kuwa hore

requirements.txt

@@ -1,3 +1,11 @@
-altair
-pandas
-streamlit
+fastapi
+uvicorn
+streamlit
+requests
+joblib
+numpy
+pandas
+matplotlib
+seaborn
+scikit-learn
+pydantic

salary_prediction_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0aa11e6460aa11df099849e890a088dcae64e6898bde2b5eb86e5cfc22d9bde
+size 1724369

streamlit_app.py

@@ -0,0 +1,29 @@
+import streamlit as st
+import requests
+
+st.title("Salary Prediction App")
+
+# Input fields for user
+Gender = st.selectbox("Gender", ["Male", "Female"])
+Education_Level = st.selectbox("Education Level", ["Bachelor", "Master", "PhD"])
+Job_Title = st.selectbox("Job Title", ["Data Scientist", "Software Engineer", "Product Manager", "Business Analyst", "HR Specialist"])
+Age = st.number_input("Age", min_value=18, max_value=70, value=25)
+Years_of_Experience = st.number_input("Years of Experience", min_value=0.0, max_value=50.0, value=1.0)
+
+# Encode Gender (assuming 0 for Female, 1 for Male)
+gender_encoded = 1 if Gender == "Male" else 0
+
+if st.button("Predict Salary"):
+    payload = {
+        "Gender": gender_encoded,
+        "Job_Title": Job_Title,
+        "Education_Level": Education_Level,
+        "Age": Age,
+        "Years_of_Experience": Years_of_Experience
+    }
+    response = requests.post("http://localhost:8000/predict", json=payload)
+    if response.status_code == 200:
+        result = response.json()
+        st.success(f"Predicted Salary: {result['predicted_salary']}")
+    else:
+        st.error("Error: Could not get prediction from API.")