import os
import streamlit as st
from groq import Groq
from dotenv import load_dotenv
from datetime import datetime  # Import datetime module

# Load environment variables
load_dotenv()

# Ensure 'data' folder exists (use lowercase 'data' for consistency)
if not os.path.exists("data"):
    os.makedirs("data")

# Function to get speech transcription from audio
def get_speech_transcription(audio_file):
    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    
    # Read the audio file content
    transcription = client.audio.transcriptions.create(
        file=(audio_file.name, audio_file.read()),
        model="whisper-large-v3",  # Use the appropriate model
        response_format="verbose_json",
    )

    return transcription.text

# Function to get Groq completions for the transcript
def get_groq_completions(user_content):
    client = Groq(api_key=os.getenv("GROQ_API_KEY"))
    
    prompt = f"""
    You will be provided with a transcript of a meeting. 
    Summarize the key points from the transcript in a structured format.
    If any new topics are discussed, make them as a title and corresponding discussion will be description using number points also add time how much time they discuss on those topics.

    \n{user_content}
    """
    
    completion = client.chat.completions.create(
        model="llama-3.2-90b-text-preview",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful AI assistant."
            },
            {
                "role": "user",
                "content": prompt
            }
        ],
        temperature=0.8,
        max_tokens=6000,
        top_p=1,
        stream=True,
        stop=None,
    )

    result = ""
    for chunk in completion:
        result += chunk.choices[0].delta.content or ""

    return result

# Function to save the user content in a file
def save_user_content(email, content):
    # Get the current date and time
    current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    
    # Create filename using email and current timestamp
    filename = f"{email}_{current_time}.txt"
    
    file_path = os.path.join("data", filename)  # Ensure 'data' directory is used
    with open(file_path, "w") as f:
        f.write(content)
    
    return file_path  # Return the file path for confirmation

# Streamlit interface
def main():
    # Add project title
    st.title("Minutes of Meetings")  # Add title here
    st.sidebar.title("Upload Options")
    
    # User email input
    email = st.sidebar.text_input("Please enter your email address:")
    
    # Sidebar options
    upload_option = st.sidebar.selectbox("Choose how to provide the content:", 
                                          ("Upload Audio File", "Upload Text File", "Paste Text"))
    
    user_content = ""
    
    # Conditional logic based on sidebar selection
    if upload_option == "Upload Audio File":
        audio_file = st.sidebar.file_uploader("Upload an audio file", type=["mp3", "wav", "m4a"])
        
        if audio_file and st.sidebar.button("Generate MoM from Audio"):
            if not email:
                st.warning("Please enter your email address.")
                return
            st.info("Transcribing audio... Please wait.")
            user_content = get_speech_transcription(audio_file)
            st.success("Audio transcribed successfully!")
            
    elif upload_option == "Upload Text File":
        text_file = st.sidebar.file_uploader("Upload a text file", type=["txt"])
        
        if text_file and st.sidebar.button("Generate MoM from Text File"):
            if not email:
                st.warning("Please enter your email address.")
                return
            user_content = text_file.read().decode("utf-8")
            st.success("Text file uploaded successfully!")
            
    elif upload_option == "Paste Text":
        user_content = st.sidebar.text_area("Paste your text here:")
        
        if st.sidebar.button("Generate MoM from Pasted Text"):
            if not email:
                st.warning("Please enter your email address.")
                return
            if not user_content:
                st.warning("Please paste some text before generating the MoM.")
                return
    
    if user_content:
        st.info("Generating MoM... Please wait.")
        
        # Save user content and get file path
        file_path = save_user_content(email, user_content)
        
        # Generate MoM
        generated_mom = get_groq_completions(user_content)
        
        # Display the generated MoM
        st.markdown("### Generated MoM:")
        st.text_area("", value=generated_mom, height=500)

if __name__ == "__main__":
    main()