feat: Add Streamlit app for interactive loan applications

- Introduced a new Streamlit application to facilitate
  interactive loan applications using the trained credit
  scoring model.
- Updated README to include instructions for running the
  Streamlit app and added a demo image.
- Enhanced pyproject.toml to include Streamlit as an
  optional dependency.

This addition allows users to visualize and interact
with the credit scoring model in a user-friendly manner.

Signed-off-by: D. Danchev <[email protected]>

Author: danchev

README.md

@@ -35,7 +35,7 @@ podman run -d -p 6379:6379 --name redis -e "ALLOW_EMPTY_PASSWORD=yes"  docker.io
 Install Feast using uv
 
 ```
-uv sync  
+uv sync --all-extras
 ```
 
 We have already set up a feature repository in [feature_repo/](feature_repo/). As a result, all we have to do is configure the [feature_store.yaml/](feature_repo/feature_store.yaml) in the feature repository. Please set the connection string of the Postgresql and Redis according to your local infra setup.  
@@ -248,4 +248,17 @@ Example returned feature values:
         }
     ]
 }
-```
+```
+
+
+## Interactive demo (using Streamlit)
+
+Once the credit scoring model has been trained it can be used for interactive loan applications using Streamlit:
+
+Simply start the Streamlit application
+```
+uv run streamlit run streamlit_app.py
+```
+Then navigate to the URL on which Streamlit is being served. You should see a user interface through which loan applications can be made:
+
+![Streamlit Loan Application](streamlit.png)

pyproject.toml

@@ -14,3 +14,9 @@ dependencies = [
     "grpcio-tools",
     "grpcio-reflection",
 ]
+[project.optional-dependencies]
+streamlit = [
+    "streamlit>=1.50.0",
+    "shap>=0.49.0",
+    "matplotlib>=3.10.7",
+]

streamlit.png

@@ -0,0 +1,132 @@
+import datetime
+import logging
+import sys
+from collections import OrderedDict
+
+import pandas as pd
+import shap
+import streamlit as st
+from matplotlib import pyplot as plt
+
+from credit_model import CreditScoringModel
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s %(levelname)s %(message)s",
+    handlers=[logging.StreamHandler(sys.stdout)],
+)
+
+logging.debug("Streamlit app started.")
+
+st.set_page_config(layout="wide")
+model = CreditScoringModel()
+if not model.is_model_trained():
+    raise Exception("The credit scoring model has not been trained. Please run run.py.")
+
+
+def get_loan_request():
+    zipcode = st.sidebar.text_input("Zip code", "94109")
+    date_of_birth = st.sidebar.date_input(
+        "Date of birth", value=datetime.date(year=1986, day=19, month=3)
+    )
+    ssn_last_four = st.sidebar.text_input(
+        "Last four digits of social security number", "3643"
+    )
+    dob_ssn = f"{date_of_birth.strftime('%Y%m%d')}_{str(ssn_last_four)}"
+    age = st.sidebar.slider("Age", 0, 130, 25)
+    income = st.sidebar.slider("Yearly Income", 0, 1000000, 120000)
+    person_home_ownership = st.sidebar.selectbox(
+        "Do you own or rent your home?", ("RENT", "MORTGAGE", "OWN")
+    )
+
+    employment = st.sidebar.slider(
+        "How long have you been employed (months)?", 0, 120, 12
+    )
+
+    loan_intent = st.sidebar.selectbox(
+        "Why do you want to apply for a loan?",
+        (
+            "PERSONAL",
+            "VENTURE",
+            "HOMEIMPROVEMENT",
+            "EDUCATION",
+            "MEDICAL",
+            "DEBTCONSOLIDATION",
+        ),
+    )
+
+    amount = st.sidebar.slider("Loan amount", 0, 100000, 10000)
+    interest = st.sidebar.slider("Preferred interest rate", 1.0, 25.0, 12.0, step=0.1)
+    return OrderedDict(
+        {
+            "zipcode": [int(zipcode)],
+            "dob_ssn": [dob_ssn],
+            "person_age": [age],
+            "person_income": [income],
+            "person_home_ownership": [person_home_ownership],
+            "person_emp_length": [float(employment)],
+            "loan_intent": [loan_intent],
+            "loan_amnt": [amount],
+            "loan_int_rate": [interest],
+        }
+    )
+
+
+# Application
+st.title("Loan Application")
+
+# Input Side Bar
+st.header("User input:")
+loan_request = get_loan_request()
+df = pd.DataFrame.from_dict(loan_request)
+
+logging.debug(f"User input: {loan_request}")
+st.write(df)
+
+# Full feature vector
+st.header("Feature vector (user input + zipcode features + user features):")
+vector = model._get_online_features_from_feast(loan_request)
+ordered_vector = loan_request.copy()
+key_list = vector.keys()
+key_list = sorted(key_list)
+for vector_key in key_list:
+    if vector_key not in ordered_vector:
+        ordered_vector[vector_key] = vector[vector_key]
+df = pd.DataFrame.from_dict(ordered_vector)
+
+logging.debug(f"Online features from Feast: {vector}")
+st.write(df)
+
+# Results of prediction
+st.header("Application Status (model prediction):")
+result = model.predict(loan_request)
+
+if result == 0:
+    st.success("Your loan has been approved!")
+elif result == 1:
+    st.error("Your loan has been rejected!")
+
+logging.debug(f"Model prediction result: {result}")
+
+# Feature importance
+st.header("Feature Importance")
+# TODO: Load a sample dataset from feature store instead of a static file
+X = pd.read_parquet("data/training_dataset_sample.parquet")
+X['total_debt_due'] = (X['credit_card_due'] + X['mortgage_due'] + X['student_loan_due'] + X['vehicle_loan_due'] + X['loan_amnt']).astype(float)
+explainer = shap.TreeExplainer(model.classifier)
+shap_values = explainer.shap_values(X)
+left, right = st.columns(2)
+with left:
+    fig, ax = plt.subplots()
+    plt.title("Feature importance based on SHAP values")
+    shap.summary_plot(shap_values[:,:,1], X)  # Select only the values for class 1
+    st.pyplot(fig, bbox_inches="tight")
+    st.write("---")
+
+with right:
+    fig, ax = plt.subplots()
+    plt.title("Feature importance based on SHAP values (Bar)")
+    shap.summary_plot(shap_values[:,:,1], X, plot_type="bar")  # Select only the values for class 1
+    st.pyplot(fig, bbox_inches="tight")
+
+logging.debug("Streamlit app finished.")