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app.py

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+import scipy
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from keras.models import load_model
+import pickle
+
+def mnist_prediction(test_image, model='KNN'):
+  test_image_flatten = test_image.reshape((-1, 28*28))
+  if model == 'KNN':
+    with open('KNN_best_model_final.pkl', 'rb') as file:
+      knn_loaded = pickle.load(file)
+    ans = knn_loaded.predict(test_image_flatten)
+    return ans[0]
+  elif model == 'SoftMax':
+    with open('softmax_best_model_final.pkl', 'rb') as file:
+      softmax_model_loaded = pickle.load(file)
+    ans = softmax_model_loaded.predict(test_image_flatten)
+    return ans[0]
+  elif model == 'Deep Neural Network':
+    dnn_model = load_model("deep_nn_model_final.h5")
+    ans_prediction = dnn_model.predict(np.asarray(test_image_flatten))
+    ans = np.argmax(ans_prediction)
+    return ans
+  elif model == 'CNN':
+    cnn_model = load_model("cnn_model_final.h5")
+    ans_prediction = cnn_model.predict(np.asarray([test_image]))
+    ans = np.argmax(ans_prediction)
+    return ans
+  elif model == 'SVM':
+    with open('svm_best_model_final.pkl', 'rb') as file:
+      svm_model_loaded = pickle.load(file)
+    ans = svm_model_loaded.predict(test_image_flatten)
+    return ans[0]
+  elif model == 'Decision Tree':
+    with open('tree_model_final.pkl', 'rb') as file:
+      tree_model_loaded = pickle.load(file)
+    ans = tree_model_loaded.predict(test_image_flatten)
+    return ans[0]
+  elif model == 'Random Forest':
+    with open('forest_model_final.pkl', 'rb') as file:
+      forest_model_loaded = pickle.load(file)
+    ans = forest_model_loaded.predict(test_image_flatten)
+    return ans[0]
+  return "Not found"
+
+input_image = gr.inputs.Image(shape=(28, 28), image_mode='L')
+input_model = gr.inputs.Dropdown(['KNN', 'SoftMax', 'Deep Neural Network', 'CNN', 'SVM', 'Decision Tree', 'Random Forest'])
+
+output_label = gr.outputs.Textbox(label="Predicted Digit")
+
+gr.Interface(fn=mnist_prediction, 
+                         inputs = [input_image, input_model], 
+                         outputs = output_label,
+                         title = "MNIST classification", 
+                         ).launch(debug=True)