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app.py

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+import gradio as gr
+import pickle as pkl
+import pandas as pd
+
+# Load saved model & preprocessor
+model = pkl.load(open("rid.pkl", "rb"))
+preprocessor = pkl.load(open("prep.pkl", "rb"))
+
+# Define prediction function
+def predict_salary(name, age, gender, department, job_title, experience_years, education_level, location):
+    # Build input dataframe (same schema as training)
+    input_df = pd.DataFrame([{
+        "Name": name,
+        "Age": int(age),
+        "Gender": gender,
+        "Department": department,
+        "Job_Title": job_title,
+        "Experience_Years": int(experience_years),
+        "Education_Level": education_level,
+        "Location": location
+    }])
+
+    # Apply preprocessing
+    processed = preprocessor.transform(input_df)
+
+    # Predict salary
+    prediction = model.predict(processed)[0]
+    return f"💰 Predicted Salary: {round(prediction, 2)}"
+
+# Define Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🏢 Employer Salary Prediction App")
+    gr.Markdown("Fill in the employee details and get the predicted salary.")
+
+    with gr.Row():
+        with gr.Column():
+            name = gr.Textbox(label="Name", placeholder="Enter employee name")
+            age = gr.Number(label="Age", value=25)
+            gender = gr.Dropdown(choices=["Male", "Female", "Other"], label="Gender")
+            department = gr.Textbox(label="Department", placeholder="e.g. HR, IT, Finance")
+            job_title = gr.Textbox(label="Job Title", placeholder="e.g. Data Scientist")
+            experience = gr.Number(label="Experience (Years)", value=1)
+            education = gr.Dropdown(
+                choices=["High School", "Bachelors", "Masters", "PhD"], 
+                label="Education Level"
+            )
+            location = gr.Textbox(label="Location", placeholder="Enter city")
+            
+            submit_btn = gr.Button("🔮 Predict Salary")
+
+        with gr.Column():
+            output = gr.Textbox(label="Prediction")
+
+    submit_btn.click(
+        fn=predict_salary, 
+        inputs=[name, age, gender, department, job_title, experience, education, location], 
+        outputs=output
+    )
+
+# Run the app
+if __name__ == "__main__":
+    demo.launch()

prep.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9cd2eb5e08d6445a800c16f0a2c5cde852725006827d2304634a0378903bb29c
+size 4004

requirements.txt

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+pandas
+numpy
+scikit-learn
+gradio
+xgboost
+imbalanced-learn

rid.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a98e94be0d7503daff615467e901ed3d81868998e1711847279dc578f3beeabb
+size 4415