Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""gradio_test.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1nr6ieHBAcOKjo04y5MYF-ndbKN7nK2mr
+"""
+
+#!pip install gradio
+
+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(share=True)
+
+#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