Update app.py

app.py

@@ -1,3 +1,74 @@
+# import gradio as gr
+# import pickle
+# from sklearn.neighbors import KNeighborsClassifier
+# from sklearn.linear_model import LogisticRegression
+# from keras.models import Sequential
+# from keras.layers import Dense
+# from sklearn.pipeline import Pipeline
+# from sklearn.svm import LinearSVC
+# from sklearn.tree import DecisionTreeClassifier
+# from sklearn.ensemble import RandomForestClassifier
+# import tensorflow as tf
+# import sklearn
+# import tensorflow
+# from tensorflow import keras
+# from tensorflow.keras.models import load_model
+
+# input_1 = gr.Image(shape=(28,28),image_mode='L')
+ 
+# input_2 = gr.Dropdown(["SoftMax", "KNN", "Deep Neural Network", "Decision Tree", "Random Forest"])
+
+# output = gr.Label(num_top_classes=6)
+
+# def predict_softmax(test_img):
+#   Softmax_model = pickle.load(open('softmax_model.pkl', 'rb'))
+#   predictions = Softmax_model.predict_proba(test_img)
+#   return {i: float(predictions[0][i]) for i in range(0,10)}
+  
+# def predict_knn(test_img):
+#   Knn_model = pickle.load(open('knn_model.pkl', 'rb'))
+#   predictions = Knn_model.predict_proba(test_img)
+#   return {i: float(predictions[0][i]) for i in range(0,10)}
+    
+# def predict_neural(test_img):
+#   Neural_model = load_model("deep_neural_model.h5")
+#   predictions = Neural_model.predict(test_img)
+#   return {i: float(predictions[0][i]) for i in range(0,10)}
+  
+# def predict_tree(test_img):
+#   tree_model = pickle.load(open('tree_clf.pkl', 'rb'))
+#   predictions = tree_model.predict_proba(test_img)
+#   return {i: float(predictions[0][i]) for i in range(0,10)}
+  
+# def predict_rf(test_img):
+#   rf_model = pickle.load(open('rf_clf.pkl', 'rb'))
+#   predictions = rf_model.predict_proba(test_img)
+#   return {i: float(predictions[0][i]) for i in range(0,10)}
+
+# def predictDigitClass(test_img,chosen_model):
+#   test_img_flatten=test_img.reshape(-1,28*28)
+#   if chosen_model == "SoftMax":
+#     fashionProbs = predict_softmax(test_img_flatten)
+#     return fashionProbs
+#   elif chosen_model == "KNN":
+#     fashionProbs = predict_knn(test_img_flatten)
+#     return fashionProbs  
+#   elif chosen_model == "Deep Neural Network":
+#     fashionProbs = predict_neural(test_img_flatten)
+#     return fashionProbs 
+#   elif chosen_model == "SVM":
+#     fashionProbs = predict_svm(test_img_flatten)
+#     return fashionProbs     
+#   elif chosen_model == "Decision Tree":
+#     fashionProbs = predict_tree(test_img_flatten)
+#     return fashionProbs
+#   elif chosen_model == "Random Forest":
+#     fashionProbs = predict_rf(test_img_flatten)
+#     return fashionProbs
+    
+# gr.Interface(fn=predictDigitClass,inputs=[input_1,input_2],outputs=output).launch(debug=True)
+
+
 import gradio as gr
 import pickle
 from sklearn.neighbors import KNeighborsClassifier
@@ -9,61 +80,54 @@ from sklearn.svm import LinearSVC
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.ensemble import RandomForestClassifier
 import tensorflow as tf
-import sklearn
-import tensorflow
-from tensorflow import keras
 from tensorflow.keras.models import load_model
 
