Create app.py

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# Download the model from Hugging Face hub
+model_filename = hf_hub_download(repo_id="poudel/Job_Predictor", filename="random_forest_pipeline.pkl")
+
+# Load the model
+loaded_model = joblib.load(model_filename)
+
+# Download the dataset from Hugging Face hub
+data_filename = hf_hub_download(repo_id="poudel/Job_Predictor", filename="cleaned_erecruit_open_data.xlsx")
+
+# Load the dataset
+data = pd.read_excel(data_filename)
+
+# Get unique values for dropdowns
+position_titles = data['PositionTitle'].unique().tolist()
+designations = data['Designation'].unique().tolist()
+agencies = data['Agency'].unique().tolist()
+vacancy_types = data['VacancyType'].unique().tolist()
+employment_categories = data['EmploymentCategory'].unique().tolist()
+locations = data['Locations'].unique().tolist()
+vacancy_6_months_or_less = data['Vacancy6MonthsOrLess'].unique().tolist()
+
+# Define a function to make predictions based on user input
+def predict_applicants(position_title, designation, agency, vacancy_type, employment_category, location, vacancy_6_months_or_less, number_of_vacancies, number_of_successful_applicants):
+    # Create a DataFrame from the inputs
+    input_data = pd.DataFrame({
+        'PositionTitle': [position_title],
+        'Designation': [designation],
+        'Agency': [agency],
+        'VacancyType': [vacancy_type],
+        'EmploymentCategory': [employment_category],
+        'Locations': [location],
+        'Vacancy6MonthsOrLess': [vacancy_6_months_or_less],
+        'NumberOfSuccessfulApplicants': [number_of_successful_applicants],
+        'NumberOfVacancies': [number_of_vacancies]
+    })
+
+    # Calculate additional features
+    input_data['Success_Ratio'] = input_data['NumberOfSuccessfulApplicants'] / input_data['NumberOfVacancies'].replace(0, np.nan)
+    input_data['Applicants_per_Vacancy'] = input_data['NumberOfVacancies'] / np.where(input_data['NumberOfSuccessfulApplicants'] == 0, np.nan, input_data['NumberOfSuccessfulApplicants'])
+    input_data['Success_Ratio'].fillna(0, inplace=True)
+    input_data['Applicants_per_Vacancy'].fillna(0, inplace=True)
+
+    # Make predictions using the loaded model pipeline
+    try:
+        prediction = loaded_model.predict(input_data)
+        return f"Predicted Number of Applicants: {int(prediction[0])}"
+    except Exception as e:
+        return f"Error during prediction: {str(e)}"
+
+# Create the Gradio Blocks Interface
+with gr.Blocks() as interface:
+    # Add a title and description
+    gr.Markdown("# NT's Job Predictor")
+    gr.Markdown("Select the job details below to predict the number of applicants for a given position.")
+
+    with gr.Row():
+        position_title_input = gr.Dropdown(choices=position_titles, label="Position Title", value=None)
+        designation_input = gr.Dropdown(choices=designations, label="Designation", value=None)
+        agency_input = gr.Dropdown(choices=agencies, label="Agency", value=None)
+
+    with gr.Row():
+        vacancy_type_input = gr.Dropdown(choices=vacancy_types, label="Vacancy Type", value=None)
+        employment_category_input = gr.Dropdown(choices=employment_categories, label="Employment Category", value=None)
+        location_input = gr.Dropdown(choices=locations, label="Locations", value=None)
+        vacancy_6_months_or_less_input = gr.Dropdown(choices=vacancy_6_months_or_less, label="Vacancy 6 Months or Less", value=None)
+
+    with gr.Row():
+        number_of_vacancies_input = gr.Number(label="Past Number of Vacancies", value=None)
+        number_of_successful_applicants_input = gr.Number(label="Past Number of Successful Applicants", value=None)
+
+    predict_button = gr.Button("Predict")
+
+    predicted_applicants_output = gr.Textbox(label="Predicted Number of Applicants")
+
+    predict_button.click(
+        fn=predict_applicants,
+        inputs=[
+            position_title_input,
+            designation_input,
+            agency_input,
+            vacancy_type_input,
+            employment_category_input,
+            location_input,
+            vacancy_6_months_or_less_input,
+            number_of_vacancies_input,
+            number_of_successful_applicants_input
+        ],
+        outputs=predicted_applicants_output
+    )
+
+interface.launch()