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app.py

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+import gradio as gr
+from gears import load_tensor, calc_preds
+
+coeffs = load_tensor()
+
+examples = [
+    """
+    -0.260648; -0.469648; 2.496266; -0.083724; 0.129681; 0.732898; 0.519014; -0.130006; 0.727159;
+    0.637735; -0.987020; 0.293438; -0.941386; 0.549020; 1.804879; 0.215598; 0.512307; 0.333644;
+    0.124270; 0.091202; -0.110552; 0.217606; -0.134794; 0.165959; 0.126280; -0.434824; -0.081230; -0.151045;
+    17982.1;
+    """,
+
+    """
+    0.985100; -0.356045; 0.558056; -0.429654; 0.277140; 0.428605; 0.406466; -0.133118; 0.347452; 0.529808;
+    0.140107; 1.564246; 0.574074; 0.627719; 0.706121; 0.789188; 0.403810; 0.201799; -0.340687; -0.233984;
+    -0.194936; -0.605761; 0.079469; -0.577395; 0.190090; 0.296503; -0.248052; -0.064512;
+    6531.370000;
+    """,
+
+    """
+    -0.478427; 0.142165; -0.046838; 0.683350; 0.067820; -0.404898; -0.206496; 0.184366; -0.762935; -0.228392; 0.660903;
+    -0.387520; -0.533249; -0.502266; 0.405143; -0.060691; -0.207237; 0.305603; 0.134876; -0.033921; 0.098977; -0.075191;
+    -0.481489; 0.678900; -0.011520; 0.409021; 0.075859; -0.447139;
+    1534.530000;
+    """,
+
+    """
+    -0.617111; -1.733888; 1.150655; 0.207829; 0.903533; -0.171524; 0.551679; -0.167744; 0.338861; 0.291418; -0.884790;
+    0.344854; 0.763902; 0.068704; 2.560478; 0.955467; 0.663089; 1.897704; 0.024869; 1.841243; 0.153856; 0.369734; 1.471004;
+    -0.497633; 0.377656; -0.328051; -0.512415; -0.013653;
+    10554.680000;
+    """
+]
+
+ans_examples = [0,0,1,1]
+
+# 0, 1, 541, 623
+
+def predict(indeps="0;0"):
+    indeps = list(map(float,indeps.split(';')))
+
+    fraud = calc_preds(coeffs, indeps)
+
+    if fraud >= 0.5:
+        return "Fraud"
+
+    return "Not fraud"
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.components.TextBox,
+    outputs=gr.components.Textbox,
+    examples=examples
+).launch(share=False)
+
+

gears.py

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+import torch
+import torch.nn.functional as F
+
+import numpy as np
+
+
+def load_tensor():
+    coeffs = [None,None]
+    coeffs[0] = torch.load('tensor1.pt')
+    coeffs[1] = torch.load('tensor2.pt')
+    return coeffs
+
+
+
+def calc_preds(coeffs, indeps):
+    layers,consts = coeffs
+    n = len(layers)
+    res = indeps
+    for i,l in enumerate(layers):
+        res = res@l + consts[i]
+        if i!=n-1: res = F.relu(res)
+    return torch.sigmoid(res)

tensor1.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:359cef2b080012664b03ab0e9931e2cc83993c57f167a66e8b319bc7bf860b71
+size 2787

tensor2.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7cfa74c8d172743343ae607314a41a6c629e358604bc61995b6a7f9cfe62bafa
+size 2787