Update app.py

app.py

@@ -1,43 +1,75 @@
-import requests
-import joblib
-import os
+import streamlit as st
+import numpy as np
 import pandas as pd
-import gradio as gr
+import joblib
 import pickle
 
-countries = [
-    "United States of America", "Other", "Germany",
-    "United Kingdom of Great Britain and Northern Ireland",
-    "India", "Canada", "France", "Brazil", "Spain",
-    "Netherlands", "Australia", "Italy", "Poland",
-    "Sweden", "Russian Federation", "Switzerland", "Turkey",
-    "Israel", "Austria", "Norway", "Portugal", "Denmark",
-    "Belgium", "Finland", "Mexico", "New Zealand", "Greece",
-    "South Africa", "Pakistan", "Czech Republic",
-    "Iran, Islamic Republic of"
-]
-
-
-
-# Load the trained model
-model = pickle.load(open("salary_model.pkl", 'rb'))
-
-# Define prediction function
-def predict_salary(country, education, experience, remote):
-    input_data = pd.DataFrame([[country, education, experience, remote]], 
-                              columns=['Country', 'EdLevel', 'YearsCodePro', 'RemoteWork'])
-    prediction = model.predict(input_data)[0]
-    return f"Estimated Salary: ${round(prediction, 2)}"
-
-# Define Gradio app
-inputs = [
-    gr.Dropdown(countries, label="Country"),
-    gr.Dropdown(["Bachelor’s degree", "Master’s degree", "Post grad", "Less than a Bachelors"], label="Education Level"),
-    gr.Number(label="Years of Professional Experience"),
-    gr.Dropdown(["Fully remote", "Hybrid", "In-office"], label="Remote Work Status")
-]
-
-output = gr.Textbox(label="Predicted Salary")
-
-app = gr.Interface(fn=predict_salary, inputs=inputs, outputs=output, title="Salary Prediction App")
-app.launch()
+model = pickle.load(open("PycaretGBR.pkl", 'rb'))
+# model = joblib.load("lr.joblib")
+st.title('Developer Salary Prediction 2024')
+st.write("""### We need some information to predict the salary""")
+
+countries = (
+"Australia",
+"Austria",
+"Belgium",
+"Brazil",
+"Canada",
+"Czech Republic",
+"Denmark",
+"France",
+"Germany",
+"India",
+"Israel",
+"Italy",
+"Netherlands",
+"Norway",
+"Poland",
+"Russian Federation",
+"Spain",
+"Sweden",
+"Switzerland",
+"Ukraine"
+"United Kingdom of Great Britain and Northern Ireland",
+"United States of America"
+)
+
+education = (
+    "Less than a Bachelors",
+    "Bachelor’s degree",
+    "Master’s degree",
+    "Post grad"
+)
+
+employment = (
+    "Employed, full-time",
+    "Independent contractor, freelancer, or self-employed",
+    "Student, part-time",
+    "Retired", 
+    "Not employed, but looking for work",
+    "Employed, part-time",
+    "Student, full-time"
+)
+
+country = st.selectbox("Country", countries)
+education = st.selectbox("Education Level", education)
+expericence = st.slider("Years of Experience", 0, 50, 3)
+employment = st.selectbox("Employment Type", employment)
+
+columns = ['Country', 'EdLevel', 'YearsCodePro', 'Employment']
+
+ok = st.button("Calculate Salary")
+if ok:
+    X_new_df = pd.DataFrame([[country,education,expericence,employment]],
+                            columns = ['Country', 'EdLevel', 'YearsCodePro', 'Employment'])
+    print("##########")
+    print("##########")
+    print("##########")
+    print(model)
+    print("##########")
+    print("##########")
+    print("##########")
+    salary = model.predict(X_new_df)
+    
+    st.subheader(f"The estimated salary is {salary[0]:.2f} $")\
+