Hugging Face's logo

Spaces:
MohamedYacoub
/
credit-score-classification
Build error

App Files Community
unknown commited on
Commit
db3b173
•
1 Parent(s): 2be9831

update requirments

Browse files
Files changed (2) hide show
  1. app.py +30 -24
  2. requirements.txt +0 -0
app.py CHANGED
@@ -1,11 +1,16 @@
 
 
 
1
  import joblib
 
 
2
 
3
  model = joblib.load("xgb.pkl")
4
 
5
 
6
  def predict(*args):
7
  input_data = []
8
-
9
  for i in args:
10
  input_data.append(float(i))
11
 
@@ -22,6 +27,7 @@ def predict(*args):
22
  else:
23
  return "The Credit Score is Standard"
24
 
 
25
  with gr.Blocks() as app:
26
  gr.Markdown(
27
  """
@@ -51,15 +57,15 @@ with gr.Blocks() as app:
51
  )
52
  Credit_Mix = gr.Dropdown(
53
  label="Credit Mix (Bad: 0, Don't Have: 1, Good: 2, Standard: 3)",
54
- choices=[0,1,2,3],
55
- value=lambda: random.choice([0,1,2,3]),
56
  )
57
  Outstanding_Debt = gr.TextArea(label="Outstanding Debt")
58
  Credit_Utilization_Ratio = gr.TextArea(label="Credit Utilization Ratio")
59
  Payment_of_Min_Amount = gr.Dropdown(
60
  label="Payment of Minimum Amount (NM: 0, No: 1, Yes: 2)",
61
- choices=[0,1,2],
62
- value=lambda: random.choice([0,1,2]),
63
  )
64
  Total_EMI_per_month = gr.TextArea(label="Total Equated Monthly Installment")
65
  Amount_invested_monthly = gr.TextArea(label="Amount Invested Monthly")
@@ -67,48 +73,48 @@ with gr.Blocks() as app:
67
  Credit_History_Age_In_Years = gr.TextArea(label="Credit History in Years")
68
  StudentLoan = gr.Dropdown(
69
  label="Student Loan (Don't Have: 0, Have: 1)",
70
- choices=[0,1],
71
- value=lambda: random.choice([0,1]),
72
  )
73
- MortgageLoan= gr.Dropdown(
74
  label="Mortage Loan (Don't Have: 0, Have: 1)",
75
- choices=[0,1],
76
- value=lambda: random.choice([0,1]),
77
  )
78
  PersonalLoan = gr.Dropdown(
79
  label="Personal Loan (Don't Have: 0, Have: 1)",
80
- choices=[0,1],
81
- value=lambda: random.choice([0,1]),
82
  )
83
  DebtConsolidationLoan = gr.Dropdown(
84
  label="Debt Consolidation Loan (Don't Have: 0, Have: 1)",
85
- choices=[0,1],
86
- value=lambda: random.choice([0,1]),
87
  )
88
  Credit_BuilderLoan = gr.Dropdown(
89
  label="Credit Builder Loan (Don't Have: 0, Have: 1)",
90
- choices=[0,1],
91
- value=lambda: random.choice([0,1]),
92
  )
93
  HomeEquityLoan = gr.Dropdown(
94
  label="Home Equity Loan (Don't Have: 0, Have: 1)",
95
- choices=[0,1],
96
- value=lambda: random.choice([0,1]),
97
  )
98
  NotSpecified = gr.Dropdown(
99
  label="Unspecified Loan (Don't Have: 0, Have: 1)",
100
- choices=[0,1],
101
- value=lambda: random.choice([0,1]),
102
  )
103
  AutoLoan = gr.Dropdown(
104
  label="Auto Loan (Don't Have: 0, Have: 1)",
105
- choices=[0,1],
106
- value=lambda: random.choice([0,1]),
107
  )
108
  PaydayLoan = gr.Dropdown(
109
  label="Payday Loan (Don't Have: 0, Have: 1)",
110
- choices=[0,1],
111
- value=lambda: random.choice([0,1]),
112
  )
113
  with gr.Column():
114
  label = gr.Label()
 
