Update app.py

app.py

@@ -2,22 +2,23 @@ import streamlit as st
 import pandas as pd
 import requests
 
-# Function to get health advice based on inputs
-def get_health_advice(df, age, heart_rate, systolic_bp, diastolic_bp):
+# 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['Age'].between(age - 2, age + 2)) &  # Allow ±2 years
-        (df['Heart_Rate'].between(heart_rate - 5, heart_rate + 5)) &  # Allow ±5 bpm
-        (df['Blood_Pressure_Systolic'].between(systolic_bp - 10, systolic_bp + 10)) &  # Allow ±10
-        (df['Blood_Pressure_Diastolic'].between(diastolic_bp - 10, diastolic_bp + 10))  # Allow ±10
+        (df['anxiety_level'] == anxiety_level) &
+        (df['self_esteem'] == self_esteem) &
+        (df['academic_performance'] == academic_performance) &
+        (df['study_load'] == study_load) &
+        (df['depression'] == depression)
     ]
     if not filtered_df.empty:
-        return filtered_df.iloc[0]['Health_Risk_Level']
-    return "No matching health data found."
+        return filtered_df.iloc[0]['stress_level']
+    return "No matching data found."
 
-# Function to get health articles from GROC API
+# Function to fetch related health articles from GROC API
 def get_health_documents_from_groc(query):
     api_key = "YOUR_GROC_API_KEY"  # Replace with your actual GROC API key
-    url = f"https://api.groc.com/v1/search"
+    url = "https://api.groc.com/v1/search"
     params = {
         "query": query,
         "api_key": api_key,
@@ -25,38 +26,38 @@ def get_health_documents_from_groc(query):
     }
     response = requests.get(url, params=params)
     if response.status_code == 200:
-        data = response.json()
-        return data.get("results", [])
+        return response.json().get("results", [])
     else:
         st.error(f"Error {response.status_code}: {response.text}")
-        return [{"title": f"Error: {response.status_code}", "url": ""}]
+        return []
 
 # Main Streamlit app
 def main():
-    st.title("Health Risk Level and Advisory Assistant")
+    st.title("Student Stress Analysis and Health Advisory")
 
-    # File upload
+    # 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:")
         st.dataframe(df.head())
 
-        # User input
-        age = st.number_input("Enter Age", min_value=1, max_value=100, step=1)
-        heart_rate = st.number_input("Enter Heart Rate (bpm)", min_value=30, max_value=200, step=1)
-        systolic_bp = st.number_input("Enter Systolic Blood Pressure", min_value=80, max_value=200, step=1)
-        diastolic_bp = st.number_input("Enter Diastolic Blood Pressure", min_value=40, max_value=120, step=1)
+        # 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())
 
-        # Predict health risk level
-        if st.button("Get Health Risk Level"):
-            risk_level = get_health_advice(df, age, heart_rate, systolic_bp, diastolic_bp)
-            st.write(f"Health Risk Level: {risk_level}")
+        # Analyze stress level
+        if st.button("Analyze Stress Level"):
+            stress_level = analyze_stress_level(df, anxiety_level, self_esteem, academic_performance, study_load, depression)
+            st.write(f"Stress Level: {stress_level}")
 
-            # Retrieve related health articles
-            query = f"Health risk for age {age}, heart rate {heart_rate}, BP {systolic_bp}/{diastolic_bp}"
-            st.write("Related Health Articles:")
+            # Fetch related health articles
+            query = f"Stress management articles 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']})")