Update app.py

app.py

@@ -3,13 +3,10 @@ import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
-import requests
-from googleapiclient.discovery import build
-from google.oauth2.credentials import Credentials
 from groq import Groq
 
 # Groq API Key (replace with your actual API key)
-groq_api_key = "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"
+groq_api_key = "your_groq_api_key"
 
 # Initialize Groq client
 client = Groq(api_key=groq_api_key)
@@ -75,70 +72,69 @@ def main():
         This app helps identify areas of concern in students' well-being and provides personalized advice based on their responses.
     """)
 
-    st.markdown("## 📂 Upload Your Dataset")
-    uploaded_file = st.file_uploader("Upload your dataset (CSV)", type=["csv"])
-    if uploaded_file:
-        df = load_data(uploaded_file)
-        st.success("Dataset uploaded successfully!")
-        st.write("### Dataset Preview:")
-        st.dataframe(df.head())
-
-        required_columns = [
-            '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', 'stress_level'
-        ]
-        missing_columns = [col for col in required_columns if col not in df.columns]
-        
-        if missing_columns:
-            st.error(f"The uploaded dataset is missing the following required columns: {', '.join(missing_columns)}")
-        else:
-            if df.isnull().values.any():
-                st.warning("The dataset contains missing values. Rows with missing values will be skipped.")
-                df = df.dropna()
-
-            tab1, tab2, tab3 = st.tabs(["🏠 Home", "📊 Analysis", "📰 Resources"])
-
-            with tab1:
-                st.write("### Welcome to the Well-being Advisor!")
-                st.write("""
-                    Use the tabs to explore data, generate advice, and access mental health resources.
-                """)
-
-            with tab2:
-                st.markdown("### 📊 Select a Row for Analysis")
-                selected_row = st.selectbox(
-                    "Select a row (based on index) to analyze:",
-                    options=df.index,
-                    format_func=lambda x: f"Row {x} - Stress Level: {stress_level_to_string(df.loc[x, 'stress_level'])}, Anxiety: {df.loc[x, 'anxiety_level']}, Depression: {df.loc[x, 'depression']}",
-                )
-                row_data = df.loc[selected_row].to_dict()
-                st.write("### Selected User Details:")
-                st.json(row_data)
-
-                st.subheader("🔔 Health Advice Based on Groq")
-                advice = get_personalized_advice(row_data)
-                if advice:
-                    st.write(advice)
-                else:
-                    st.warning("No specific advice available based on this user's data.")
-
-                # Include graphs in analysis tab
-                plot_graphs(df)
-
-            with tab3:
-                st.subheader("📰 Mental Health Resources")
-                articles = fetch_health_articles("mental health")
-                if articles:
-                    for article in articles:
-                        st.write(f"**{article['title']}**")
-                        st.write(f"{article['description']}")
-                        st.write(f"[Read more]({article['url']})")
-                else:
-                    st.write("No articles available at the moment.")
+    # Dynamic Data Input
+    st.markdown("## 📋 Enter Your Data")
+    st.write("Please fill out the form below to receive personalized well-being advice.")
+
+    # Dynamic form for user input
+    anxiety_level = st.slider("Anxiety Level (1 to 10)", 1, 10, 5)
+    self_esteem = st.slider("Self-esteem (1 to 10)", 1, 10, 5)
+    mental_health_history = st.selectbox("Mental Health History", ["None", "Minor Issues", "Moderate Issues", "Severe Issues"])
+    depression = st.slider("Depression Level (1 to 10)", 1, 10, 5)
+    headache = st.slider("Headache Level (1 to 10)", 1, 10, 5)
+    blood_pressure = st.slider("Blood Pressure (1 to 10)", 1, 10, 5)
+    sleep_quality = st.slider("Sleep Quality (1 to 10)", 1, 10, 5)
+    breathing_problem = st.selectbox("Breathing Problem", ["None", "Mild", "Severe"])
+    noise_level = st.slider("Noise Level (1 to 10)", 1, 10, 5)
+    living_conditions = st.slider("Living Conditions (1 to 10)", 1, 10, 5)
+    safety = st.slider("Safety (1 to 10)", 1, 10, 5)
+    basic_needs = st.slider("Basic Needs Satisfaction (1 to 10)", 1, 10, 5)
+    academic_performance = st.slider("Academic Performance (1 to 10)", 1, 10, 5)
+    study_load = st.slider("Study Load (1 to 10)", 1, 10, 5)
+    teacher_student_relationship = st.slider("Teacher-Student Relationship (1 to 10)", 1, 10, 5)
+    future_career_concerns = st.slider("Future Career Concerns (1 to 10)", 1, 10, 5)
+    social_support = st.slider("Social Support (1 to 10)", 1, 10, 5)
+    peer_pressure = st.slider("Peer Pressure (1 to 10)", 1, 10, 5)
+    extracurricular_activities = st.slider("Extracurricular Activities (1 to 10)", 1, 10, 5)
+    bullying = st.slider("Bullying Level (1 to 10)", 1, 10, 5)
+    stress_level = st.slider("Stress Level (0 - Low, 1 - Moderate, 2 - High)", 0, 2, 1)
+
+    user_data = {
+        "anxiety_level": anxiety_level,
+        "self_esteem": self_esteem,
+        "mental_health_history": mental_health_history,
+        "depression": depression,
+        "headache": headache,
+        "blood_pressure": blood_pressure,
+        "sleep_quality": sleep_quality,
+        "breathing_problem": breathing_problem,
+        "noise_level": noise_level,
+        "living_conditions": living_conditions,
+        "safety": safety,
+        "basic_needs": basic_needs,
+        "academic_performance": academic_performance,
+        "study_load": study_load,
+        "teacher_student_relationship": teacher_student_relationship,
+        "future_career_concerns": future_career_concerns,
+        "social_support": social_support,
+        "peer_pressure": peer_pressure,
+        "extracurricular_activities": extracurricular_activities,
+        "bullying": bullying,
+        "stress_level": stress_level
+    }
+
+    st.write(f"### Selected User Data: {user_data}")
+
+    # Provide personalized advice
+    st.subheader("🔔 Health Advice Based on Your Input")
+    advice = get_personalized_advice(user_data)
+    if advice:
+        st.write(advice)
+    else:
+        st.warning("No advice available at the moment.")
+
+    # Include graphs (if relevant data exists)
+    plot_graphs(pd.DataFrame([user_data]))  # Placeholder for any data visualization logic
 
 if __name__ == "__main__":
     main()