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import gradio as gr
import torch
from torch import tensor
from torch.nn import functional as F
from sklearn.preprocessing import LabelEncoder
import pandas as pd
label_encoder = LabelEncoder()
coeffs = torch.load('fakejobposts.pth')
indep_cols = ['job_title', 'company_name', 'company_desc', 'job_desc',
'job_requirement', 'salary', 'location', 'employment_type',
'department']
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)
if torch.sigmoid(res) > 0.5:
return 'Real Job Post'
else:
return 'Fake Job Post'
def main(job_title, company_name, company_desc, job_desc,
job_requirement, salary, location, employment_type,
department):
df = pd.DataFrame(columns=indep_cols)
df.loc[0] = [job_title, company_name, company_desc, job_desc,
job_requirement, salary, location, employment_type,
department]
for column in df.columns:
df[column] = label_encoder.fit_transform(df[column])
t_indep = tensor(df[indep_cols].values, dtype=torch.float)
vals,indices = t_indep.max(dim=0)
t_indep = t_indep / vals
return calc_preds(coeffs, t_indep)
iface = gr.Interface(
fn=main,
inputs=[gr.Textbox(label="Job title"), gr.Textbox(label="Company name"),
gr.Textbox(label="Company description"), gr.Textbox(label="Job description"),
gr.Textbox(label="Job Requirements"), gr.Textbox(label="Salary"),
gr.Textbox(label="Location"), gr.Textbox(label="Employment Type"),
gr.Textbox(label="Department")],
outputs="text",
title="Job posting identifier",
description="Identifies job posts as real or fake"
)
iface.launch(share=True) |