import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from datetime import date
import os
from groq import Groq
from gtts import gTTS
import tempfile
import requests

# Set your API keys here
api_key = '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'
news_api_key = '11a477927da6466ca67f7fc512a2d8f2'  # Add your News API key

# Initialize Groq client
client = Groq(api_key=api_key)

# Function for MedBot
def query_groq(prompt):
    try:
        response = client.chat.completions.create(
            messages=[{"role": "user", "content": prompt}],
            model="llama3-8b-8192"
        )
        return response.choices[0].message.content
    except Exception as e:
        return f"Error interacting with Groq API: {e}"

def text_to_speech(text):
    try:
        tts = gTTS(text=text, lang='en')
        temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
        tts.save(temp_audio.name)
        return temp_audio.name
    except Exception as e:
        return f"Error generating speech: {e}"

def med_chatbot(user_input):
    llm_response = query_groq(user_input)
    if "Error" in llm_response:
        return llm_response, None
    audio_path = text_to_speech(llm_response)
    return llm_response, audio_path

# Function to fetch latest healthcare/therapist news
def get_healthcare_news():
    url = f'https://newsapi.org/v2/everything?q=healthcare OR therapy&apiKey={news_api_key}'
    try:
        response = requests.get(url)
        news_data = response.json()
        articles = news_data.get('articles', [])
        news_list = []
        for article in articles[:5]:  # Get the top 5 articles
            title = article.get('title', 'No Title')
            description = article.get('description', 'No Description')
            url = article.get('url', '')
            news_list.append(f"**{title}**\n{description}\n[Read more]({url})\n\n")
        return news_list
    except Exception as e:
        return [f"Error fetching news: {e}"]

# Streamlit Page Configuration
st.set_page_config(page_title="Healthcare Application", layout="wide")

# Add a logo at the top of your app
logo_path = 'https://huggingface.co/spaces/King-Afridi/MediTherapist-Your-Complete-Health-Wellness-Companion/resolve/main/pexels-totalshape-2383010.jpg'  # Path to your uploaded logo
st.image(logo_path, width=200)

# Add healthcare-themed background image
st.markdown("""
    <style>
    body {
        background-image: url(background_url = 'https://huggingface.co/spaces/your-space-name/resolve/main/your-image.jpg'
); /* Path to your uploaded background image */
        background-size: cover;
        font-family: 'Arial', sans-serif;
    }
    </style>
""", unsafe_allow_html=True)

# Sidebar Navigation
menu = st.sidebar.radio("Navigation", ["MedBot", "Therapist Dashboard", "Healthcare News"])

# MedBot Interface
if menu == "MedBot":
    st.title("MedBot", anchor="medbot")
    st.write("A real-time medical chatbot with text-to-speech capabilities. Ask your questions below.")

    user_input = st.text_input("Ask your medical question:")
    if st.button("Submit"):
        response, audio_path = med_chatbot(user_input)
        st.text("Response:")
        st.write(response)
        if audio_path:
            st.audio(audio_path, format="audio/mp3")

# Therapist Dashboard Interface
elif menu == "Therapist Dashboard":
    # Updated therapist interface

    # Utility functions for persistent storage
    def load_data(file_name):
        if os.path.exists(file_name):
            return pd.read_csv(file_name)
        else:
            return pd.DataFrame()

    def save_data(file_name, data):
        data.to_csv(file_name, index=False)

    # Load stored data
    sessions_file = "sessions.csv"
    moods_file = "moods.csv"
    sessions_data = load_data(sessions_file)
    moods_data = load_data(moods_file)

    # Ensure necessary columns exist in loaded data
    if not sessions_data.empty and "Patient" not in sessions_data.columns:
        sessions_data["Patient"] = ""
    if not moods_data.empty and "Patient" not in moods_data.columns:
        moods_data["Patient"] = ""

    # Title
    st.title("Therapist Dashboard", anchor="dashboard")
    st.write("A comprehensive tool for therapists to manage sessions, track progress, and analyze patient data.")

    # Sidebar Navigation
    dashboard_menu = st.sidebar.radio("Dashboard Menu", ["Home", "Session Tracker", "Mood Tracker", "Patient Analytics"])

    if dashboard_menu == "Home":
        st.header("Welcome to the Therapist Dashboard")
        st.write("This application provides tools to enhance your therapy practice. Navigate through the sidebar to explore features.")

    elif dashboard_menu == "Session Tracker":
        st.header("Session Tracker")

        # Log session
        st.write("### Log New Session")
        session_date = st.date_input("Session Date", value=date.today())
        session_time = st.time_input("Session Time")
        patient_name = st.text_input("Patient Name")
        session_notes = st.text_area("Session Notes")

        if st.button("Save Session"):
            new_session = pd.DataFrame({
                "Date": [session_date],
                "Time": [session_time],
                "Patient": [patient_name],
                "Notes": [session_notes]
            })
            sessions_data = pd.concat([sessions_data, new_session], ignore_index=True)
            save_data(sessions_file, sessions_data)
            st.success(f"Session for {patient_name} on {session_date} saved successfully!")

        # Display session logs
        st.write("### Recent Sessions")
        if not sessions_data.empty:
            st.table(sessions_data.tail(10))
        else:
            st.write("No sessions logged yet.")

    elif dashboard_menu == "Mood Tracker":
        st.header("Mood Tracker")

        # Log mood
        st.write("### Log Mood")
        mood_date = st.date_input("Date", value=date.today(), key="mood_date")
        mood_level = st.slider("Mood Level (1-10)", 1, 10, 5)
        patient_name = st.text_input("Patient Name (for Mood Tracker)", key="mood_patient")
        mood_notes = st.text_area("Notes", key="mood_notes")

        if st.button("Save Mood Entry"):
            new_mood = pd.DataFrame({
                "Date": [mood_date],
                "Mood Level": [mood_level],
                "Patient": [patient_name],
                "Notes": [mood_notes]
            })
            moods_data = pd.concat([moods_data, new_mood], ignore_index=True)
            save_data(moods_file, moods_data)
            st.success("Mood entry saved!")

        # Display mood trends
        st.write("### Mood Trends")
        if not moods_data.empty:
            moods_data["Date"] = pd.to_datetime(moods_data["Date"])
            fig, ax = plt.subplots()
            ax.plot(moods_data["Date"], moods_data["Mood Level"], marker="o")
            ax.set_title("Mood Trends")
            ax.set_xlabel("Date")
            ax.set_ylabel("Mood Level")
            st.pyplot(fig)
        else:
            st.write("No mood entries logged yet.")

    elif dashboard_menu == "Patient Analytics":
        st.header("Patient Analytics")

        if not sessions_data.empty:
            st.write("### Session Analytics")
            analytics_data = sessions_data.groupby("Patient").size().reset_index(name="Sessions Conducted")

            if not moods_data.empty:
                avg_mood = moods_data.groupby("Patient")["Mood Level"].mean().reset_index()
                avg_mood.rename(columns={"Mood Level": "Average Mood"}, inplace=True)
                analytics_data = pd.merge(analytics_data, avg_mood, on="Patient", how="left")

            st.bar_chart(analytics_data.set_index("Patient"))

            # Detailed patient reports
            selected_patient = st.selectbox("Select a patient for detailed analytics", analytics_data["Patient"].unique())
            patient_data = sessions_data[sessions_data["Patient"] == selected_patient]
            st.write(f"### Detailed Report for {selected_patient}")
            st.table(patient_data)