Spaces:
Sleeping
Sleeping
Create app.py
Browse files
app.py
ADDED
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import gradio as gr
|
2 |
+
import joblib
|
3 |
+
|
4 |
+
def compute_trust_score(age, tcpa, adcopy, distance, lead_rate):
|
5 |
+
|
6 |
+
dict_weight = {
|
7 |
+
'age' : 0.001354,
|
8 |
+
'tcpa' : 0.372240,
|
9 |
+
'adcopy' : 0.296214,
|
10 |
+
'distance' : 0.320232,
|
11 |
+
'rate' : 0.009961
|
12 |
+
}
|
13 |
+
|
14 |
+
# Example computation for trust score (you can replace this with your actual computation)
|
15 |
+
data_age = age * dict_weight['age']
|
16 |
+
data_tcpa = tcpa * dict_weight['tcpa']
|
17 |
+
data_adcopy = adcopy * dict_weight['adcopy']
|
18 |
+
data_distance = distance * dict_weight['distance']
|
19 |
+
data_rate = lead_rate * dict_weight['rate']
|
20 |
+
|
21 |
+
lgbm = joblib.load('lgbm_model.joblib')
|
22 |
+
|
23 |
+
trust_score = lgbm.predict([[data_age, data_tcpa, data_adcopy, data_distance, data_rate]])
|
24 |
+
|
25 |
+
return trust_score
|
26 |
+
|
27 |
+
inputs = [
|
28 |
+
gr.Number(label="Age"),
|
29 |
+
gr.Number(label="TCPA Percentage"),
|
30 |
+
gr.Number(label="Adcopy Percentage"),
|
31 |
+
gr.Number(label="Mean Lead Distance"),
|
32 |
+
gr.Number(label="Daily Average")
|
33 |
+
]
|
34 |
+
|
35 |
+
output = gr.Textbox(label="Trust Score")
|
36 |
+
|
37 |
+
iface = gr.Interface(fn=compute_trust_score, inputs=inputs, outputs=output, title="Trust Score Calculator")
|
38 |
+
|
39 |
+
if __name__ == "__main__":
|
40 |
+
iface.launch()
|