Create app.py

app.py

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+import warnings
+warnings.filterwarnings('ignore')
+import pandas as pd
+import numpy as np
+from sklearn import preprocessing
+from sklearn.ensemble import GradientBoostingClassifier
+import pickle
+import gradio as gr
+
+# Load the model and encoder
+with open("model.pkl", "rb") as model_file:
+    gbc = pickle.load(model_file)
+
+with open("encoder.pkl", "rb") as encoder_file:
+    encoder_dict = pickle.load(encoder_file)
+
+def predict_employability(Age, Accessibility, EdLevel, Employment, Gender, MentalHealth, MainBranch, YearsCode, PreviousSalary, ComputerSkills, Continent):
+    data = {
+        'Age': Age, 'Accessibility': Accessibility, 'EdLevel': EdLevel, 'Employment': Employment, 
+        'Gender': Gender, 'MentalHealth': MentalHealth, 'MainBranch': MainBranch, 
+        'YearsCode': YearsCode, 'PreviousSalary': PreviousSalary, 'ComputerSkills': ComputerSkills, 
+        'Continent': Continent
+    }
+    df = pd.DataFrame([list(data.values())], columns=[
+        'Age', 'Accessibility', 'EdLevel', 'Employment', 'Gender', 'MentalHealth', 
+        'MainBranch', 'YearsCode', 'PreviousSalary', 'ComputerSkills', 'Continent'
+    ])
+    category_col = ['Age', 'Accessibility', 'EdLevel', 'Gender', 'MentalHealth', 'MainBranch', 'Continent']
+    for cat in encoder_dict:
+        for col in df.columns:
+            le = preprocessing.LabelEncoder()
+            if cat == col:
+                le.classes_ = np.array(encoder_dict[cat], dtype=object)
+                for unique_item in df[col].unique():
+                    if unique_item not in le.classes_:
+                        df[col] = ['Unknown' if x == unique_item else x for x in df[col]]
+                df[col] = le.transform(df[col].astype(str))
+    features_list = df.values.tolist()
+    prediction = gbc.predict(features_list)
+    return "Employable" if prediction[0] == 1 else "Less Employable"
+
+iface = gr.Interface(
+    fn=predict_employability,
+    inputs=[
+        gr.Dropdown(['<35', '>35'], label="Age"),
+        gr.Dropdown(['No', 'Yes'], label="Accessibility"),
+        gr.Dropdown(['Master', 'NoHigherEd', 'Other', 'PhD', 'Undergraduate'], label="EdLevel"),
+        gr.Slider(0, 1, step=1, label="Employment"),
+        gr.Dropdown(['Man', 'NonBinary', 'Woman'], label="Gender"),
+        gr.Dropdown(['No', 'Yes'], label="MentalHealth"),
+        gr.Dropdown(['Dev', 'NotDev'], label="MainBranch"),
+        gr.Slider(0, 50, step=1, label="YearsCode"),
+        gr.Number(label="PreviousSalary"),
+        gr.Slider(0, 100, step=1, label="ComputerSkills"),
+        gr.Dropdown(['Africa', 'Asia', 'Europe', 'North_America', 'Oceania', 'Others', 'South_America'], label="Continent")
+    ],
+    outputs="text",
+    title="Employability Prediction",
+    description="Predict employability based on various features."
+)
+
+iface.launch()