app.py

# Install necessary libraries
#!pip install gradio pandas scikit-learn joblib

import pandas as pd
import joblib
import gradio as gr

# Load the model and scaler from the binary files
with open('model.bin', 'rb') as file:
    model = joblib.load(file)

with open('scaler.bin', 'rb') as file:
    scaler = joblib.load(file)


# Define ordinal mappings and columns
ordinal_mappings = {
    'Not Applicable': 0,
    'Strongly disagree': 1,
    'Disagree': 2,
    'Neutral': 3,
    'Agree': 4,
    'Strongly agree': 5
}

ordinal_columns = [
    'PoorAcademicPerformanceSelfPerception', 'AcademicCriticismSelfPerception',
    'UnsatisfiedAcademicWorkloadSelfPerception', 'NonInterestSubjectOpinion',
    'UnhappySubjectOpinion', 'NonInterestInstitutionOpinion',
    'UnhappyInstitutionOpinion', 'ParentalStrictness', 'ParentalAcademicPressure',
    'ParentalMarriagePressure', 'ParentalCareerPressure',
    'ParentalStudyAbroadPressure', 'ParentalUnderstanding', 'SiblingBonding',
    'ParentalRelationshipStability', 'PeerRelationship', 'TeacherSupport',
    'PartnerRelationshipImpact', 'PhysicalViolenceExperience',
    'SexualViolenceExperience', 'VerbalViolenceExperience',
    'EmotionalViolenceExperience'
]


# Define the prediction function
def predict_depression_level(age, gender, cgpa, poor_academic_performance,
                             academic_criticism, unsatisfied_workload,
                             non_interest_subject, unhappy_subject,
                             non_interest_institution, unhappy_institution,
                             parental_strictness, parental_academic_pressure,
                             parental_marriage_pressure, parental_career_pressure,
                             parental_study_abroad, parental_understanding,
                             sibling_bonding, parental_relationship_stability,
                             peer_relationship, teacher_support,
                             partner_relationship_impact, physical_violence,
                             sexual_violence, verbal_violence, emotional_violence,
                             little_interest, feeling_down, sleeping_issue,
                             feeling_tired, poor_appetite, feeling_bad,
                             trouble_concentrating, slowness, self_harm):

    # Convert gender to numeric
    gender = 1 if gender == 'Female' else 0

    # Define feature names matching those used during fitting
    feature_names = [
        'Age', 'Gender', 'CGPA', 'PoorAcademicPerformanceSelfPerception', 'AcademicCriticismSelfPerception',
        'UnsatisfiedAcademicWorkloadSelfPerception', 'NonInterestSubjectOpinion', 'UnhappySubjectOpinion',
        'NonInterestInstitutionOpinion', 'UnhappyInstitutionOpinion', 'ParentalStrictness', 'ParentalAcademicPressure',
        'ParentalMarriagePressure', 'ParentalCareerPressure', 'ParentalStudyAbroadPressure', 'ParentalUnderstanding',
        'SiblingBonding', 'ParentalRelationshipStability', 'PeerRelationship', 'TeacherSupport',
        'PartnerRelationshipImpact', 'PhysicalViolenceExperience', 'SexualViolenceExperience', 'VerbalViolenceExperience',
        'EmotionalViolenceExperience', 'little interest', 'feeling down', 'Sleeping issue', 'feeling tired',
        'poor appetite', 'feeling bad', 'trouble concertrating', 'slowness', 'self harm'
    ]

    # Map ordinal columns to numerical values using ordinal_mappings
    input_data = pd.DataFrame([[age, gender, cgpa, ordinal_mappings[poor_academic_performance],
                                ordinal_mappings[academic_criticism], ordinal_mappings[unsatisfied_workload],
                                ordinal_mappings[non_interest_subject], ordinal_mappings[unhappy_subject],
                                ordinal_mappings[non_interest_institution], ordinal_mappings[unhappy_institution],
                                ordinal_mappings[parental_strictness], ordinal_mappings[parental_academic_pressure],
                                ordinal_mappings[parental_marriage_pressure], ordinal_mappings[parental_career_pressure],
                                ordinal_mappings[parental_study_abroad], ordinal_mappings[parental_understanding],
                                ordinal_mappings[sibling_bonding], ordinal_mappings[parental_relationship_stability],
                                ordinal_mappings[peer_relationship], ordinal_mappings[teacher_support],
                                ordinal_mappings[partner_relationship_impact], ordinal_mappings[physical_violence],
                                ordinal_mappings[sexual_violence], ordinal_mappings[verbal_violence], ordinal_mappings[emotional_violence],
                                little_interest, feeling_down, sleeping_issue, feeling_tired,
                                poor_appetite, feeling_bad, trouble_concentrating, slowness, self_harm]],
                              columns=feature_names)

