app.py

import gradio as gr

#Other Imports
import os
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler

def train_model(data, target):
# dependent and independent variables
    X = data.drop(columns=target)
    y = data[target]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

   #standardize the data
    sc = StandardScaler()
    X_train = sc.fit_transform(X_train)
    X_test = sc.transform(X_test)

    #train the model
    model = LogisticRegression(random_state=0, solver='lbfgs', multi_class='auto')
    model.fit(X_train, y_train)

    #print the accuracy score
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)

    return accuracy

# Upload csv file and train the model
def upload_csv(Input_CSV, Target_Variable):
    columns = list(pd.read_csv('./' + Input_CSV).columns)

    if Target_Variable not in columns:
        Target_Variable = columns[-1]

    data = pd.read_csv('./' + Input_CSV)

    accuracy = train_model(data, Target_Variable)

    return (data.head(4)), Target_Variable, accuracy

#list the csv files in current working directory
files = [f for f in os.listdir('.') if os.path.isfile(f) and f.endswith('csv')]

#set the inputs and corresponding outputs
inputs = [gr.Dropdown(files, chioces=True), gr.Textbox()]
outputs = ['dataframe', gr.Textbox(label="Target Variable"), gr.Textbox(label="Accuracy Score")]

#launch the dashboard
demo = gr.Interface(upload_csv, inputs, outputs)
demo.launch()

#in some cases this line might produce an error
# in case the above block of code throws error
# an argument needs to be passed in launch()
# demo.launch(share=True)
# the above line when run, solves the error