app.py

#!pip install gradio pandas scikit-learn joblib


import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
import gradio as gr
import joblib

# Load your dataset (assuming 'AIDA-PHQ-Updated.csv' is in the same directory as your script)
df = pd.read_csv("AIDA-PHQ-Updated.csv")

df.head()

# Ordinal mappings (if not already applied)
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'
]

for col in ordinal_columns:
    df[col] = df[col].map(ordinal_mappings)

# Encode the Gender column
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
df['Gender'] = encoder.fit_transform(df['Gender'])

df.head()

# Split into X and y
X = df.drop('DepressionLevel', axis=1)
y = df['DepressionLevel']

X.columns

X.head()

# Split into train and test sets
random_state = 2024
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state)

# Standardize features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)



# Train Logistic Regression model
logreg = LogisticRegression(C=10.0, solver='newton-cg')
logreg.fit(X_train_scaled, y_train)


# Save the model as a .bin file
with open('model.bin', 'wb') as file:
    joblib.dump(logreg, file)

# Save the scaler as a .bin file
with open('scaler.bin', 'wb') as file:
    joblib.dump(scaler, file)

def predict_depression_level_logreg(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 = logreg.predict(input_data_scaled)[0]
    return str(prediction)


# Define Gradio interface with proper input labels and output type
inputs = [
    gr.Slider(minimum=int(df['Age'].min()), maximum=int(df['Age'].max()), label="Age"),
    gr.Dropdown(choices=["Male", "Female"], label="Gender"),
    gr.Slider(minimum=float(df['CGPA'].min()), maximum=float(df['CGPA'].max()), 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)

# # Sample input values (these should match the features and their order in your function)
# sample_input = {
#     "Age": 25,
#     "Gender": "Female",  # Assuming string value for Gender
#     "CGPA": 3.5,
#     "PoorAcademicPerformanceSelfPerception": "Disagree",  # String value, needs mapping
#     "AcademicCriticismSelfPerception": "Neutral",  # String value, needs mapping
#     "UnsatisfiedAcademicWorkloadSelfPerception": "Strongly disagree",  # String value, needs mapping
#     "NonInterestSubjectOpinion": "Disagree",  # String value, needs mapping
#     "UnhappySubjectOpinion": "Neutral",  # String value, needs mapping
#     "NonInterestInstitutionOpinion": "Strongly disagree",  # String value, needs mapping
#     "UnhappyInstitutionOpinion": "Disagree",  # String value, needs mapping
#     "ParentalStrictness": "Strongly agree",  # String value, needs mapping
#     "ParentalAcademicPressure": "Agree",  # String value, needs mapping
#     "ParentalMarriagePressure": "Neutral",  # String value, needs mapping
#     "ParentalCareerPressure": "Strongly disagree",  # String value, needs mapping
#     "ParentalStudyAbroadPressure": "Neutral",  # String value, needs mapping
#     "ParentalUnderstanding": "Strongly agree",  # String value, needs mapping
#     "SiblingBonding": "Strongly agree",  # String value, needs mapping
#     "ParentalRelationshipStability": "Neutral",  # String value, needs mapping
#     "PeerRelationship": "Agree",  # String value, needs mapping
#     "TeacherSupport": "Strongly agree",  # String value, needs mapping
#     "PartnerRelationshipImpact": "Neutral",  # String value, needs mapping
#     "PhysicalViolenceExperience": 0,
#     "SexualViolenceExperience": 1,
#     "VerbalViolenceExperience": 0,
#     "EmotionalViolenceExperience": 2,
#     "little interest": 1,
#     "feeling down": 3,
#     "Sleeping issue": 2,
#     "feeling tired": 4,
#     "poor appetite": 2,
#     "feeling bad": 3,
#     "trouble concertrating": 1,
#     "slowness": 2,
#     "self harm": 0
# }

# # Convert the sample input dictionary to a DataFrame
# sample_df = pd.DataFrame([sample_input])

# # Map ordinal columns to numerical values using ordinal_mappings
# for col in ordinal_columns:
#     sample_df[col] = sample_df[col].map(ordinal_mappings)

# # Convert gender to numeric
# sample_df['Gender'] = 1 if sample_df['Gender'].iloc[0] == 'Female' else 0

# # Scale the input data using the scaler from your model
# sample_df_scaled = scaler.transform(sample_df)

# # Predict using your function
# prediction = predict_depression_level_logreg(*sample_df_scaled[0])

# # Print or use the prediction as needed
# print("Predicted Depression Level:", prediction)


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

# Create Gradio interface
iface = gr.Interface(fn=predict_depression_level_logreg, 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)

# #importing libraries
# import numpy as np
# import lime
# # import lime.lime_tabular
# from lime import lime_tabular

# # Training Logistic Regression classifier
# model = LogisticRegression()
# model.fit(X_train, y_train)

# # Defining the lime explainer
# explainer = lime_tabular.LimeTabularExplainer(
#     training_data=np.array(X_train),
#     feature_names=X_train.columns,
#     class_names=['MINIMAL','MILD','Moderate','ModeratelySevere','Severe'],
#     mode='classification'
# )

# #importing libraries
# import numpy as np
# import lime
# # import lime.lime_tabular
# from lime import lime_tabular

# # Training Logistic Regression classifier
# model = LogisticRegression()
# model.fit(X_train, y_train)

# # Defining the lime explainer
# explainer = lime_tabular.LimeTabularExplainer(
#     training_data=np.array(X_train),
#     feature_names=X_train.columns,
#     class_names=['MINIMAL','MILD','Moderate','ModeratelySevere','Severe'],
#     mode='classification'
# )

# exp = explainer.explain_instance(
#     data_row=X_test.iloc[5],
#     predict_fn=model.predict_proba, num_features = 12
# )

# exp.show_in_notebook(show_table=True)