-input_1 = gr.Image(shape=(28,28),image_mode='L')
- 
+input_1 = gr.Image(image_mode='L', type='numpy', preprocessing=lambda img: img.resize((28, 28)))
 input_2 = gr.Dropdown(["SoftMax", "KNN", "Deep Neural Network", "Decision Tree", "Random Forest"])
-
 output = gr.Label(num_top_classes=6)
 
 def predict_softmax(test_img):
-  Softmax_model = pickle.load(open('softmax_model.pkl', 'rb'))
-  predictions = Softmax_model.predict_proba(test_img)
-  return {i: float(predictions[0][i]) for i in range(0,10)}
-  
+    Softmax_model = pickle.load(open('softmax_model.pkl', 'rb'))
+    predictions = Softmax_model.predict_proba(test_img)
+    return {i: float(predictions[0][i]) for i in range(0, 10)}
+
 def predict_knn(test_img):
-  Knn_model = pickle.load(open('knn_model.pkl', 'rb'))
-  predictions = Knn_model.predict_proba(test_img)
-  return {i: float(predictions[0][i]) for i in range(0,10)}
-    
+    Knn_model = pickle.load(open('knn_model.pkl', 'rb'))
+    predictions = Knn_model.predict_proba(test_img)
+    return {i: float(predictions[0][i]) for i in range(0, 10)}
+
 def predict_neural(test_img):
-  Neural_model = load_model("deep_neural_model.h5")
-  predictions = Neural_model.predict(test_img)
-  return {i: float(predictions[0][i]) for i in range(0,10)}
-  
+    Neural_model = load_model("deep_neural_model.h5")
+    predictions = Neural_model.predict(test_img)
+    return {i: float(predictions[0][i]) for i in range(0, 10)}
+
 def predict_tree(test_img):
-  tree_model = pickle.load(open('tree_clf.pkl', 'rb'))
-  predictions = tree_model.predict_proba(test_img)
-  return {i: float(predictions[0][i]) for i in range(0,10)}
-  
+    tree_model = pickle.load(open('tree_clf.pkl', 'rb'))
+    predictions = tree_model.predict_proba(test_img)
+    return {i: float(predictions[0][i]) for i in range(0, 10)}
+
 def predict_rf(test_img):
-  rf_model = pickle.load(open('rf_clf.pkl', 'rb'))
-  predictions = rf_model.predict_proba(test_img)
-  return {i: float(predictions[0][i]) for i in range(0,10)}
+    rf_model = pickle.load(open('rf_clf.pkl', 'rb'))
+    predictions = rf_model.predict_proba(test_img)
+    return {i: float(predictions[0][i]) for i in range(0, 10)}
+
+def predictDigitClass(test_img, chosen_model):
+    test_img_flatten = test_img.reshape(-1, 28*28)
+    if chosen_model == "SoftMax":
+        fashionProbs = predict_softmax(test_img_flatten)
+        return fashionProbs
+    elif chosen_model == "KNN":
+        fashionProbs = predict_knn(test_img_flatten)
+        return fashionProbs  
+    elif chosen_model == "Deep Neural Network":
+        fashionProbs = predict_neural(test_img_flatten)
+        return fashionProbs 
+    elif chosen_model == "Decision Tree":
+        fashionProbs = predict_tree(test_img_flatten)
+        return fashionProbs
+    elif chosen_model == "Random Forest":
+        fashionProbs = predict_rf(test_img_flatten)
+        return fashionProbs
+
+gr.Interface(fn=predictDigitClass, inputs=[input_1, input_2], outputs=output).launch(debug=True)
 
-def predictDigitClass(test_img,chosen_model):
-  test_img_flatten=test_img.reshape(-1,28*28)
-  if chosen_model == "SoftMax":
-    fashionProbs = predict_softmax(test_img_flatten)
-    return fashionProbs
-  elif chosen_model == "KNN":
-    fashionProbs = predict_knn(test_img_flatten)
-    return fashionProbs  
-  elif chosen_model == "Deep Neural Network":
-    fashionProbs = predict_neural(test_img_flatten)
-    return fashionProbs 
-  elif chosen_model == "SVM":
-    fashionProbs = predict_svm(test_img_flatten)
-    return fashionProbs     
-  elif chosen_model == "Decision Tree":
-    fashionProbs = predict_tree(test_img_flatten)
-    return fashionProbs
-  elif chosen_model == "Random Forest":
-    fashionProbs = predict_rf(test_img_flatten)
-    return fashionProbs
-    
-gr.Interface(fn=predictDigitClass,inputs=[input_1,input_2],outputs=output).launch(debug=True)