Update app.py

app.py

@@ -1,141 +1,141 @@
-import gradio as gr
-import pandas as pd
-import joblib
-
-# Load the trained models
-xgb_model_depression = joblib.load('Models/xgb_model_depression.pkl')
-xgb_model_anxiety = joblib.load('Models/xgb_model_anxiety.pkl')
-
-# Depression and Anxiety Severity Class Mappings
-depression_classes = {
-    0: "No Depression",
-    1: "Mild Depression",
-    2: "Moderate Depression",
-    3: "Severe Depression",
-    4: "Very Severe Depression",
-    5: "Extreme Depression"
-}
-
-anxiety_classes = {
-    0: "No Anxiety",
-    1: "Mild Anxiety",
-    2: "Moderate Anxiety",
-    3: "Severe Anxiety",
-    4: "Extreme Anxiety"
-}
-
-# Prediction functions
-def predict_depression_severity(age, gender, bmi, who_bmi, phq_score, depressiveness, suicidal, depression_diagnosis, depression_treatment, gad_score, anxiousness, anxiety_diagnosis, anxiety_treatment, epworth_score, sleepiness):
-    # Convert categorical values to numeric
-    gender = 0 if gender == "Male" else 1
-    suicidal = int(suicidal)  # Convert to int (0 or 1)
-    depression_diagnosis = int(depression_diagnosis)  # Convert to int (0 or 1)
-    depression_treatment = int(depression_treatment)  # Convert to int (0 or 1)
-    anxiety_diagnosis = int(anxiety_diagnosis)  # Convert to int (0 or 1)
-    anxiety_treatment = int(anxiety_treatment)  # Convert to int (0 or 1)
-    sleepiness = int(sleepiness)  # Convert to int (0 or 1)
-
-    input_data = {
-        'age': age,
-        'gender': gender,
-        'bmi': bmi,
-        'who_bmi': who_bmi,
-        'phq_score': phq_score,
-        'depressiveness': depressiveness,
-        'suicidal': suicidal,
-        'depression_diagnosis': depression_diagnosis,
-        'depression_treatment': depression_treatment,
-        'gad_score': gad_score,
-        'anxiousness': anxiousness,
-        'anxiety_diagnosis': anxiety_diagnosis,
-        'anxiety_treatment': anxiety_treatment,
-        'epworth_score': epworth_score,
-        'sleepiness': sleepiness
-    }
-    
-    # Ensure the input is a DataFrame
-    input_df = pd.DataFrame([input_data])
-    prediction = xgb_model_depression.predict(input_df)
-    
-    # Map prediction to readable class
-    return depression_classes[prediction[0]]
-
-def predict_anxiety_severity(age, gender, bmi, who_bmi, phq_score, depressiveness, suicidal, depression_diagnosis, depression_treatment, gad_score, anxiousness, anxiety_diagnosis, anxiety_treatment, epworth_score, sleepiness):
-    # Convert categorical values to numeric
-    gender = 0 if gender == "Male" else 1
-    suicidal = int(suicidal)  # Convert to int (0 or 1)
-    depression_diagnosis = int(depression_diagnosis)  # Convert to int (0 or 1)
-    depression_treatment = int(depression_treatment)  # Convert to int (0 or 1)
-    anxiety_diagnosis = int(anxiety_diagnosis)  # Convert to int (0 or 1)
-    anxiety_treatment = int(anxiety_treatment)  # Convert to int (0 or 1)
-    sleepiness = int(sleepiness)  # Convert to int (0 or 1)
-
-    input_data = {
-        'age': age,
-        'gender': gender,
-        'bmi': bmi,
-        'who_bmi': who_bmi,
-        'phq_score': phq_score,
-        'depressiveness': depressiveness,
