "Intent": [intent_mapping[intent]],
+    "Amount": [amount],
+    "Rate": [rate],
+    "Percent_income": [percent_income],
+    "Default": [default_mapping[default]],
+    "Cred_length": [cred_length]
 })
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Predict and show results
+if st.button("🔍 Predict Credit Risk"):
+    prediction = model.predict(input_df)
+    proba = model.predict_proba(input_df)[0]
+
+    status = "🟢 Fully Paid" if prediction[0] == 0 else "🔴 Charged Off"
+    st.success(f"**Predicted Loan Status:** {status}")
+
+    # Risk metric
+    st.metric(label="Loan Risk Score (Default)", value=f"{proba[1]*100:.1f}%")
+
+    # Probabilities
+    st.subheader("📊 Prediction Probabilities")
+    st.write(f"🟢 Fully Paid: **{proba[0] * 100:.2f}%**")
+    st.write(f"🔴 Charged Off: **{proba[1] * 100:.2f}%**")
+
+    # Prepare downloadable result
+    result_df = input_df.copy()
+    result_df["Predicted Status"] = status
+    result_df["Fully Paid Probability (%)"] = round(proba[0] * 100, 2)
+    result_df["Charged Off Probability (%)"] = round(proba[1] * 100, 2)
+
+    # Timestamped filename
+    filename = f"credit_risk_prediction_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
+    
+    # Download button
+    st.download_button(
+        label="💾 Download Prediction as CSV",
+        data=result_df.to_csv(index=False),
+        file_name=filename,
+        mime="text/csv"
+    )
+
+# Footer
+st.markdown("---")
+st.caption("Made with ❤️ by Zeeshan Ahmad Wattoo | BS Software Engineering, Semester 6")