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# 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="SAD: Self Assessment of Depression",
description="A questionnaire to determine potential depression severity.")
iface.launch(debug=True, share=True) |