Create app.py

app.py

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+import gradio as gr
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+import joblib
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.neighbors import KNeighborsClassifier
+import numpy as np 
+
+import gradio as gr
+# define image data type
+input_image = gr.inputs.Image(label = "Input Image")
+select_algorithm = gr.inputs.Dropdown(choices=["Decision Tree", "Random Forest", "AdaBoost", "Gradient Tree Boosting", "KNN"], label = "Select Algorithm")
+
+out_classify = gr.outputs.Textbox(label = "Predict Class")
+out_prob = gr.outputs.Textbox(label = "Predict Probability")
+
+"""
+gradio interface
+"""
+def predict_interface(input_image, select_algorithm):
+  """
+  evaluate model
+  """
+  # Convert image to NumPy array
+  print(input_image.shape)
+  input_image2 = input_image.mean(axis=2)
+  print(input_image2.shape)
+  img_array = input_image2.reshape(1, 28*28)
+
+
+  model_dict = {"Decision Tree":"best_dt_model.joblib",
+                "Random Forest":"best_rf_model.joblib",
+                "AdaBoost":"best_ada_model.joblib",
+                "Gradient Tree Boosting":"best_gbc_model.joblib",
+                "KNN":"best_knn_model.joblib"}
+  # Reload the best trained model from disk using joblib
+  loaded_model = joblib.load(model_dict[select_algorithm])
+
+  # Use the reloaded model to make predictions on the validation data
+  out_classify = loaded_model.predict(img_array)
+  out_prob = loaded_model.predict_proba(img_array)
+  class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
+"Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]
+  out_prob2 = '\n'.join([f"{name}\t\t\t{np.round(pro,2)}" for name, pro in zip(class_names, out_prob[0])])
+  return class_names[out_classify[0]], out_prob2 
+
+gr.Interface(fn=predict_interface, inputs=[input_image, select_algorithm], 
+             outputs=[out_classify, out_prob],
+             examples=[["fashion_1.png", "Random Forest"], 
+                       ["fashion_2.png", "Random Forest"], 
+                       ["fashion_3.png", "Random Forest"]]
+             ).launch(debug=True)