Upload app.py

app.py

@@ -24,45 +24,12 @@ std = np.array([3.9504547e+04, 1.9434261e+00, 1.6578650e+00, 1.4903845e+00,
         6.0594761e-01, 5.0777191e-01, 4.8668963e-01, 4.0041801e-01,
         3.3410770e-01, 2.5052232e+02])
 
-fts_min_max = {'Amount': [0.0, 25691.16],
-                'Time': [0.0, 172792.0],
-                'V1': [-56.407509631329, 2.45492999121121],
-                'V10': [-24.5882624372475, 23.7451361206545],
-                'V11': [-4.79747346479757, 12.0189131816199],
-                'V12': [-18.6837146333443, 7.8483920756446],
-                'V13': [-5.79188120632084, 7.12688295859376],
-                'V14': [-19.2143254902614, 10.5267660517847],
-                'V15': [-4.49894467676621, 8.87774159774277],
-                'V16': [-14.1298545174931, 17.3151115176278],
-                'V17': [-25.1627993693248, 9.25352625047285],
-                'V18': [-9.49874592104677, 5.04106918541184],
-                'V19': [-7.21352743017759, 5.59197142733558],
-                'V2': [-72.7157275629303, 22.0577289904909],
-                'V20': [-54.497720494566, 39.4209042482199],
-                'V21': [-34.8303821448146, 27.2028391573154],
-                'V22': [-10.933143697655, 10.5030900899454],
-                'V23': [-44.8077352037913, 22.5284116897749],
-                'V24': [-2.83662691870341, 4.58454913689817],
-                'V25': [-10.2953970749851, 7.51958867870916],
-                'V26': [-2.60455055280817, 3.5173456116238],
-                'V27': [-22.5656793207827, 31.6121981061363],
-                'V28': [-15.4300839055349, 33.8478078188831],
-                'V3': [-48.3255893623954, 9.38255843282114],
-                'V4': [-5.68317119816995, 16.8753440335975],
-                'V5': [-113.743306711146, 34.8016658766686],
-                'V6': [-26.1605059358433, 73.3016255459646],
-                'V7': [-43.5572415712451, 120.589493945238],
-                'V8': [-73.2167184552674, 20.0072083651213],
-                'V9': [-13.4340663182301, 15.5949946071278]}
-
-def infer(seed):
+# def infer(seed):
+def infer(Time, V1, V2, V3,V4,V5,V6,V7,V8,V9,V10,V11,V12,V13,V14,V15,V16,V17,V18,V19,V20,V21,V22,V23,V24,V25,V26,V27,V28,Amount):
 
     data = pd.DataFrame({
-        col: round(np.random.uniform(fts_min_max[col][0], fts_min_max[col][1]), 0)
-        if col =='Time'
-        else np.random.uniform(fts_min_max[col][0], fts_min_max[col][1])
-        for col in ['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21',
-                    'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount']
+        'Time': Time, 'V1': V1, 'V2':V2, 'V3':V3, 'V4':V4, 'V5':V5, 'V6':V6, 'V7':V7, 'V8':V8, 'V9':V9, 'V10':V10, 'V11':V11, 'V12':V12, 'V13':V13, 'V14':V14, 'V15':V15, 'V16':V16,
+        'V17':V17, 'V18':V18, 'V19':V19, 'V20':V20, 'V21':V21, 'V22':V22, 'V23':V23, 'V24':V24, 'V25':V25, 'V26':V26, 'V27':V27, 'V28':V28, 'Amount':Amount
     }, index=[0])
 
     test_features = data.copy().values
@@ -73,27 +40,81 @@ def infer(seed):
     data = data.round(decimals = 2)
 
     #return f"{round(pred.flatten()[0]*100, 5)}%", data.values.tolist()
-    result = 'This is fraudulent transaction!' if pred.flatten()[0] > 0.5 else "This is real transaction!"
-    return result, data.values.tolist()
+    result = f'This is fraudulent transaction with probability is {round(pred.flatten()[0]*100, 5)}%' if pred.flatten()[0] > 0.5 else f"This is real transaction with probability is {round((1-pred.flatten()[0])*100, 3)}%"
+    return result #, data.values.tolist()
 