1
+ import random
2
+
3
+ import gradio as gr
4
  import joblib
5
+ import numpy as np
6
+ import xgboost
7
 
8
  model = joblib.load("xgb.pkl")
9
 
10
 
11
  def predict(*args):
12
  input_data = []
13
+
14
  for i in args:
15
  input_data.append(float(i))
16
 
 
27
  else:
28
  return "The Credit Score is Standard"
29
 
30
+
31
  with gr.Blocks() as app:
32
  gr.Markdown(
33
  """
 
57
  )
58
  Credit_Mix = gr.Dropdown(
59
  label="Credit Mix (Bad: 0, Don't Have: 1, Good: 2, Standard: 3)",
60
+ choices=[0, 1, 2, 3],
61
+ value=lambda: random.choice([0, 1, 2, 3]),
62
  )
63
  Outstanding_Debt = gr.TextArea(label="Outstanding Debt")
64
  Credit_Utilization_Ratio = gr.TextArea(label="Credit Utilization Ratio")
65
  Payment_of_Min_Amount = gr.Dropdown(
66
  label="Payment of Minimum Amount (NM: 0, No: 1, Yes: 2)",
67
+ choices=[0, 1, 2],
68
+ value=lambda: random.choice([0, 1, 2]),
69
  )
70
  Total_EMI_per_month = gr.TextArea(label="Total Equated Monthly Installment")
71
  Amount_invested_monthly = gr.TextArea(label="Amount Invested Monthly")
 
73
  Credit_History_Age_In_Years = gr.TextArea(label="Credit History in Years")
74
  StudentLoan = gr.Dropdown(
75
  label="Student Loan (Don't Have: 0, Have: 1)",
76
+ choices=[0, 1],
77
+ value=lambda: random.choice([0, 1]),
78
  )
79
+ MortgageLoan = gr.Dropdown(
80
  label="Mortage Loan (Don't Have: 0, Have: 1)",
81
+ choices=[0, 1],
82
+ value=lambda: random.choice([0, 1]),
83
  )
84
  PersonalLoan = gr.Dropdown(
85
  label="Personal Loan (Don't Have: 0, Have: 1)",
86
+ choices=[0, 1],
87
+ value=lambda: random.choice([0, 1]),
88
  )
89
  DebtConsolidationLoan = gr.Dropdown(
90
  label="Debt Consolidation Loan (Don't Have: 0, Have: 1)",
91
+ choices=[0, 1],
92
+ value=lambda: random.choice([0, 1]),
93
  )
94
  Credit_BuilderLoan = gr.Dropdown(
95
  label="Credit Builder Loan (Don't Have: 0, Have: 1)",
96
+ choices=[0, 1],
97
+ value=lambda: random.choice([0, 1]),
98
  )
99
  HomeEquityLoan = gr.Dropdown(
100
  label="Home Equity Loan (Don't Have: 0, Have: 1)",
101
+ choices=[0, 1],
102
+ value=lambda: random.choice([0, 1]),
103
  )
104
  NotSpecified = gr.Dropdown(
105
  label="Unspecified Loan (Don't Have: 0, Have: 1)",
106
+ choices=[0, 1],
107
+ value=lambda: random.choice([0, 1]),
108
  )
109
  AutoLoan = gr.Dropdown(
110
  label="Auto Loan (Don't Have: 0, Have: 1)",
111
+ choices=[0, 1],
112
+ value=lambda: random.choice([0, 1]),
113
  )
114
  PaydayLoan = gr.Dropdown(
115
  label="Payday Loan (Don't Have: 0, Have: 1)",
116
+ choices=[0, 1],
117
+ value=lambda: random.choice([0, 1]),
118
  )
119
  with gr.Column():
120
  label = gr.Label()
requirements.txt CHANGED
Binary files a/requirements.txt and b/requirements.txt differ