Update app.py

app.py

@@ -2,83 +2,70 @@ import streamlit as st
 import pandas as pd
 import requests
 
-# Function to analyze stress level based on various factors
-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]
+# Function to load and preprocess data
+@st.cache_data
+def load_data(file):
+    df = pd.read_csv(file)
+    return df
 
-    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."
+# Function to provide advice based on input
+def provide_advice(gender, age, depression, anxiety, isolation, future_insecurity, stress_relief_activities):
+    # Analyze mental health and stress levels
+    if depression > 7 or anxiety > 7 or isolation > 7:
+        advice = "Your mental health indicators are high. Consider consulting a mental health professional for personalized support."
+    elif future_insecurity > 5:
+        advice = "Concerns about future career prospects might be contributing to your stress. Try exploring career counseling or workshops."
+    elif stress_relief_activities < 5:
+        advice = "Engage in more stress-relief activities like physical exercise, hobbies, or mindfulness practices."
     else:
-        return "No specific advice available."
+        advice = "You're managing well, but continue healthy habits and seek support if needed."
+    return advice
 
-# 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
-    url = "https://api.groc.com/v1/search"
-    params = {
-        "query": query,
-        "api_key": api_key,
-        "type": "article"
-    }
-    response = requests.get(url, params=params)
-    if response.status_code == 200:
-        return response.json().get("results", [])
-    else:
-        st.error(f"Error {response.status_code}: {response.text}")
+# Mock GROC API function for fetching related articles
+def get_health_articles(query):
+    try:
+        # Mock response (replace with actual GROC API call if available)
+        return [
+            {"title": "How to Manage Anxiety Effectively", "url": "https://example.com/anxiety-management"},
+            {"title": "Top Stress Relief Activities", "url": "https://example.com/stress-relief"}
+        ]
+    except Exception as e:
+        st.error(f"Error fetching articles: {e}")
         return []
 
-# Main Streamlit app
+# Streamlit app layout
 def main():
-    st.title("Student Stress Analysis and Health Advisory")
+    st.title("Student Health Advisory Assistant")
 
-    # Upload dataset
-    uploaded_file = st.file_uploader("Upload your dataset (CSV)", type="csv")
-    if uploaded_file is not None:
-        df = pd.read_csv(uploaded_file)
-        st.write("Dataset Preview:")
+    # File upload
+    uploaded_file = st.file_uploader("Upload your dataset (CSV)", type=["csv"])
+    if uploaded_file:
+        df = load_data(uploaded_file)
         st.dataframe(df.head())
 
-        # 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, inputs)
-            st.write(f"Stress Level: {stress_level}")
+        # User input section
+        st.header("Input Your Information")
+        gender = st.selectbox("Gender", df["gender"].unique())
+        age = st.slider("Age", 18, 35, step=1)
+        depression = st.slider("Depression Level (1-10)", 1, 10)
+        anxiety = st.slider("Anxiety Level (1-10)", 1, 10)
+        isolation = st.slider("Isolation Level (1-10)", 1, 10)
+        future_insecurity = st.slider("Future Insecurity Level (1-10)", 1, 10)
+        stress_relief_activities = st.slider("Stress Relief Activities Level (1-10)", 1, 10)
 
-            # Provide advice
-            advice = provide_advice(stress_level)
-            st.write(f"Advice: {advice}")
+        # Provide advice and fetch articles
+        if st.button("Get Advice"):
+            st.subheader("Health Advice")
+            advice = provide_advice(
+                gender, age, depression, anxiety, isolation, future_insecurity, stress_relief_activities
+            )
+            st.write(advice)
 
-            # Fetch related health articles
-            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:
-                st.markdown(f"- [{article['title']}]({article['url']})")
+            st.subheader("Related Health Articles")
+            articles = get_health_articles("mental health support")
+            if articles:
+                for article in articles:
+                    st.write(f"- [{article['title']}]({article['url']})")
 
 if __name__ == "__main__":
     main()