from model_functions import *
from preprocessor import *
import streamlit as st
import pandas as pd


@st.cache_data
def load_example_file(file):
    with open(file, "rb") as f:
        return f.read()


def main():
    # Load models
    tokenizer_sentiment, model_sentiment = load_sentiment_analyzer()
    tokenizer_summary, model_summary = load_summarizer()
    pipe_ner = load_NER()
    
    st.markdown("""
        <style>
            body {{
                background-color: #e6fedb;
            }}
        </style>
        <h1><img src="logo.png" width="60" height="60"> WhatsApp Analysis Tool</h1>
    """, unsafe_allow_html=True)
    st.markdown("This app summarizes Whatsapp chats and provides named entity recognition as well as sentiment analysis for the conversation")
    st.markdown("**NOTE**: *This app can only receive chats downloaded from IOS as the downloaded chat format is different than from Android.*")
    st.markdown("Download your whatsapp chat by going to Settings > Chats > Export Chat and there select the chat you want to summarize (download 'Without Media').")

    st.markdown("**Example Files**: Download example zip files to test the app:")
    example_files = {
        "Example 1": "example1.zip",
        "Example 2": "example2.zip",
        "Example 3": "example3.zip"
    }
    
    for name, file in example_files.items():
        data = load_example_file(file)
        st.download_button(label=name, data=data, file_name=file, mime="application/zip")
    

    # File uploader
    uploaded_file = st.file_uploader("Choose a file (.zip)", type=['zip'])
    
    if uploaded_file is not None:
        file_type = detect_file_type(uploaded_file.name)
        if file_type == "zip":
            # Process the file
            data = preprocess_whatsapp_messages(uploaded_file, file_type)
            if data.empty:
                st.write("No messages found or the file could not be processed.")
            else:
                # Date selector
                date_options = data['date'].dt.strftime('%Y-%m-%d').unique()
                selected_date = st.selectbox("Select a date for analysis:", date_options)

                if selected_date:
                    text_for_analysis = get_dated_input(data, selected_date)
                    with st.expander("Show/Hide Original Conversation"):
                        st.markdown(f"```\n{text_for_analysis}\n```", unsafe_allow_html=True)
                    process = st.button('Process')
                    if process:
                        # Perform analysis
                        sentiment = get_sentiment_analysis(text_for_analysis, tokenizer_sentiment, model_sentiment)
                        summary = generate_summary(text_for_analysis, tokenizer_summary, model_summary)
                        ner_results = get_NER(summary, pipe_ner)
    
                        # Display results
                        st.subheader("Sentiment Analysis")
                        st.write("Sentiment:", sentiment)
    
                        st.subheader("Summary")
                        st.write("Summary:", summary)
    
                        st.subheader("Named Entity Recognition")
                        ner_df = pd.DataFrame(ner_results, columns=["Word", "Entity Group"])
                        st.write(ner_df)
        else:
            st.error("Unsupported file type. Please upload a .txt or .zip file.")
    else:
        st.info("Please upload a file to proceed.")

if __name__ == "__main__":
    main()