Upload app (2).py

app (2).py

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+# -*- coding: utf-8 -*-
+"""app
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1B_g2XLYu46kFDIFzNnnJzBQ0GBPssCQw
+"""
+
+import pickle
+import pandas as pd
+import shap
+from shap.plots._force_matplotlib import draw_additive_plot
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load the model
+loaded_model = pickle.load(open("salar_xgb_team.pkl", 'rb'))
+
+# Setup SHAP (do not change)
+explainer = shap.Explainer(loaded_model)
+
+# Define main prediction function
+def main_func(age, education_num, sex, capital_gain, capital_loss, hours_per_week):
+    sex = 1 if sex == "Male" else 0
+    new_row = pd.DataFrame.from_dict({
+        'age': age,
+        'education-num': education_num,
+        'sex': sex,
+        'capital-gain': capital_gain,
+        'capital-loss': capital_loss,
+        'hours-per-week': hours_per_week
+    }, orient='index').transpose()
+
+    prob = loaded_model.predict_proba(new_row)
+
+    shap_values = explainer(new_row)
+    plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)
+
+    plt.tight_layout()
+    local_plot = plt.gcf()
+    plt.close()
+
+    return {"≤ $50K": float(prob[0][0]), "> $50K": float(prob[0][1])}, local_plot
+
+# Gradio UI
+title = "**Household Income Predictor & Interpreter** 💰"
+description1 = """This app takes demographic and economic features to predict whether a household earns ≤ $50K or > $50K annually.🚀"""
+description2 = """Adjust the values and click Analyze to get predictions and feature importance."""
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description1)
+    gr.Markdown("""---""")
+    gr.Markdown(description2)
+    gr.Markdown("""---""")
+
+    gr.Image("Household.png")
+
+    age = gr.Number(label="Age", value=35)
+    education_num = gr.Number(label="Education Level (numeric)", value=10)
+    sex = gr.Radio(choices=["Male", "Female"], label="Sex", value="Male")
+    capital_gain = gr.Number(label="Capital Gain", value=0)
+    capital_loss = gr.Number(label="Capital Loss", value=0)
+    hours_per_week = gr.Number(label="Hours per Week", value=40)
+    # salary_class = gr.Number(label="(Optional) Salary Class for SHAP Context", value=0)  # Can remove if not needed
+
+    submit_btn = gr.Button("Analyze")
+
+    with gr.Column(visible=True) as output_col:
+        label = gr.Label(label="Predicted Income")
+        local_plot = gr.Plot(label='SHAP Interpretation:')
+
+    submit_btn.click(
+        main_func,
+        [age, education_num, sex, capital_gain, capital_loss, hours_per_week],
+        [label, local_plot], api_name="Income_Predictor"
+    )
+
+    gr.Markdown("### Try these examples:")
+    gr.Examples(
+        [[39,13, "Male", 0, 0, 40], [52, 9, "Female", 0, 1876, 45]],
+        [age, education_num, sex, capital_gain, capital_loss, hours_per_week],
+        [label, local_plot],
+        main_func,
+        cache_examples=True
+    )
+
+demo.launch()