Update app.py

app.py

@@ -1,75 +1,60 @@
-import streamlit as st
-import numpy as np
-import pandas as pd
+import requests
 import joblib
-import pickle
-
-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} $")\
-
+import os
+import pandas as pd
+import gradio as gr
+
+HF_TOKEN = os.getenv("HF_TOKEN")  
+
+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"
+]
+
+
+# Hugging Face model details
+#model_url = "https://huggingface.co/hassanshe7/salary_model/resolve/main/salary_model.pkl?download=true"
+model_url = "https://huggingface.co/spaces/hassanshe7/Stackoverflow_salary_prediction/resolve/main/salary_model.pkl?download=true"
+model_path = "salary_model.pkl"
+
+# Download model with authentication
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+response = requests.get(model_url, headers=headers)
+
+if response.status_code == 200:
+    with open(model_path, "wb") as f:
+        f.write(response.content)
+    print("Model downloaded successfully.")
+else:
+    print(f"Failed to download model: {response.text}")
+    exit(1)
+
+# Load the trained model
+model = joblib.load(model_path)
+
+# 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()