Update app.py

app.py

@@ -3,18 +3,27 @@ import pandas as pd
 import requests
 
 # Function to analyze stress level based on various factors
-def analyze_stress_level(df, anxiety_level, self_esteem, academic_performance, study_load, depression):
-    filtered_df = df[
-        (df['anxiety_level'] == anxiety_level) &
-        (df['self_esteem'] == self_esteem) &
-        (df['academic_performance'] == academic_performance) &
-        (df['study_load'] == study_load) &
-        (df['depression'] == depression)
-    ]
+def analyze_stress_level(df, inputs):
+    filtered_df = df
+    for column, value in inputs.items():
+        if column in df.columns:
+            filtered_df = filtered_df[filtered_df[column] == value]
+
     if not filtered_df.empty:
         return filtered_df.iloc[0]['stress_level']
     return "No matching data found."
 
+# Function to provide advice based on stress level
+def provide_advice(stress_level):
+    if stress_level == "Low":
+        return "Keep up the good work! Continue maintaining a balanced lifestyle."
+    elif stress_level == "Moderate":
+        return "Consider practicing relaxation techniques like yoga or meditation. Maintain healthy sleep and exercise routines."
+    elif stress_level == "High":
+        return "Seek support from counselors or health professionals. Share your concerns with trusted friends or family."
+    else:
+        return "No specific advice available."
+
 # Function to fetch related health articles from GROC API
 def get_health_documents_from_groc(query):
     api_key = "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"  # Replace with your actual GROC API key
@@ -42,20 +51,30 @@ def main():
         st.write("Dataset Preview:")
         st.dataframe(df.head())
 
-        # Input fields
-        anxiety_level = st.selectbox("Anxiety Level", df['anxiety_level'].unique())
-        self_esteem = st.selectbox("Self Esteem", df['self_esteem'].unique())
-        academic_performance = st.selectbox("Academic Performance", df['academic_performance'].unique())
-        study_load = st.selectbox("Study Load", df['study_load'].unique())
-        depression = st.selectbox("Depression", df['depression'].unique())
+        # Dynamic input fields based on dataset columns
+        inputs = {}
+        for column in [
+            'anxiety_level', 'self_esteem', 'mental_health_history', 'depression',
+            'headache', 'blood_pressure', 'sleep_quality', 'breathing_problem',
+            'noise_level', 'living_conditions', 'safety', 'basic_needs',
+            'academic_performance', 'study_load', 'teacher_student_relationship',
+            'future_career_concerns', 'social_support', 'peer_pressure',
+            'extracurricular_activities', 'bullying'
+        ]:
+            if column in df.columns:
+                inputs[column] = st.selectbox(f"Select {column.replace('_', ' ').title()}", df[column].unique())
 
         # Analyze stress level
         if st.button("Analyze Stress Level"):
-            stress_level = analyze_stress_level(df, anxiety_level, self_esteem, academic_performance, study_load, depression)
+            stress_level = analyze_stress_level(df, inputs)
             st.write(f"Stress Level: {stress_level}")
 
+            # Provide advice
+            advice = provide_advice(stress_level)
+            st.write(f"Advice: {advice}")
+
             # Fetch related health articles
-            query = f"Stress management articles for stress level: {stress_level}"
+            query = f"Health advice for stress level: {stress_level}"
             articles = get_health_documents_from_groc(query)
             st.write("Related Health Articles:")
             for article in articles: