import csv
import datetime
import os
from typing import Optional
import gradio as gr

from onnx_export import convert
from huggingface_hub import HfApi, Repository


DATASET_REPO_URL = "https://huggingface.co/datasets/optimum/exporters"
DATA_FILENAME = "data.csv"
DATA_FILE = os.path.join("data", DATA_FILENAME)

HF_TOKEN = os.environ.get("HF_TOKEN")

repo: Optional[Repository] = None
if HF_TOKEN:
    repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL, token=HF_TOKEN)

def onnx_export(token: str, model_id: str, task: str) -> str:
    if token == "" or model_id == "":
        return """
        ### Invalid input 🐞

        Please fill a token and model_id.
        """
    try:
        api = HfApi(token=token)

        error, commit_info = convert(api=api, model_id=model_id, task=task)
        if error != "0":
            return error
        
        print("[commit_info]", commit_info)

        # save in a private dataset
        if repo is not None:
            repo.git_pull(rebase=True)
            with open(DATA_FILE, "a") as csvfile:
                writer = csv.DictWriter(
                    csvfile, fieldnames=["model_id", "pr_url", "time"]
                )
                writer.writerow(
                    {
                        "model_id": model_id,
                        "pr_url": commit_info.pr_url,
                        "time": str(datetime.now()),
                    }
                )
            commit_url = repo.push_to_hub()
            print("[dataset]", commit_url)

        return f"### Success 🔥 Yay! This model was successfully converted and a PR was open using your token, here: {commit_info.pr_url}]({commit_info.pr_url})"
    except Exception as e:
        return f"### Error: {e}"

DESCRIPTION = """
<p align="center">
    <img src="https://huggingface.co/spaces/optimum/exporters/resolve/main/clean_hf_onnx.png"/>
</p>

<p align="center">
    # Convert any PyTorch model to ONNX with 🤗 Optimum exporters 🏎️
</p>

This Space allows to automatically convert to ONNX 🤗 transformers models hosted on the Hugging Face Hub. It opens a PR on the target model, and it is up to the owner of the original model
to merge the PR to allow people to leverage the ONNX standard to share and use the model on a wide range of devices!

Once converted, the model can for example be used in the [🤗 Optimum](https://huggingface.co/docs/optimum/) library following closely the transormers API.
Check out [this guide](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models) to see how!

The steps are the following:
- Paste a read-access token from hf.co/settings/tokens. Read access is enough given that we will open a PR against the source repo.
- Input a model id from the Hub (for example:)
- If necessary, input the task for this model.
- Click "Convert to ONNX"
- That's it! You'll get feedback if it works or not, and if it worked, you'll get the URL of the opened PR!

Note: in case the model to convert is larger than 2 GB, it will be saved in a subfolder called `onnx/`. To load it from Optimum, the argument `subfolder="onnx"` should be provided.
"""

with gr.Blocks() as demo:
    gr.Markdown(DESCRIPTION)

    with gr.Row():
        input_token = gr.TextBox(max_lines=1, label="Hugging Face token"),
        input_model = gr.TextBox(max_lines=1, label="Model name", placeholder="textattack/distilbert-base-cased-CoLA"),
        input_task = gr.TextBox(value="auto", max_lines=1, label="Task (can be left blank, will be automatically inferred)")
        output = gr.Markdown(label="Output")
    
    btn = gr.Button("Convert to ONNX")
    btn.click(fn=onnx_export, inputs=[input_token, input_model, input_task], outputs=output)

"""
demo = gr.Interface(
    title="",
    description=DESCRIPTION,
    allow_flagging="never",
    article="Check out the [🤗 Optimum repoository on GitHub](https://github.com/huggingface/optimum) as well!",
    inputs=[
        gr.Text(max_lines=1, label="Hugging Face token"),
        gr.Text(max_lines=1, label="Model name", placeholder="textattack/distilbert-base-cased-CoLA"),
        gr.Text(value="auto", max_lines=1, label="Task (can be left blank, will be automatically inferred)")
    ],
    outputs=[gr.Markdown(label="output")],
    fn=onnx_export,
)
"""

demo.launch()