import streamlit as st
import pandas as pd
import plotly.express as px
from streamlit_lottie import st_lottie
import requests
import ast

# Set page config at the very beginning
st.set_page_config(page_title="Health Explorer", page_icon="🩺", layout="wide")

# Function to load Lottie animation
def load_lottieurl(url: str):
    r = requests.get(url)
    if r.status_code != 200:
        return None
    return r.json()

# Load DataFrame
@st.cache_data
def load_data():
    df = pd.read_csv('disease.csv')
    # Convert string representations of lists to actual lists
    df['symptom'] = df['symptom'].apply(ast.literal_eval)
    df['aurvedic_treatments'] = df['aurvedic_treatments'].apply(ast.literal_eval)
    return df

# Load data
df = load_data()

# Sidebar
st.sidebar.title("Health Explorer 🩺")
page = st.sidebar.radio("Navigate", ["Home", "Disease Search", "Statistics"])

if page == "Home":
    st.title("Welcome to Health Explorer")
    
    col1, col2 = st.columns([2, 1])
    
    with col1:
        st.markdown("""
        Explore information about various diseases, their treatments, and prevention methods.
        Use the sidebar to navigate through different sections of the app.
        
        - **Disease Search**: Look up specific diseases and get detailed information.
        - **Statistics**: View overall statistics about the diseases in our database.
        """)
    
    with col2:
        lottie_health = load_lottieurl("https://assets1.lottiefiles.com/packages/lf20_5njp3vgg.json")
        st_lottie(lottie_health, height=200)

elif page == "Disease Search":
    st.title("Disease Information Search")
    
    # Input search query with suggestions from the Disease column
    search_query = st.text_input("Start typing a disease name...", "")
    
    # Generate suggestions dynamically based on input
    if search_query:
        suggestions = df[df['Disease'].str.contains(search_query, case=False)]['Disease'].tolist()
    else:
        suggestions = df['Disease'].tolist()
    
    # Show suggestions in a selectbox (if suggestions exist)
    selected_disease = st.selectbox("Search results:", suggestions)
    
    # If a disease is selected, display the details
    if selected_disease:
        result = df[df['Disease'] == selected_disease]
        for index, row in result.iterrows():
            st.header(row['Disease'])
            
            col1, col2 = st.columns(2)
            
            with col1:
                st.subheader("Treatment")
                st.info(row['treatment'])
                
                st.subheader("Prevention")
                st.success(row['prevention'])
            
            with col2:
                st.subheader("Symptoms")
                for symptom in row['symptom']:
                    st.warning(f"• {symptom}")
                
                st.subheader("Ayurvedic Treatments")
                for treatment in row['aurvedic_treatments']:
                    st.info(f"• {treatment}")
            
            # Word cloud of symptoms
            try:
                from wordcloud import WordCloud
                import matplotlib.pyplot as plt
                
                wordcloud = WordCloud(width=800, height=400, background_color='white').generate(' '.join(row['symptom']))
                
                plt.figure(figsize=(10, 5))
                plt.imshow(wordcloud, interpolation='bilinear')
                plt.axis('off')
                st.pyplot(plt)
            except ImportError:
                st.write("Word cloud visualization is not available.")

elif page == "Statistics":
    st.title("Disease Statistics")
    
    # Count of diseases by treatment type
    treatment_counts = df['treatment'].value_counts()
    
    fig = px.pie(values=treatment_counts.values, names=treatment_counts.index, title="Distribution of Treatment Types")
    st.plotly_chart(fig)
    
    # Top 10 most common symptoms
    all_symptoms = [symptom for symptoms in df['symptom'] for symptom in symptoms]
    symptom_counts = pd.Series(all_symptoms).value_counts().head(10)
    
    fig = px.bar(x=symptom_counts.index, y=symptom_counts.values, title="Top 10 Most Common Symptoms")
    fig.update_xaxes(title="Symptom")
    fig.update_yaxes(title="Frequency")
    st.plotly_chart(fig)

# Footer
st.sidebar.markdown("---")
st.sidebar.info("Created with ❤️ by [Sumit Yadav](sumityadav.com.np)")