-        'suicidal': suicidal,
-        'depression_diagnosis': depression_diagnosis,
-        'depression_treatment': depression_treatment,
-        'gad_score': gad_score,
-        'anxiousness': anxiousness,
-        'anxiety_diagnosis': anxiety_diagnosis,
-        'anxiety_treatment': anxiety_treatment,
-        'epworth_score': epworth_score,
-        'sleepiness': sleepiness
-    }
-    
-    # Ensure the input is a DataFrame
-    input_df = pd.DataFrame([input_data])
-    prediction = xgb_model_anxiety.predict(input_df)
-    
-    # Map prediction to readable class
-    return anxiety_classes[prediction[0]]
-
-# Wrapper function to call both predictions
-def predict_both(age, gender, bmi, who_bmi, phq_score, depressiveness, suicidal, depression_diagnosis, depression_treatment, gad_score, anxiousness, anxiety_diagnosis, anxiety_treatment, epworth_score, sleepiness):
-    depression_prediction = predict_depression_severity(age, gender, bmi, who_bmi, phq_score, depressiveness, suicidal, depression_diagnosis, depression_treatment, gad_score, anxiousness, anxiety_diagnosis, anxiety_treatment, epworth_score, sleepiness)
-    anxiety_prediction = predict_anxiety_severity(age, gender, bmi, who_bmi, phq_score, depressiveness, suicidal, depression_diagnosis, depression_treatment, gad_score, anxiousness, anxiety_diagnosis, anxiety_treatment, epworth_score, sleepiness)
-    
-    return depression_prediction, anxiety_prediction
-
-# Gradio interface setup
-inputs = [
-    gr.Number(label="Age (in years)", info="Enter your age."),
-    gr.Radio(["Male", "Female"], label="Gender", info="Select your gender."),
-    gr.Number(label="BMI", info="Enter your BMI value."),
-    gr.Number(label="WHO BMI Classification", info="Enter your WHO BMI classification (0: Underweight, 1: Normal, 2: Overweight, 3: Obese)."),
-    gr.Number(label="PHQ Score", info="Enter your PHQ-9 score for depression (0-27)."),
-    gr.Number(label="Depressiveness", info="Enter your level of depressiveness (0-1)."),
-    gr.Radio([0, 1], label="Suicidal", info="Indicate if you've experienced suicidal thoughts (0: No, 1: Yes)."),
-    gr.Radio([0, 1], label="Depression Diagnosis", info="Indicate if you've been diagnosed with depression (0: No, 1: Yes)."),
-    gr.Radio([0, 1], label="Depression Treatment", info="Indicate if you're receiving treatment for depression (0: No, 1: Yes)."),
-    gr.Number(label="GAD Score", info="Enter your GAD-7 score for anxiety (0-21)."),
-    gr.Number(label="Anxiousness", info="Enter your level of anxiousness (0-1)."),
-    gr.Radio([0, 1], label="Anxiety Diagnosis", info="Indicate if you've been diagnosed with anxiety (0: No, 1: Yes)."),
-    gr.Radio([0, 1], label="Anxiety Treatment", info="Indicate if you're receiving treatment for anxiety (0: No, 1: Yes)."),
-    gr.Number(label="Epworth Score", info="Enter your Epworth Sleepiness Scale score (0-24)."),
-    gr.Number(label="Sleepiness", info="Enter your sleepiness score (0-1).")
-]
-
-outputs = [
-    gr.Textbox(label="Predicted Depression Severity", info="The predicted severity of your depression."),
-    gr.Textbox(label="Predicted Anxiety Severity", info="The predicted severity of your anxiety.")
