Create app.py

app.py

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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()