# 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)