app.py

import streamlit as st
import pandas as pd
import psycopg2
import os

# Load DB credentials from Hugging Face secrets or environment variables
DB_HOST = os.getenv("DB_HOST")
DB_PORT = os.getenv("DB_PORT", "5432")
DB_NAME = os.getenv("DB_NAME")
DB_USER = os.getenv("DB_USER")
DB_PASSWORD = os.getenv("DB_PASSWORD")

@st.cache_data(ttl=600)
def get_data():
    try:
        conn = psycopg2.connect(
            host=DB_HOST,
            port=DB_PORT,
            dbname=DB_NAME,
            user=DB_USER,
            password=DB_PASSWORD,
            sslmode="require"

        )
        query = "SELECT country, year, section, question_code, question_text, answer_code, answer_text FROM survey_info;"
        df = pd.read_sql_query(query, conn)
        conn.close()
        return df
    except Exception as e:
        st.error(f"Failed to connect to the database: {e}")
        st.stop()

# Load data
df = get_data()

# Streamlit UI
st.title("🌍 CGD Survey Explorer (Live DB)")

# Multiselect filters (defaults = empty, shows all if none selected)
country_options = sorted(df["country"].dropna().unique())
year_options = sorted(df["year"].dropna().unique())

selected_countries = st.sidebar.multiselect("Select Country/Countries", country_options)
selected_years = st.sidebar.multiselect("Select Year(s)", year_options)

keyword = st.sidebar.text_input("Keyword Search", "")

# Apply filters
filtered = df[
    (df["country"].isin(selected_countries) if selected_countries else True) &
    (df["year"].isin(selected_years) if selected_years else True) &
    (df["question_text"].str.contains(keyword, case=False, na=False))
]

# Generate dynamic subheading
heading_parts = []
if selected_countries:
    heading_parts.append("Countries: " + ", ".join(selected_countries))
if selected_years:
    heading_parts.append("Years: " + ", ".join(map(str, selected_years)))
if heading_parts:
    st.markdown("### Results for " + " | ".join(heading_parts))
else:
    st.markdown("### Results for All Countries and Years")

# Display results including answer_text
st.dataframe(filtered[["country", "year", "question_text", "answer_text"]])

# Empty result message
if filtered.empty:
    st.info("No matching questions found.")