 # get the inputs
-inputs = [gr.Slider(minimum=0, maximum=3000, step=1, label='Choose a random number', value=5)]
+inputs = [
+        gr.Slider(minimum=0, maximum=172700, step=10, label='Time', value=10),
+        gr.Slider(minimum=-56, maximum=2, step=0.1, label='V1', value=0),
+        gr.Slider(minimum=-72, maximum=23, step=0.1, label='V2', value=0),
+        gr.Slider(minimum=-48, maximum=9, step=0.1, label='V3', value=0),
+        gr.Slider(minimum=-5, maximum=16, step=0.1, label='V4', value=0),
+        gr.Slider(minimum=-113, maximum=32, step=0.1, label='V5', value=0),
+        gr.Slider(minimum=-26, maximum=73, step=0.1, label='V6', value=0),
+        gr.Slider(minimum=-43, maximum=113, step=0.1, label='V7', value=0),
+        gr.Slider(minimum=-73, maximum=20, step=0.1, label='V8', value=0),
+        gr.Slider(minimum=-13, maximum=15, step=0.1, label='V9', value=0),
+        gr.Slider(minimum=-24, maximum=23, step=0.1, label='V10', value=0),
+        gr.Slider(minimum=-4, maximum=12, step=0.1, label='V11', value=0),
+        gr.Slider(minimum=-18, maximum=7, step=0.1, label='V12', value=0),
+        gr.Slider(minimum=-5, maximum=7, step=0.1, label='V13', value=0),
+        gr.Slider(minimum=-19, maximum=10, step=0.1, label='V14', value=0),
+        gr.Slider(minimum=-4, maximum=8, step=0.1, label='V15', value=0),
+        gr.Slider(minimum=-14, maximum=17, step=0.1, label='V16', value=0),
+        gr.Slider(minimum=-25, maximum=9, step=0.1, label='V17', value=0),
+        gr.Slider(minimum=-9, maximum=5, step=0.1, label='V18', value=0),
+        gr.Slider(minimum=-7, maximum=5, step=0.1, label='V19', value=0),
+        gr.Slider(minimum=-54, maximum=39, step=0.1, label='V20', value=0),
+        gr.Slider(minimum=-34, maximum=27, step=0.1, label='V21', value=0),
+        gr.Slider(minimum=-10, maximum=10, step=0.1, label='V22', value=0),
+        gr.Slider(minimum=-44, maximum=22, step=0.1, label='V23', value=0),
+        gr.Slider(minimum=-2, maximum=4, step=0.1, label='V24', value=0),
+        gr.Slider(minimum=-10, maximum=7, step=0.1, label='V25', value=0),
+        gr.Slider(minimum=-2, maximum=5, step=0.1, label='V26', value=0),
+        gr.Slider(minimum=-22, maximum=31, step=0.1, label='V27', value=0),
+        gr.Slider(minimum=-15, maximum=33, step=0.1, label='V28', value=0),
+        gr.Slider(minimum=0, maximum=25691, step=10, label='Amount', value=10),
+]
 
 # the app outputs two segmented images
-output = [
-          gr.Textbox(label=''),
-          gr.Dataframe(headers = ['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21',
-                                  'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount'],
-                       max_rows = 1, row_count = 1, max_cols = 30, col_count = 30,
-                       type='pandas', label='Display of generated data input for model')
-          ]
+output = [gr.Textbox(label='')]
 
 title = 'Imbalanced Classification with Tensorflow'
 description = 'Imbalanced Classification in predicting Credit card Fraud.'
 
-article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. Based on this <a href=\"https://keras.io/examples/structured_data/imbalanced_classification/\">keras example</a> by <a href=\"https://twitter.com/fchollet\">fchollet.</a> HuggingFace Model <a href=\"https://huggingface.co/keras-io/imbalanced_classification\">here</a> "
+article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. <br> Based on this <a href=\"https://keras.io/examples/structured_data/imbalanced_classification/\">keras example</a> by <a href=\"https://twitter.com/fchollet\">fchollet.</a> <br> HuggingFace Model <a href=\"https://huggingface.co/keras-io/imbalanced_classification\">here</a> "
 
-#examples = [123, 2022, 975]
+examples = [[1.39271000e+05, -1.01966286e-01, -2.19383079e-01,
+             2.91556060e-01, -1.48187543e+00,  4.92119816e-01,
+             7.19159629e-01,  1.09760697e+00, -3.29724791e-01,
+            -5.36999746e-01,  4.44389995e-01, -6.76070884e-01,
+            -9.84972751e-01, -1.17935517e+00,  2.52172631e-02,
+             8.96468489e-01, -2.84669137e+00,  4.31550427e-01,
+             1.93543856e-01, -2.23790737e+00, -6.42987862e-01,
+             6.46948603e-02,  1.18711606e+00, -1.11801609e-01,
+            -1.05455472e+00, -8.00450431e-01, -1.24265267e-01,
+            -1.39086191e-01, -2.60693707e-01,  1.06000000e+02],
+            [ 2.32520000e+04,  1.29377595e+00,  3.69202958e-01,
+             3.09448258e-01,  6.29771401e-01,  8.24778408e-02,
+            -4.35559896e-01,  4.37783478e-02, -2.96998723e-01,
+             1.22423979e+00, -4.73446723e-01,  1.64211418e-01,
+            -1.84665003e+00,  3.17030274e+00,  1.41499690e+00,
+             2.40625127e-01,  4.69934516e-01, -5.57937991e-02,
+            -1.23218906e-01, -1.83562143e-02, -1.01346309e-02,
+            -4.41576748e-01, -1.00570303e+00,  1.84647505e-02,
+            -4.91389484e-01,  3.36660905e-01,  1.02774527e-01,
+            -5.27911412e-02,  9.04270816e-03,  1.69900000e+01],
+            [ 1.42659000e+05,  1.83404703e+00, -1.94875520e+00,
+            -1.78165816e+00, -1.56784758e+00, -1.00460825e+00,
+            -8.37986768e-01, -3.40342869e-01, -3.37935548e-01,
+            -1.96463717e+00,  1.72661800e+00,  6.57974945e-01,
+            -3.98308615e-01,  1.14503226e-01,  3.12088209e-01,
+            -4.79675451e-01, -3.73087478e-01,  1.73461298e-01,
+             1.71939285e-01,  1.69937838e-01,  5.18207228e-02,
+            -1.29125608e-01, -5.58702004e-01,  3.59553819e-02,
+            -4.36588035e-01, -2.80344803e-01, -3.32314616e-01,
+            -7.22218399e-02, -2.88505958e-02,  2.62000000e+02,]]
 
-gr.Interface(infer, inputs, output, allow_flagging='never', #examples= examples, 
+gr.Interface(infer, inputs, output, examples= examples, allow_flagging='never',
              title=title, description=description, article=article, live=False).launch(enable_queue=True, debug=False)