Upload streamlit_app.py

src/streamlit_app.py

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+import os
+import joblib
+import numpy as np
+import pandas as pd
+import requests
+import streamlit as st
+import shap
+import tempfile
+import streamlit.components.v1 as components
+import scipy.sparse as sp 
+
+# Setup
+API_URL = os.getenv("API_URL", "https://signe22-diabetes-prediction-api.hf.space/predict_batch")
+NUMERIC_FEATURES = ['age','alcohol_consumption_per_week','physical_activity_minutes_per_week',
+                    'diet_score','bmi','cholesterol_total','insulin_level','map','glucose_fasting']
+CATEGORICAL_FEATURES = ['gender','ethnicity','education_level','income_level','employment_status',
+                        'smoking_status','family_history_diabetes','hypertension_history','cardiovascular_history']
+ALL_FEATURES = NUMERIC_FEATURES + CATEGORICAL_FEATURES
+
+st.set_page_config(page_title="Diabetes Predictions", layout="wide")
+
+# Function for SHAP plot
+def st_shap(plot, height=None):
+    """Renders a SHAP plot in Streamlit using standalone HTML + JS and white background."""
+    import tempfile, os
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as tmpfile:
+        shap.save_html(tmpfile.name, plot)
+        html = open(tmpfile.name, "r").read()
+        os.unlink(tmpfile.name)
+
+    # HTML style
+    styled_html = f"""
+    <div style="
+        background-color: white;
+        padding: 20px;
+        border-radius: 12px;
+        box-shadow: 0 0 10px rgba(0,0,0,0.05);
+    ">
+        {html}
+    </div>
+    """
+
+    components.html(styled_html, height=height or 500, width=1000, scrolling=True)
+
+def _to_dense(X):
+    """Ensure X is a dense NumPy array (convert from sparse if needed)."""
+    if sp.issparse(X):
+        return X.toarray()
+    return np.asarray(X)
+
+# Function to create synthetic data
+def synth_data(n_policyholders=100, seed=42):
+    rng = np.random.default_rng(seed); n = n_policyholders
+    age = rng.normal(50, 15, n).clip(18, 90)
+    alcohol = rng.gamma(2, 3, n).clip(0, 40)
+    activity = rng.normal(150, 60, n).clip(0, 600)
+    diet = rng.uniform(1, 10, n)
+    bmi = rng.normal(27, 5, n).clip(15, 50)
+    chol = rng.normal(200, 40, n).clip(100, 400)
+    insulin = rng.normal(10, 5, n).clip(2, 40)
+    map_ = rng.normal(95, 10, n).clip(70, 130)
+    glucose = rng.normal(100, 25, n).clip(60, 250)
+    gender = np.random.choice(['Male','Female','Other'], n, p=[0.48,0.5,0.02])
+    ethnicity = np.random.choice(['White','Black','Asian','Hispanic','Other'], n)
+    edu = np.random.choice(['High School','Bachelor','Master','PhD'], n, p=[0.4,0.35,0.2,0.05])
+    income = np.random.choice(['Low','Middle','High'], n, p=[0.3,0.5,0.2])
+    emp = np.random.choice(['Employed','Unemployed','Retired','Student'], n, p=[0.6,0.1,0.25,0.05])
+    smoke = np.random.choice(['Never','Former','Current'], n, p=[0.6,0.25,0.15])
+    fam = np.random.choice(['Yes','No'], n, p=[0.35,0.65])
+    hyper = np.random.choice(['Yes','No'], n, p=[0.3,0.7])
+    cardio = np.random.choice(['Yes','No'], n, p=[0.2,0.8])
+    df = pd.DataFrame({
+        'age':age,'alcohol_consumption_per_week':alcohol,'physical_activity_minutes_per_week':activity,
+        'diet_score':diet,'bmi':bmi,'cholesterol_total':chol,'insulin_level':insulin,'map':map_,'glucose_fasting':glucose,
+        'gender':gender,'ethnicity':ethnicity,'education_level':edu,'income_level':income,
+        'employment_status':emp,'smoking_status':smoke,'family_history_diabetes':fam,
+        'hypertension_history':hyper,'cardiovascular_history':cardio
+    })
+    df.insert(0, 'policyholder_id', range(1, len(df)+1))
+    return df
+
+def call_api(df: pd.DataFrame):
+    payload = {'data': df[ALL_FEATURES].to_dict(orient="records")}
+    r = requests.post(API_URL, json=payload, timeout=60); r.raise_for_status()
