Delete app.py

app.py

@@ -1,148 +0,0 @@
-import os
-import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import requests
-
-news_api_key = "fe1e6bcbbf384b3e9220a7a1138805e0"  # Replace with your News API key
-
-@st.cache_data
-def load_data(file):
-    return pd.read_csv(file)
-
-def fetch_health_articles(query):
-    url = f"https://newsapi.org/v2/everything?q={query}&apiKey={news_api_key}"
-    response = requests.get(url)
-    if response.status_code == 200:
-        articles = response.json().get('articles', [])
-        return articles[:5]
-    else:
-        st.error("Failed to fetch news articles. Please check your API key or try again later.")
-        return []
-
-def provide_advice_from_articles(data):
-    advice = []
-    if data['depression'] > 7:
-        advice.append("Searching for articles related to high depression...")
-        articles = fetch_health_articles("high depression")
-        for article in articles:
-            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
-    elif data['anxiety_level'] > 7:
-        advice.append("Searching for articles related to high anxiety...")
-        articles = fetch_health_articles("high anxiety")
-        for article in articles:
-            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
-    elif data['stress_level'] > 7:
-        advice.append("Searching for articles related to high stress...")
-        articles = fetch_health_articles("high stress")
-        for article in articles:
-            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
-    else:
-        advice.append("Searching for general health advice articles...")
-        articles = fetch_health_articles("mental health")
-        for article in articles:
-            advice.append(f"**{article['title']}**\n{article['description']}\n[Read more]({article['url']})")
-    return advice
-
-def plot_graphs(data):
-    # Create subplots for visualization
-    st.markdown("### 📊 Data Visualizations")
-    st.write("Explore key insights through visualizations.")
-
-    # Correlation heatmap
-    st.markdown("#### Correlation Heatmap")
-    fig, ax = plt.subplots(figsize=(10, 8))
-    sns.heatmap(data.corr(), annot=True, cmap="coolwarm", ax=ax)
-    ax.set_title("Correlation Heatmap")
-    st.pyplot(fig)
-
-def main():
-    st.set_page_config(
-        page_title="Student Well-being Advisor",
-        page_icon="📊",
-        layout="wide",
-        initial_sidebar_state="expanded",
-    )
-
-    st.sidebar.title("Navigation")
-    st.sidebar.write("Use the sidebar to navigate through the app.")
-    st.sidebar.markdown("### 📂 Upload Data")
-    st.sidebar.write("Start by uploading your dataset for analysis.")
-    st.sidebar.markdown("### 📊 Analysis & Advice")
-    st.sidebar.write("Get detailed insights and personalized advice.")
-
-    st.title("🎓 Student Well-being Advisor")
-    st.subheader("Analyze data and provide professional mental health recommendations.")
-    st.write("""
-        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: {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 Articles")
-                advice = provide_advice_from_articles(row_data)
-                if advice:
-                    for i, tip in enumerate(advice, 1):
-                        st.write(f"📌 **{i}.** {tip}")
-                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.")
-
-if __name__ == "__main__":
-    main()