import json
import os
import argparse
from tqdm import tqdm
import tiktoken
from openai import OpenAI
from huggingface_hub import hf_hub_download

def gpt_4o(input_text):
    client=OpenAI(api_key=os.environ.get("OAI"))
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "user", "content": [{"type": "text", "text": input_text}]}
        ],
        response_format={"type": "json_object"},
        temperature=0,
        max_tokens=4096,
        top_p=0,
        frequency_penalty=0,
        presence_penalty=0
    )
    return response.choices[0].message.content

def run_gpt4_event_extraction(data_dir, max_tokens=100000):

    all_info_path = os.path.join(data_dir, "all_info_with_txt.json")
    output_dir = os.path.join(data_dir, "gpt4_event_extraction")
    os.makedirs(output_dir, exist_ok=True)
    icl_path = hf_hub_download(
                repo_id="PledgeTracker/demo_feedback",   
                filename="icl.txt",            
                repo_type="dataset",                     
                token=os.environ["HF_TOKEN"]            
            )
    ICL = open(icl_path, "r").read()
    all_info = open(all_info_path, "r").readlines()

    enc = tiktoken.encoding_for_model("gpt-4o")

    for i, line in enumerate(all_info):
        ID = i
        urls = []
        results = []

        data = json.loads(line)
        docs = data["evidence"]
        claim = data["claim"]

        output_path = os.path.join(output_dir, f"gpt4o_results_{ID}_claim.json")
        if os.path.exists(output_path):
            print(f"Already exist: {output_path}")

        else:

            for doc in tqdm(docs):
                if doc["url"] in urls:
                    continue

                text = " ".join(doc["text"])
                input_text = (
                    f"{ICL}\nNow please only summarize events that are useful for verifying the pledge '{claim}', and their dates in the JSON format.\n\nInput:\n\nTitle: {doc['metadata']['title']}\n"
                    f"Date: {doc['metadata']['date']}\nArticle: {text}\nPledge: {claim}\n\n"
                    f"Output:\n"
                )

                urls.append(doc["url"])
                text_tokens = enc.encode(input_text)
                if len(text_tokens) > max_tokens:
                    input_text = enc.decode(text_tokens[:max_tokens])

                try:
                    output = gpt_4o(input_text)
                    # print(f"GPT-4o Response: {output}")
                    results.append({
                        "url": doc["url"],
                        "title": doc["metadata"]["title"],
                        "date": doc["metadata"]["date"],
                        "article": text,
                        "output": json.loads(output)
                    })
                except Exception as e:
                    print(f"Error processing doc: {e}")
                    continue

            
            with open(output_path, "w", encoding="utf-8") as f:
                json.dump(results, f, ensure_ascii=False, indent=4)

    return output_path

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Run GPT-4o event extraction")
    parser.add_argument("--data_dir", type=str, required=True, help="Root data directory")
    parser.add_argument("--icl_path", type=str, required=True, help="Path to ICL prompt file")
    parser.add_argument("--max_tokens", type=int, default=100000, help="Maximum token limit for input")

    args = parser.parse_args()

    run_gpt4_event_extraction(
        base_dir=args.base_dir,
        icl_path=args.icl_path,
        max_tokens=args.max_tokens
    )