-]
-
-# Creating the interface with a submit button
-interface = gr.Interface(
-    fn=predict_both,
-    inputs=inputs,
-    outputs=outputs,
-    title="Mental Health Severity Prediction",
-    description="This app predicts the severity of depression and anxiety based on various inputs related to mental health. Please fill in the fields below and click submit to get predictions.",
-    live=False,  # Set to False to ensure predictions are only made after submit
-    allow_flagging="never",  # Prevent users from flagging the results
-)
-
-# Launch the app
-interface.launch()
+import gradio as gr
+import pandas as pd
+import joblib
+
+# Load the trained models
+xgb_model_depression = joblib.load('xgb_model_depression.pkl')
+xgb_model_anxiety = joblib.load('xgb_model_anxiety.pkl')
+
+# Depression and Anxiety Severity Class Mappings
+depression_classes = {
+    0: "No Depression",
+    1: "Mild Depression",
+    2: "Moderate Depression",
+    3: "Severe Depression",
+    4: "Very Severe Depression",
+    5: "Extreme Depression"
+}
+
+anxiety_classes = {
+    0: "No Anxiety",
+    1: "Mild Anxiety",
+    2: "Moderate Anxiety",
+    3: "Severe Anxiety",
+    4: "Extreme Anxiety"
+}
+
+# Prediction functions
+def predict_depression_severity(age, gender, bmi, who_bmi, phq_score, depressiveness, suicidal, depression_diagnosis, depression_treatment, gad_score, anxiousness, anxiety_diagnosis, anxiety_treatment, epworth_score, sleepiness):
+    # Convert categorical values to numeric
+    gender = 0 if gender == "Male" else 1
+    suicidal = int(suicidal)  # Convert to int (0 or 1)
+    depression_diagnosis = int(depression_diagnosis)  # Convert to int (0 or 1)
+    depression_treatment = int(depression_treatment)  # Convert to int (0 or 1)
+    anxiety_diagnosis = int(anxiety_diagnosis)  # Convert to int (0 or 1)
+    anxiety_treatment = int(anxiety_treatment)  # Convert to int (0 or 1)
+    sleepiness = int(sleepiness)  # Convert to int (0 or 1)
+
+    input_data = {
+        'age': age,
+        'gender': gender,
+        'bmi': bmi,
+        'who_bmi': who_bmi,
+        'phq_score': phq_score,
+        'depressiveness': depressiveness,
+        'suicidal': suicidal,
+        'depression_diagnosis': depression_diagnosis,
+        'depression_treatment': depression_treatment,
+        'gad_score': gad_score,
+        'anxiousness': anxiousness,
+        'anxiety_diagnosis': anxiety_diagnosis,
+        'anxiety_treatment': anxiety_treatment,
+        'epworth_score': epworth_score,
+        'sleepiness': sleepiness
+    }
+    
+    # Ensure the input is a DataFrame
+    input_df = pd.DataFrame([input_data])
+    prediction = xgb_model_depression.predict(input_df)
+    
+    # Map prediction to readable class
+    return depression_classes[prediction[0]]
+
+def predict_anxiety_severity(age, gender, bmi, who_bmi, phq_score, depressiveness, suicidal, depression_diagnosis, depression_treatment, gad_score, anxiousness, anxiety_diagnosis, anxiety_treatment, epworth_score, sleepiness):
+    # Convert categorical values to numeric
+    gender = 0 if gender == "Male" else 1
+    suicidal = int(suicidal)  # Convert to int (0 or 1)
+    depression_diagnosis = int(depression_diagnosis)  # Convert to int (0 or 1)
+    depression_treatment = int(depression_treatment)  # Convert to int (0 or 1)
+    anxiety_diagnosis = int(anxiety_diagnosis)  # Convert to int (0 or 1)
+    anxiety_treatment = int(anxiety_treatment)  # Convert to int (0 or 1)
+    sleepiness = int(sleepiness)  # Convert to int (0 or 1)
+
+    input_data = {
+        'age': age,
+        'gender': gender,
+        'bmi': bmi,
+        'who_bmi': who_bmi,
+        'phq_score': phq_score,
+        'depressiveness': depressiveness,
+        'suicidal': suicidal,
