# API = "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"
import os
import streamlit as st
from groq import Groq
import pandas as pd
import speech_recognition as sr
import pyttsx3
from tabulate import tabulate  # Import tabulate for better table formatting

# API_KEY (Replace with your actual Groq API key)
API = "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"

# Set up the Groq client
client = Groq(api_key=API)

# Function to process user input with Llama model
def process_prompt(prompt, model="llama-3.3-70b-versatile"):
    try:
        chat_completion = client.chat.completions.create(
            messages=[{"role": "user", "content": prompt}],
            model=model,
            stream=False
        )
        return chat_completion.choices[0].message.content
    except Exception as e:
        print(f"Error processing prompt: {e}")  # Log the error for debugging
        return "An error occurred. Please try again later."

# Function to process uploaded files
def process_file(file):
    if file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet":
        data = pd.read_excel(file)
    elif file.type == "text/csv":
        data = pd.read_csv(file)
    else:
        return "Unsupported file format."
    return data

# Function to generate human-friendly responses for file data
def analyze_file_data(data):
    summary = f"File contains {data.shape[0]} rows and {data.shape[1]} columns."
    response = f"""
    **File Analysis:**
    - 📄 Total Rows: {data.shape[0]}
    - 📊 Total Columns: {data.shape[1]}
    - First Few Rows:
    {tabulate.tabulate(data.head(), headers='keys', tablefmt='pipe')}  # Use tabulate for better table formatting
    """
    return response

# Function for speech-to-text
def speech_to_text():
    recognizer = sr.Recognizer()
    with sr.Microphone() as source:
        st.write("🎙️ Listening...")
        audio = recognizer.listen(source)
    try:
        return recognizer.recognize_google(audio)
    except sr.UnknownValueError:
        return "Sorry, I didn't catch that."

# Function for text-to-speech
def text_to_speech(text):
    engine = pyttsx3.init()
    engine.say(text)
    engine.runAndWait()

# Function for chat history management (using session_state)
def get_chat_history():
    chat_history = st.session_state.get("chat_history", [])
    return chat_history

def update_chat_history(user_input, response):
    chat_history = get_chat_history()
    chat_history.append(("User", user_input))
    chat_history.append(("Bot", response))
    st.session_state["chat_history"] = chat_history

# Streamlit UI
def chatbot_ui():
    st.title("📢 Real-Time AI Chatbot")

    # File upload feature
    st.sidebar.header("📂 File Management")
    uploaded_file = st.sidebar.file_uploader("Upload a file (CSV/Excel)", type=["csv", "xlsx"])
    if uploaded_file:
        data = process_file(uploaded_file)
        st.sidebar.write(analyze_file_data(data))
        if st.sidebar.button("Delete File"):
            uploaded_file = None
            st.sidebar.write("File deleted.")

    # Chat section
    st.header("💬 Chat Section")
    chat_history = get_chat_history()

    with st.form("chat_form", clear_on_submit=True):
        user_input = st.text_input("Type your message or prompt here...")
        submitted = st.form_submit_button("Send")
        if submitted and user_input:
            response = process_prompt(user_input)
            update_chat_history(user_input, response)

    # Display chat history
    for sender, message in chat_history:
        if sender == "User":
            st.write(f"**👤 You:** {message}")
        else:
            st.write(f"**🤖 Bot:** {message}")

    # Save/download chat
    if st.button("Download Chat"):
        chat_file = "\n".join([f"{sender}: {message}" for sender, message in chat_history])
        st.download_button("Download", chat_file, "chat_history.txt", "text/plain")

    # Speech-to-Text Section
    st.header("🎙️ Speech-to-Text")
    if st.button("Start Speech Recognition"):
        st.write(f"**👂 You said:** {speech_to_text()}")

    # Text-to-Speech Section
    st.header("🔊 Text-to-Speech")
    tts_text = st.text_input("Enter text to convert to speech:")
    if st.button("Speak Text"):
        text_to_speech(tts_text)

if __name__ == "__main__":
    chatbot_ui()