Update app.py

app.py

@@ -1,23 +1,21 @@
+# Install necessary libraries
 #!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
+import gradio as gr
 
-# Load your dataset (assuming 'AIDA-PHQ-Updated.csv' is in the same directory as your script)
-df = pd.read_csv("AIDA-PHQ-Updated.csv")
+# Load the model and scaler from the binary files
+with open('model.bin', 'rb') as file:
+    model = joblib.load(file)
 
-df.head()
+with open('scaler.bin', 'rb') as file:
+    scaler = joblib.load(file)
 
-# Ordinal mappings (if not already applied)
+
+# Define ordinal mappings and columns
 ordinal_mappings = {
-    'Not Applicable' : 0,
+    'Not Applicable': 0,
     'Strongly disagree': 1,
     'Disagree': 2,
     'Neutral': 3,
@@ -38,62 +36,22 @@ ordinal_columns = [
     '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):
+# 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
@@ -127,18 +85,19 @@ def predict_depression_level_logreg(age, gender, cgpa, poor_academic_performance
                               columns=feature_names)
 
     input_data_scaled = scaler.transform(input_data)
-    prediction = logreg.predict(input_data_scaled)[0]
-    return str(prediction)
+    prediction = model.predict(input_data_scaled)[0]
+    return "Your depression severity may be " + str(prediction)
 
 
-# Define Gradio interface with proper input labels and output type
+# Define Gradio interface inputs
 inputs = [
-    gr.Slider(minimum=int(df['Age'].min()), maximum=int(df['Age'].max()), label="Age"),
+    gr.Slider(minimum=0, maximum=100, label="Age"),
     gr.Dropdown(choices=["Male", "Female"], label="Gender"),
-    gr.Slider(minimum=float(df['CGPA'].min()), maximum=float(df['CGPA'].max()), label="CGPA")
+    gr.Slider(minimum=0.0, maximum=4.0, label="CGPA")
 ]
 
 
+
 # Updated labels for ordinal columns
 ordinal_labels = {
     'PoorAcademicPerformanceSelfPerception': 'Your Academic Performance is poor.',
@@ -167,6 +126,7 @@ ordinal_labels = {
     '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]))
@@ -187,112 +147,12 @@ additional_inputs = [
 
 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",
+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)
-
-# #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)
-
+iface.launch(debug=True, share=True)