import json
import logging
import os
from datetime import datetime

import boto3
import gradio as gr
from dotenv import load_dotenv

from agents.accountability_agent import AccountabilityAgent
from agents.outline_agent import OutlineAgent
from agents.research_agent import ResearchAgent
from agents.synthesis_agent import SynthesisAgent
from agents.thinking_ladder_agent import ThinkingLadderAgent

logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

load_dotenv()


outline_agent = OutlineAgent()
research_agent = ResearchAgent()
thinking_ladder_agent = ThinkingLadderAgent()
synthesis_agent = SynthesisAgent()
accountability_agent = AccountabilityAgent()


def save_logs_and_upload_to_s3(logs, bucket_name):
    # Generate timestamp and filename
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"logs_{timestamp}.json"

    # Save logs to a JSON file
    with open(filename, "w") as f:
        json.dump(logs, f, indent=4)

    # Initialize a session using Amazon S3 with environment variables
    s3_client = boto3.client(
        "s3",
        aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
        aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
        region_name=os.getenv("AWS_DEFAULT_REGION"),
    )

    # Attempt to upload the file to S3
    try:
        s3_client.upload_file(filename, bucket_name, filename)
        print(f"File {filename} successfully uploaded to {bucket_name}")
        return True
    except Exception as e:
        print(f"Error uploading {filename} to S3: {e}")
        return False


def main(query: str) -> tuple[str, str, float]:
    logs = []
    log_entry = {}

    logging.info(f"Received query: {query}")
    log_entry["query"] = query

    main_title, themes, metrics_a = outline_agent.run(query)
    logging.info(f"OutlineAgent output - Main Title: {main_title}, Themes: {themes}, Metrics: {metrics_a}")
    log_entry["outline_agent"] = {"main_title": main_title, "themes": themes, "metrics": metrics_a}

    sources, metrics_b = research_agent.run(themes)
    logging.info(f"ResearchAgent output - Sources: {sources}, Metrics: {metrics_b}")
    log_entry["research_agent"] = {"sources": sources, "metrics": metrics_b}

    classified_sources, metrics_c = thinking_ladder_agent.run(themes, sources)
    logging.info(f"ThinkingLadderAgent output - Classified Sources: {classified_sources}, Metrics: {metrics_c}")
    log_entry["thinking_ladder_agent"] = {"classified_sources": classified_sources, "metrics": metrics_c}

    synthesis, metrics_d = synthesis_agent.run(main_title, themes, classified_sources)
    logging.info(f"SynthesisAgent output - Synthesis: {synthesis}, Metrics: {metrics_d}")
    log_entry["synthesis_agent"] = {"synthesis": synthesis, "metrics": metrics_d}

    accountability, metrics_e = accountability_agent.run(query, synthesis)
    logging.info(f"AccountabilityAgent output - Accountability: {accountability}, Metrics: {metrics_e}")
    log_entry["accountability_agent"] = {"accountability": accountability, "metrics": metrics_e}

    cost = sum(list(map(lambda metrics: metrics["cost"], [metrics_a, metrics_b, metrics_c, metrics_d, metrics_e])))
    logging.info(f"Total Cost: {cost}")
    log_entry["total_cost"] = cost

    logs.append(log_entry)
    save_logs_and_upload_to_s3(logs, "dediro-bakcup-1")

    return synthesis, accountability, cost


gr.Interface(
    fn=main,
    inputs=gr.Textbox(label="Query:"),
    outputs=[gr.Textbox(label="Generated Synthesis:"), gr.Textbox(label="Accountability:"), gr.Number(label="Cost ($):")],
).launch()