import streamlit as st
import pandas as pd
import json
from datetime import datetime

# Function to load patient data from JSON
def load_data():
    with open('data.json', 'r') as f:
        return pd.DataFrame(json.load(f))

# Function to save patient data to JSON
def save_data(df):
    with open('data.json', 'w') as f:
        json.dump(df.to_dict(orient='records'), f, indent=4)

# Load the existing data
df = load_data()

# Sidebar for navigation
st.sidebar.title("Navigation")
options = ["View Patient Data", "Add New Patient", "Add New Visit"]
choice = st.sidebar.selectbox("Choose an option", options)

if choice == "View Patient Data":
    # Sidebar for Patient Selection
    st.sidebar.header('Select Patient')
    patient_id = st.sidebar.selectbox('Patient ID', df['patient_id'].unique())

    # Filter Data for Selected Patient
    patient_data = df[df['patient_id'] == patient_id]

    # Display Patient Profile
    st.header('Patient Profile')
    st.write(f"Name: {patient_data['name'].values[0]}")
    st.write(f"Age: {patient_data['age'].values[0]}")
    st.write(f"Gender: {patient_data['gender'].values[0]}")
    st.write(f"Medical History: {patient_data['medical_history'].values[0]}")

    # Visualization of Vital Signs
    st.header('Vital Signs Over Time')

    # Line Chart for Heart Rate
    fig = px.line(patient_data, x='date', y='heart_rate', title='Heart Rate Over Time')
    st.plotly_chart(fig)

    # Line Chart for Blood Pressure
    fig = px.line(patient_data, x='date', y='blood_pressure', title='Blood Pressure Over Time')
    st.plotly_chart(fig)

    # Line Chart for Blood Glucose
    fig = px.line(patient_data, x='date', y='blood_glucose', title='Blood Glucose Over Time')
    st.plotly_chart(fig)

    # Dropdown for selecting specific visit details
    st.header('Previous Visit Details')
    selected_date = st.selectbox('Select Visit Date', patient_data['date'].unique())
    selected_visit = patient_data[patient_data['date'] == selected_date]

    st.write(f"**Visit Date:** {selected_date}")
    st.write(f"Heart Rate: {selected_visit['heart_rate'].values[0]}")
    st.write(f"Blood Pressure: {selected_visit['blood_pressure'].values[0]}")
    st.write(f"Blood Glucose: {selected_visit['blood_glucose'].values[0]}")

    # Alerts and Notifications
    st.header('Alerts')
    if selected_visit['heart_rate'].values[0] > 100:
        st.error('High heart rate detected!')
    if selected_visit['blood_pressure'].values[0] > 140:
        st.error('High blood pressure detected!')

    # Download Button for Patient Data
    st.download_button(
        label="Download Patient Data as CSV",
        data=patient_data.to_csv().encode('utf-8'),
        file_name=f'patient_{patient_id}_data.csv',
        mime='text/csv',
    )

elif choice == "Add New Patient":
    st.header("Add New Patient")

    # Input fields for new patient data
    new_patient_id = st.number_input("Patient ID", min_value=0, step=1)
    new_name = st.text_input("Name")
    new_age = st.number_input("Age", min_value=0, step=1)
    new_gender = st.selectbox("Gender", ["Male", "Female"])
    new_medical_history = st.text_area("Medical History")

    if st.button("Add Patient"):
        new_patient_data = {
            'patient_id': new_patient_id,
            'name': new_name,
            'age': new_age,
            'gender': new_gender,
            'medical_history': new_medical_history,
            'date': None,
            'heart_rate': None,
            'blood_pressure': None,
            'blood_glucose': None,
            'temperature': None,
            'medical_complaints': None,
            'symptoms': None,
            'physical_examination': None,
            'diagnosis': None,
            'extra_notes': None,
            'treatment': None
        }
        df = pd.concat([df, pd.DataFrame([new_patient_data])], ignore_index=True)
        save_data(df)
        st.success("New patient added successfully!")

elif choice == "Add New Visit":
    st.header("Add New Visit")

    # Input fields for adding a new visit
    patient_id = st.number_input("Patient ID", min_value=0, step=1)
    visit_date = st.date_input("Date of Visit", value=datetime.today())
    medical_complaints = st.text_area("Medical Complaints")
    symptoms = st.text_area("Symptoms")
    physical_examination = st.text_area("Physical Examination")
    diagnosis = st.text_area("Diagnosis")
    heart_rate = st.number_input("Heart Rate", min_value=0)
    blood_pressure = st.number_input("Blood Pressure", min_value=0)
    temperature = st.number_input("Temperature", min_value=0)
    glucose = st.number_input("Blood Glucose", min_value=0)
    extra_notes = st.text_area("Extra Notes")
    treatment = st.text_area("Treatment")

    if st.button("Add Visit"):
        new_visit_data = {
            'patient_id': patient_id,
            'name': df[df['patient_id'] == patient_id]['name'].values[0],
            'age': df[df['patient_id'] == patient_id]['age'].values[0],
            'gender': df[df['patient_id'] == patient_id]['gender'].values[0],
            'medical_history': df[df['patient_id'] == patient_id]['medical_history'].values[0],
            'date': visit_date.strftime('%Y-%m-%d'),
            'heart_rate': heart_rate,
            'blood_pressure': blood_pressure,
            'blood_glucose': glucose,
            'temperature': temperature,
            'medical_complaints': medical_complaints,
            'symptoms': symptoms,
            'physical_examination': physical_examination,
            'diagnosis': diagnosis,
            'extra_notes': extra_notes,
            'treatment': treatment
        }
        df = pd.concat([df, pd.DataFrame([new_visit_data])], ignore_index=True)
        save_data(df)
        st.success("New visit added successfully!")