first try at deployment

app.py

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+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.linear_model import SGDClassifier
+from sklearn.metrics import classification_report, confusion_matrix, make_scorer, f1_score
+import shap
+import xgboost as xgb
+import gradio as gr
+import matplotlib.pyplot as plt
+import joblib
+
+SVM = joblib.load('SVM.pkl')
+Log_Reg = joblib.load('Log_Reg.pkl')
+XGB = xgb.XGBClassifier()
+XGB.load_model('XGB.model')
+
+
+df = pd.read_csv('Superstore.csv')
+df.dropna(subset=["Region", "Category", "Sub-Category", "Quantity", "Discount"], inplace=True)
+MEDIAN = 8.662 # from the exploratory analysis file
+RANDOM_STATE = 42 # random seed to ensure results are reproducible
+
+region=np.unique(df['Region'], return_inverse=True)[1]
+category=np.unique(df['Category'], return_inverse=True)[1]
+subCategory=np.unique(df['Sub-Category'], return_inverse=True)[1]
+
+# turn quantity, discount, and profit columns into vectors of numbers
+quantity = df["Quantity"].to_numpy()
+discount = df["Discount"].to_numpy()
+profit = df["Profit"].to_numpy()
+
+vectorizedDataset = np.empty((len(region), 5))
+labels = np.empty(len(region))
+
+# generate feature vectors
+for i in range(0, len(region)):
+  data = np.zeros((1, 5))
+  data[0][0] = region[i]
+  data[0][1] = category[i]
+  data[0][2] = subCategory[i]
+  data[0][3] = quantity[i]
+  data[0][4] = discount[i]
+
+  vectorizedDataset[i] = data
+
+  if (profit[i] > MEDIAN):
+    labels[i] = 1
+  else:
+    labels[i] = 0
+
+train, test, trainLabels, testLabels = train_test_split(vectorizedDataset, labels, test_size=0.3, random_state=RANDOM_STATE)
+
+region_label = {'Central': 0, 'East': 1, 'South': 2, 'West': 3}
+category_label = {'Furniture': 0, 'Office Supplies': 1, 'Technology': 2}
+sub_category_label = {'Accessories': 0, 'Appliances': 1, 'Art': 2, 'Binders': 3, 'Bookcases': 4,
+       'Chairs': 5, 'Copiers': 6, 'Envelopes': 7, 'Fasteners': 8, 'Furnishings': 9,
+       'Labels': 10, 'Machines': 11, 'Paper': 12, 'Phones': 13, 'Storage': 14, 'Supplies': 15,
+       'Tables': 16}
+profit_label = {0: 'Below Median Profit', 1: 'Above Median Profit'}
+feature_names = ["Region", "Category", "Sub-Category", "Quantity", "Discount"]
+
+def sanitize_inputs(Region, Category, Sub_Category, Quantity, Discount):
+  try:
+    Region = region_label[Region]
+    Category = category_label[Category]
+    Sub_Category = sub_category_label[Sub_Category]
+  except KeyError:
+    return ["Please provide region, category, and sub category from the pre-defined Superstore dataset classes", None]
+
+  if Quantity < 1 or Discount < 0:
+    return ["Quantity and Discount must be positive", None]
+
+  if not isinstance(Quantity, int):
+    return ["Quantity must be an integer", None]
+
+  if Discount > 1:
+     return ["Discount cannot be greater than one", None]
+
+  return [Region, Category, Sub_Category]
+
+def XGB_predict(Region, Category, Sub_Category, Quantity, Discount):
+  sanitized = sanitize_inputs(Region, Category, Sub_Category, Quantity, Discount)
+
+  if len(sanitized)==2:
+    return sanitized
+
+  input = np.array([[sanitized[0], sanitized[1], sanitized[2], Quantity, Discount]])
+  predicted_class = XGB.predict(input)
+
+  explainer = shap.Explainer(XGB, test)
+  shap_values = explainer(input)
+  shap_values.feature_names = ["Region", "Category", "Sub-Category", "Quantity", "Discount"]
+
+  plot = shap.plots.bar(shap_values, show=False)
+  plt.savefig('shap_plot_XGB.png')
+
+  return [profit_label[predicted_class[0]], 'shap_plot_XGB.png']
+
+def SVM_predict(Region, Category, Sub_Category, Quantity, Discount):
+  sanitized = sanitize_inputs(Region, Category, Sub_Category, Quantity, Discount)
+
+  if len(sanitized)==2:
+    return sanitized
