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# importing libraries
!pip install huggingface_hub
!pip install transformers
!pip install transformers[torch]
!pip install datasets
!pip install skops
!pip install streamlit
from datasets import load_dataset, load_dataset_builder
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sklearn
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score, precision_score, recall_score, classification_report
from transformers import Trainer, TrainingArguments
from skops import hub_utils
import pickle
from skops.card import Card, metadata_from_config
from pathlib import Path
from tempfile import mkdtemp, mkstemp
import streamlit as st
from PIL import Image
# Loading the dataset
dataset_name = "saifhmb/social-network-ads"
dataset = load_dataset(dataset_name, split = 'train')
dataset = pd.DataFrame(dataset)
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
# Spliting the datset into Training and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
# Feature Scaling
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Training Logit Reg Model using the Training set
model = LogisticRegression()
model.fit(X_train, y_train)
# Predicting the Test result
y_pred = model.predict(X_test)
# Making the Confusion Matrix and evaluating performance
cm = confusion_matrix(y_pred, y_test, labels=model.classes_)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)
disp.plot()
plt.show()
acc = accuracy_score(y_test, y_pred)
ps = precision_score(y_test, y_pred)
rs = recall_score(y_test, y_pred)
# Pickling the model
pickle_out = open("model.pkl", "wb")
pickle.dump(model, pickle_out)
pickle_out.close()
# Loading the model to predict on the data
pickle_in = open('model.pkl', 'rb')
model = pickle.load(pickle_in)
def welcome():
return 'welcome all'
# defining the function which will make the prediction using the data which the user inputs
def prediction(Age, EstimatedSalary):
prediction = model.predict.sc.transform([[Age, EstimatedSalary]])
print(prediction)
return prediction
# this is the main function in which we define our webpage
def main():
# giving the webpage a title
st.title("Customer Vehicle Purchase Prediction")
Age = st.text_input("Age", "Type Here")
EstimatedSalary = st.text_input("EstimatedSalary", "Type Here")
result = ""
if st.button("Predict"):
result = prediction(Age, EstimatedSalary)
st.success('The output is {}'.format(result))
if __name__=='__main__':
main()
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