+        'depression_diagnosis': depression_diagnosis,
+        'depression_treatment': depression_treatment,
+        'gad_score': gad_score,
+        'anxiousness': anxiousness,
+        'anxiety_diagnosis': anxiety_diagnosis,
+        'anxiety_treatment': anxiety_treatment,
+        'epworth_score': epworth_score,
+        'sleepiness': sleepiness
+    }
+    
+    # Ensure the input is a DataFrame
+    input_df = pd.DataFrame([input_data])
+    prediction = xgb_model_anxiety.predict(input_df)
+    
+    # Map prediction to readable class
+    return anxiety_classes[prediction[0]]
+
+# Wrapper function to call both predictions
+def predict_both(age, gender, bmi, who_bmi, phq_score, depressiveness, suicidal, depression_diagnosis, depression_treatment, gad_score, anxiousness, anxiety_diagnosis, anxiety_treatment, epworth_score, sleepiness):
+    depression_prediction = predict_depression_severity(age, gender, bmi, who_bmi, phq_score, depressiveness, suicidal, depression_diagnosis, depression_treatment, gad_score, anxiousness, anxiety_diagnosis, anxiety_treatment, epworth_score, sleepiness)
+    anxiety_prediction = predict_anxiety_severity(age, gender, bmi, who_bmi, phq_score, depressiveness, suicidal, depression_diagnosis, depression_treatment, gad_score, anxiousness, anxiety_diagnosis, anxiety_treatment, epworth_score, sleepiness)
+    
+    return depression_prediction, anxiety_prediction
+
+# Gradio interface setup
+inputs = [
+    gr.Number(label="Age (in years)", info="Enter your age."),
+    gr.Radio(["Male", "Female"], label="Gender", info="Select your gender."),
+    gr.Number(label="BMI", info="Enter your BMI value."),
+    gr.Number(label="WHO BMI Classification", info="Enter your WHO BMI classification (0: Underweight, 1: Normal, 2: Overweight, 3: Obese)."),
+    gr.Number(label="PHQ Score", info="Enter your PHQ-9 score for depression (0-27)."),
+    gr.Number(label="Depressiveness", info="Enter your level of depressiveness (0-1)."),
+    gr.Radio([0, 1], label="Suicidal", info="Indicate if you've experienced suicidal thoughts (0: No, 1: Yes)."),
+    gr.Radio([0, 1], label="Depression Diagnosis", info="Indicate if you've been diagnosed with depression (0: No, 1: Yes)."),
+    gr.Radio([0, 1], label="Depression Treatment", info="Indicate if you're receiving treatment for depression (0: No, 1: Yes)."),
+    gr.Number(label="GAD Score", info="Enter your GAD-7 score for anxiety (0-21)."),
+    gr.Number(label="Anxiousness", info="Enter your level of anxiousness (0-1)."),
+    gr.Radio([0, 1], label="Anxiety Diagnosis", info="Indicate if you've been diagnosed with anxiety (0: No, 1: Yes)."),
+    gr.Radio([0, 1], label="Anxiety Treatment", info="Indicate if you're receiving treatment for anxiety (0: No, 1: Yes)."),
+    gr.Number(label="Epworth Score", info="Enter your Epworth Sleepiness Scale score (0-24)."),
+    gr.Number(label="Sleepiness", info="Enter your sleepiness score (0-1).")
+]
+
+outputs = [
+    gr.Textbox(label="Predicted Depression Severity", info="The predicted severity of your depression."),
+    gr.Textbox(label="Predicted Anxiety Severity", info="The predicted severity of your anxiety.")
+]
+
+# Creating the interface with a submit button
+interface = gr.Interface(
+    fn=predict_both,
+    inputs=inputs,
+    outputs=outputs,
+    title="Mental Health Severity Prediction",
+    description="This app predicts the severity of depression and anxiety based on various inputs related to mental health. Please fill in the fields below and click submit to get predictions.",
+    live=False,  # Set to False to ensure predictions are only made after submit
+    allow_flagging="never",  # Prevent users from flagging the results
+)
+
+# Launch the app
+interface.launch()