Help with civitai model data fetch script

[razvanab]

Hello!
I am trying to use ChatGPT to create a Gradio app that can retrieve the data from Civitai for all the models I have acquired from Civitai, some of which I no longer use, and display them in a list. However, I am in need of assistance with what I currently have.

import gradio as gr
import os
import asyncio
from functools import lru_cache
import logging
import aiohttp

logging.basicConfig(level=logging.INFO)

@lru_cache(maxsize=128)
async def fetch_metadata(model_id):
    logging.info(f"Fetching metadata for model {model_id}")
    url = f"https://civitai.com/api/v1/models/{model_id}/lora-training-guide-steal-parameters-metadata-greater-hiperparametros-less"
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            if response.status == 200:
                try:
                    metadata = await response.json()
                    logging.info(f"Received metadata for model {model_id}")
                    return {
                        "name": metadata.get("name"),
                        "description": metadata.get("description"),
                        "thumbnail": metadata.get("thumbnail"),
                        "link": metadata.get("link"),
                    }
                except ValueError:
                    logging.error("Response is not in JSON format.")
                    return "Error: Response is not in JSON format."
            else:
                logging.error(f"Server returned status code {response.status}.")
                return f"Error: Server returned status code {response.status}."

async def fetch_metadata_for_folder(folder_path):
    logging.info(f"Fetching metadata for models in folder {folder_path}")
    metadata_list = []
    tasks = []
    for filename in os.listdir(folder_path):
        if filename.endswith(".safetensors"):
            model_id = filename.split('.')[0]  # assuming model ID is the filename without the extension
            task = asyncio.ensure_future(fetch_metadata(model_id))
            tasks.append(task)
    responses = await asyncio.gather(*tasks)
    for response in responses:
        if response:
            metadata_list.append(response)
    return metadata_list

def scan_and_fetch_metadata(folder_path):
    if not os.path.isdir(folder_path):
        logging.error("Provided path is not a valid directory.")
        return "Provided path is not a valid directory."
    logging.info(f"Scanning folder {folder_path}")
    metadata_list = asyncio.run(fetch_metadata_for_folder(folder_path))
    if metadata_list:
        logging.info("Successfully fetched metadata.")
        return "\n".join(metadata_list)
    else:
        logging.info("No valid model information found in the specified folder.")
        return "No valid model information found in the specified folder."

def civitai_metadata_app():
    model_input = gr.components.Textbox(placeholder="Enter Model Folder Path")
    scan_button = gr.components.Button("Scan and Fetch Metadata")
    output_list = gr.components.Textbox()
    
    def scan_and_fetch_metadata_callback(folder_path, _):
        metadata_list = scan_and_fetch_metadata(folder_path)
        return metadata_list
    
    iface = gr.Interface(
        fn=scan_and_fetch_metadata_callback,
        inputs=[model_input, scan_button],
        outputs=[output_list],
        title="Civitai Model Metadata Fetcher",
        description="Enter a model folder path to scan and fetch metadata from Civitai.",
        live=False,
    )
    return iface

if __name__ == "__main__":
    civitai_metadata_app().launch()