import streamlit as st
import pandas as pd
import re
from openai import OpenAI
import concurrent.futures
import json
import os

def extract_and_parse_json_from_markdown(markdown_text: str) -> dict:
    code_block_pattern = r"``````"
    code_block_match = re.search(code_block_pattern, markdown_text)
    
    if code_block_match:
        json_str = code_block_match.group(1).strip()
    else:
        json_str = markdown_text.strip()
    
    try:
        return json.loads(json_str)
    except json.JSONDecodeError as e:
        raise ValueError(f"Invalid JSON format: {e}")

def process_event(event):
    openai = OpenAI(
        api_key=os.environ.get('DEEP_API_KEY'),
        base_url="https://api.deepinfra.com/v1/openai",
    )

    
    llm_prompt = f""" 
    You are a digital marketing campaign analyst designed to analyze and report digital marketing campaign data for Rod Wave concerts, Your job is to convert the given text into JSON

    {{
        "market": "str",
        "total_spend": "float",
        "impressions": "float",
        "clicks": "float",
        "metrics_cpc": "float",
        "metrics_cpm": "float",
        "metrics_ctr": "float",
        "metrics_cpa": "float",
        "platform_spend_meta_total": "float",
        "platform_spend_meta_instagram": "float",
        "platform_spend_meta_facebook": "float",
        "platform_spend_google_total": "float",
        "platform_spend_google_youtube": "float",
        "platform_spend_google_search_display": "float",
        "platform_spend_programmatic": "float",
        "revenue_average_ticket_price": "float",
        "revenue_total_revenue": "float",
        "revenue_roi": "float"
    }}

    Here is Text for it:
    {event}

    Return in only JSON adhering to Above Schema
    """
    
    chat_completion = openai.chat.completions.create(
        model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
        messages=[{"role": "user", "content": llm_prompt}],
    )
    return chat_completion.choices[0].message.content

def process_all_events(events):
    json_all = []
    progress_bar = st.progress(0)
    
    with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
        futures = [executor.submit(process_event, event) for event in events]
        
        for i, future in enumerate(concurrent.futures.as_completed(futures)):
            progress = (i + 1) / len(events)
            progress_bar.progress(progress)
            st.write(f"Processed event {i + 1}/{len(events)}")
            json_all.append(future.result())
    
    return json_all

def main():
    st.title("Rod Wave Concert Marketing Data Processor")
    st.write("Upload a text file containing concert marketing data to convert it to CSV format")
    
    uploaded_file = st.file_uploader("Choose a text file", type="txt")
    
    if uploaded_file is not None:
        text = uploaded_file.read().decode("utf-8")
        events = re.split(r'\n(?=Rod Wave Concert)', text)
        events = [event for event in events if event.strip()]
        
        st.write(f"Found {len(events)} events to process")
        
        if st.button("Process Data"):
            with st.spinner("Processing events..."):
                json_all = process_all_events(events)
                
                json_sanity = []
                for ele in json_all:
                    json_sanity.append(extract_and_parse_json_from_markdown(ele))
                
                df = pd.DataFrame(json_sanity)
                
                st.success("Processing complete!")
                st.write("Preview of processed data:")
                st.dataframe(df.head())
                
                csv = df.to_csv(index=False)
                st.download_button(
                    label="Download CSV",
                    data=csv,
                    file_name="processed_concert_data.csv",
                    mime="text/csv"
                )

if __name__ == "__main__":
    main()