+    return np.array(r.json()["probabilities"])
+
+# Cache model to make it faster
+@st.cache_resource
+def load_model():
+    return joblib.load("diabetes_prediction_model_20251007.pkl")
+
+pipe = load_model()
+
+# Initiate state
+if "df" not in st.session_state:
+    st.session_state.df = None
+if "selected_id" not in st.session_state:
+    st.session_state.selected_id = None
+
+# Sidebar with filters 
+with st.sidebar:
+    st.header("Simulation")
+    n_policyholders = st.slider("Synthetic policyholders per day", 10, 200, 20, 10)
+    seed = st.number_input("Random seed", 0, 99999, 42, 1)
+    threshold = st.slider("Classification threshold", 0.0, 1.0, 0.24, 0.01)
+
+    if st.button("Generate & Predict", use_container_width=True):
+        df = synth_data(n_policyholders=n_policyholders, seed=int(seed))
+        probs = call_api(df)
+        df["predicted_risk"] = probs
+        df['risk_category'] = np.where(df['predicted_risk'] >= threshold, "High-risk", "Low-risk")
+        st.session_state.df = df
+        # reset selection to first id for consistency
+        st.session_state.selected_id = int(df["policyholder_id"].iloc[0])
+        st.success("Predictions received from API")
+
+    if st.session_state.df is not None:
+        st.session_state.selected_id = st.selectbox(
+            "Select Policyholder ID:",
+            st.session_state.df["policyholder_id"].tolist(),
+            index=(
+                st.session_state.df["policyholder_id"].tolist().index(st.session_state.selected_id)
+                if st.session_state.selected_id in st.session_state.df["policyholder_id"].tolist()
+                else 0
+            ),
+            key="selected_id_widget"
+        )
+
+# Main outputs of model
+st.title("Predictions of high risk diabetes")
+st.caption("API: " + API_URL)
+
+if st.session_state.df is None:
+    st.info("Generate predictions first to enable results and SHAP explanation.")
+else:
+    df = st.session_state.df
+    st.write("### Results", df[["policyholder_id", "predicted_risk", "risk_category"]])
+    st.metric("Average predicted diabetes risk", f"{df['predicted_risk'].mean():.2%}")
+
+    st.header("Explain Prediction for a Policyholder")
+    # Set up SHAP explanation for a chosen policyholder
+    selected_id = st.session_state.selected_id
+    if selected_id is not None:
+        row = df.loc[df["policyholder_id"] == selected_id, ALL_FEATURES]
+
+        preprocessor = pipe.named_steps["preprocessor"]
+        model = pipe.named_steps["classifier"]
+
+        X_all = preprocessor.transform(df[ALL_FEATURES])
+        X_row = preprocessor.transform(row)
+        feature_names = preprocessor.get_feature_names_out(ALL_FEATURES)
+
+        explainer = shap.LinearExplainer(model, X_all)
+        shap_values = explainer.shap_values(X_row)
+
+        # Force plot
+        shap_plot = shap.force_plot(
+            explainer.expected_value,
+            shap_values[0],
+            _to_dense(X_row)[0],
+            feature_names=feature_names
+        )
+        st_shap(shap_plot, height=250)
+
+        # Shap df set up for text explanation
+        shap_df = pd.DataFrame({"Feature": feature_names, "SHAP value": shap_values[0]})
+        shap_df = shap_df.reindex(shap_df["SHAP value"].abs().sort_values(ascending=False).index)
+
+        # Text explanation of most important features
+        top_features = shap_df.head(5)  # the 5 most important features
+        increase = top_features[top_features["SHAP value"] > 0]
+        decrease = top_features[top_features["SHAP value"] < 0]
+
+        st.markdown("### Why this prediction?")
+        st.info(
+                f"The model predicts a higher diabetes risk mainly due to **{', '.join(increase['Feature']) or 'none'}**, "
+                f"while lower risk is influenced by **{', '.join(decrease['Feature']) or 'none'}**.")