+
+  input = np.array([[sanitized[0], sanitized[1], sanitized[2], Quantity, Discount]])
+  predicted_class = SVM.predict(input)
+
+  explainer = shap.Explainer(SVM, test)
+  shap_values = explainer(input)
+  shap_values.feature_names = ["Region", "Category", "Sub-Category", "Quantity", "Discount"]
+
+  plot = shap.plots.bar(shap_values, show=False)
+  plt.savefig('shap_plot_SVM.png')
+
+  return [profit_label[predicted_class[0]], 'shap_plot_SVM.png']
+
+def Log_reg_predict(Region, Category, Sub_Category, Quantity, Discount):
+  sanitized = sanitize_inputs(Region, Category, Sub_Category, Quantity, Discount)
+
+  if len(sanitized)==2:
+    return sanitized
+
+  input = np.array([[sanitized[0], sanitized[1], sanitized[2], Quantity, Discount]])
+  predicted_class = Log_Reg.predict(input)
+
+  explainer = shap.Explainer(Log_Reg, test)
+  shap_values = explainer(input)
+  shap_values.feature_names = ["Region", "Category", "Sub-Category", "Quantity", "Discount"]
+
+  plot = shap.plots.bar(shap_values, show=False)
+  plt.savefig('shap_plot_LogReg.png')
+
+  return [profit_label[predicted_class[0]], 'shap_plot_LogReg.png']
+
+
+
+LogReg_tab = gr.Interface(
+    fn=Log_reg_predict,
+    inputs=["text", "text", "text", "number", "number"],
+    outputs=[
+        gr.Label(label="Model Prediction"),
+        gr.Image(label="Shapley Values"),
+    ],
+    title="Logistic Regression Profit Prediction",
+    description="Create your own purchases and see if the Logistic Regression model predicts they will make above or below the median profit\n\nValid regions: ['Central', 'East', 'South', 'West']\n\nValid product categories: ['Furniture', 'Office Supplies', 'Technology']\n\nValid product sub-categories: ['Accessories', 'Appliances', 'Art', 'Binders', 'Bookcases', 'Chairs', 'Copiers', 'Envelopes', 'Fasteners', 'Furnishings', 'Labels', 'Machines', 'Paper', 'Phones', 'Storage', 'Supplies', 'Tables']",
+)
+
+SVM_tab = gr.Interface(
+    fn=SVM_predict,
+    inputs=["text", "text", "text", "number", "number"],
+    outputs=[
+        gr.Label(label="Model Prediction"),
+        gr.Image(label="Shapley Values"),
+    ],
+    title="SVM Profit Prediction",
+    description="Create your own purchases and see if the SVM model predicts they will make above or below the median profit\n\nValid regions: ['Central', 'East', 'South', 'West']\n\nValid product categories: ['Furniture', 'Office Supplies', 'Technology']\n\nValid product sub-categories: ['Accessories', 'Appliances', 'Art', 'Binders', 'Bookcases', 'Chairs', 'Copiers', 'Envelopes', 'Fasteners', 'Furnishings', 'Labels', 'Machines', 'Paper', 'Phones', 'Storage', 'Supplies', 'Tables']",
+)
+
+XGB_tab = gr.Interface(
+    fn=XGB_predict,
+    inputs=["text", "text", "text", "number", "number"],
+    outputs=[
+        gr.Label(label="Model Prediction"),
+        gr.Image(label="Shapley Values"),
+    ],
+    title="XGB Profit Prediction",
+    description="Create your own purchases and see if the XGB model predicts they will make above or below the median profit\n\nValid regions: ['Central', 'East', 'South', 'West']\n\nValid product categories: ['Furniture', 'Office Supplies', 'Technology']\n\nValid product sub-categories: ['Accessories', 'Appliances', 'Art', 'Binders', 'Bookcases', 'Chairs', 'Copiers', 'Envelopes', 'Fasteners', 'Furnishings', 'Labels', 'Machines', 'Paper', 'Phones', 'Storage', 'Supplies', 'Tables']",
+)
+
+
+
+demo = gr.TabbedInterface([LogReg_tab, SVM_tab, XGB_tab], tab_names=["Logistic Regression", "SVM", "XGB"], theme=gr.themes.Soft())
+
+demo.launch(debug=True, share=True)

requirements.txt

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+pandas==2.0.3
+numpy==1.25.2
+scikit-learn==1.2.2
+shap==0.45.0
+shapely==2.0.4
+xgboost==2.0.3
+matplotlib==3.7.1
+joblib==1.4.0