Update app.py

app.py

@@ -1,106 +1,243 @@
 import pickle
+
 import pandas as pd
+
 import shap
+
 import gradio as gr
+
 import numpy as np
+
 import matplotlib.pyplot as plt
  
-# Load the model
+# Load model
+
 loaded_model = pickle.load(open("salar_xgb_team.pkl", "rb"))
  
-# Setup SHAP
-explainer = shap.Explainer(loaded_model)  # DO NOT CHANGE THIS
- 
-# Main function
-def main_func(age, education_num, sex, capital_gain, capital_loss, hours_per_week):
-    # Input validation
-    if age < 18 or age > 100 or education_num < 1 or hours_per_week < 1 or hours_per_week > 100:
+# SHAP explainer (Do not change)
+
+explainer = shap.Explainer(loaded_model)
+ 
+# Mapping from dropdown labels to numeric education levels
+
+education_map = {
+
+    "Preschool": 1,
+
+    "1st-4th": 2,
+
+    "5th-6th": 3,
+
+    "7th-8th": 4,
+
+    "9th": 5,
+
+    "10th": 6,
+
+    "11th": 7,
+
+    "12th": 8,
+
+    "HS-grad": 9,
+
+    "Some-college": 10,
+
+    "Assoc-voc": 11,
+
+    "Assoc-acdm": 12,
+
+    "Bachelors": 13,
+
+    "Masters": 14,
+
+    "Prof-school": 15,
+
+    "Doctorate": 16
+
+}
+ 
+# Main prediction function
+
+def main_func(age, education_label, sex, capital_gain, capital_loss, hours_per_week):
+
+    # Validate input
+
+    if age < 18 or age > 100 or hours_per_week < 1 or hours_per_week > 100:
+
         return {"≤50K": 0.0, ">50K": 0.0}, None, "❌ Invalid inputs. Please check your entries."
  
-    # Process categorical
+    # Convert to model format
+
+    education_num = education_map.get(education_label, 9)  # default to HS-grad
+
     sex_binary = 0 if sex == "Male" else 1
  
-    # Create input row
+    # Build dataframe
+
     new_row = pd.DataFrame({
+
         'age': [age],
+
         'education-num': [education_num],
+
         'sex': [sex_binary],
+
         'capital-gain': [capital_gain],
+
         'capital-loss': [capital_loss],
+
         'hours-per-week': [hours_per_week]
+
     })
  
-    # Predict
+    # Predict and explain
+
     prob = loaded_model.predict_proba(new_row)
+
     shap_values = explainer(new_row)
  
     # SHAP plot
+
     plt.figure(figsize=(8, 4))
+
     shap.plots.bar(shap_values[0], max_display=6, show=False)
+
     plt.tight_layout()
+
     local_plot = plt.gcf()
+
     plt.close()
  
-    # Predicted class and confidence
+    # Class and confidence
+
     pred_class = ">50K" if prob[0][1] > 0.5 else "≤50K"
+
     confidence = round(prob[0][1] if pred_class == ">50K" else prob[0][0], 2)
- 
+
     interpretation = f"💼 Prediction: **{pred_class}**\nConfidence: {confidence * 100:.2f}%"
  
     return {
+
         "≤50K": round(prob[0][0], 2),
+
         ">50K": round(prob[0][1], 2)
+
     }, local_plot, interpretation
  
-# ----------- Gradio UI -----------
+# UI layout
+
 title = "**Salary Predictor & SHAP Explainer** 💰"
+
 description1 = "This app uses demographic and financial info to predict whether someone earns more than $50K annually."
-description2 = "Adjust the sliders and inputs below, then click **Analyze** to see the prediction and SHAP explanation."
+
+description2 = "Adjust the inputs and click **Analyze** to see prediction and SHAP feature contributions."
  
 with gr.Blocks(title=title) as demo:
+
     gr.Markdown(f"## {title}")
+
     gr.Markdown(description1)
+
     gr.Markdown("---")
+
     gr.Markdown(description2)
+
     gr.Markdown("---")
  
     with gr.Row():
+
         with gr.Column(scale=1):
-            age = gr.Slider(label="Age (Years)", minimum=18, maximum=100, value=35, info="Enter age between 18 and 100")
-            education_num = gr.Slider(label="Education Level (Numerical)", minimum=1, maximum=16, value=10, info="E.g., 1 = Preschool, 16 = Doctorate")
+
+            age = gr.Number(label="Age", value=35, precision=0)
+
+            education_label = gr.Dropdown(
+
+                choices=list(education_map.keys()),
+
+                label="Education Level",
+
+                value="HS-grad"
+
+            )
+
             sex = gr.Radio(["Male", "Female"], label="Sex")
+
             capital_gain = gr.Number(label="Capital Gain", value=0)
+
             capital_loss = gr.Number(label="Capital Loss", value=0)
+
             hours_per_week = gr.Slider(label="Hours Worked per Week", minimum=1, maximum=100, value=40)
  
             submit_btn = gr.Button("🔍 Analyze")
  
         with gr.Column(scale=1):
+
             label = gr.Label(label="Predicted Probabilities")
+
             local_plot = gr.Plot(label="SHAP Feature Importance")
+
             result_text = gr.Textbox(label="Prediction Summary", lines=2)
  
     submit_btn.click(
+
         main_func,
-        [age, education_num, sex, capital_gain, capital_loss, hours_per_week],
+
+        [age, education_label, sex, capital_gain, capital_loss, hours_per_week],
+
         [label, local_plot, result_text],
+
         api_name="Salary_Predictor"
+
     )
  
     gr.Markdown("### Try one of the following examples:")
+
     gr.Examples(
+
         examples=[
-            [28, 12, "Male", 0, 0, 45],
-            [52, 14, "Female", 7688, 0, 60],
-            [35, 9, "Male", 0, 1902, 40]
+
+            [28, "Some-college", "Male", 0, 0, 45],
+
+            [52, "Masters", "Female", 7688, 0, 60],
+
+            [35, "HS-grad", "Male", 0, 1902, 40]
+
         ],
-        inputs=[age, education_num, sex, capital_gain, capital_loss, hours_per_week],
+
+        inputs=[age, education_label, sex, capital_gain, capital_loss, hours_per_week],
+
         outputs=[label, local_plot, result_text],
+
         fn=main_func,
+
         cache_examples=True
+
     )
  
     gr.Markdown("---")
-    gr.Markdown("Built with ❤️ by Tania Ramesh for the 2025 AI Applications Project.")
+
+    gr.Markdown("Built with ❤️ by Group 3 for the 2025 AI Applications Project.")
+ 
+ 
+    gr.Markdown("---")
+
+    gr.Markdown("📊 Thanks for using the Salary Predictor!")
+
+    gr.Image(
+
+        value="https://media.giphy.com/media/l0MYt5jPR6QX5pnqM/giphy.gif",
+
+        label="",
+
+        show_label=False,
+
+        show_download_button=False,
+
+        height=200
+
+    )
+
  
-demo.launch()
+demo.launch()
+
+