    input_data_scaled = scaler.transform(input_data)
    prediction = model.predict(input_data_scaled)[0]
    return "Your depression severity may be " + str(prediction)


# Define Gradio interface inputs
inputs = [
    gr.Slider(minimum=0, maximum=100, label="Age"),
    gr.Dropdown(choices=["Male", "Female"], label="Gender"),
    gr.Slider(minimum=0.0, maximum=4.0, label="CGPA")
]



# Updated labels for ordinal columns
ordinal_labels = {
    'PoorAcademicPerformanceSelfPerception': 'Your Academic Performance is poor.',
    'AcademicCriticismSelfPerception': 'You experience Academic Criticism.',
    'UnsatisfiedAcademicWorkloadSelfPerception': 'You are unsatisfied with your academic workload.',
    'NonInterestSubjectOpinion': 'The subject you are studying is of non-interest to you.',
    'UnhappySubjectOpinion': 'You are unhappy with the subject you are studying.',
    'NonInterestInstitutionOpinion': 'You study at an institution of your non-interest.',
    'UnhappyInstitutionOpinion': 'You are Unhappy with your institution.',

    'ParentalStrictness': 'Your parents are strict.',
    'ParentalAcademicPressure': 'You experience academic pressure from your parents.',
    'ParentalMarriagePressure': 'You experience pressure to get married from your parents.',
    'ParentalCareerPressure': 'You experience career pressure from your parents.',
    'ParentalStudyAbroadPressure': 'You experience pressure to study abroad from your parents.',
    'ParentalUnderstanding': 'Your have poor understanding with your parents.',
    'SiblingBonding': 'You have poor bonding with your siblings.',
    'ParentalRelationshipStability': 'Your parents have unstable relationship.',

    'PeerRelationship': 'You have poor relationship with your peers.',
    'TeacherSupport': 'Teachers do not support you.',
    'PartnerRelationshipImpact': 'You have poor relationship with your partner.',
    'PhysicalViolenceExperience': 'You have experience physical violence.',
    'SexualViolenceExperience': 'You have experience sexual violence.',
    'VerbalViolenceExperience': 'You have experience verbal violence.',
    'EmotionalViolenceExperience': 'You have experienced emotional violence.',
}


# Add radio buttons for ordinal columns with updated labels
for col in ordinal_columns:
    inputs.append(gr.Radio(choices=list(ordinal_mappings.keys()), label=ordinal_labels[col]))


# Add sliders for the remaining inputs
additional_inputs = [
    gr.Slider(minimum=0, maximum=5, step=1, label="How has your interest changed over work and activities? (0= No change)"),
    gr.Slider(minimum=0, maximum=5, step=1, label="How often do you feel down?"),
    gr.Slider(minimum=0, maximum=5, step=1, label="Do you struggle to sleep?"),
    gr.Slider(minimum=0, maximum=5, step=1, label="How often do you feel tired?"),
    gr.Slider(minimum=0, maximum=5, step=1, label="How has your appetite changed?"),
    gr.Slider(minimum=0, maximum=5, step=1, label="How often do you feel bad about yourself?"),
    gr.Slider(minimum=0, maximum=5, step=1, label="How has your concentration levels changed?"),
    gr.Slider(minimum=0, maximum=5, step=1, label="Do you feel slow?"),
    gr.Slider(minimum=0, maximum=5, step=1, label="Have you had suicidal thoughts?")
]

inputs.extend(additional_inputs)


output = gr.Textbox(label="Predicted Depression Level")


# Create Gradio interface
iface = gr.Interface(fn=predict_depression_level, inputs=inputs, outputs=output, title="Understand your Depression Levels",
                     description="A questionnaire to determine potential depression severity using the questions below - ")

iface.launch(debug=True, share=True)