app.py

# 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()