import start
import gradio as gr
import pandas as pd
from glob import glob
from pathlib import Path
from tabs.dashboard import df
from tabs.faq import (
    about_olas_predict_benchmark,
    about_olas_predict,
    about_the_dataset,
    about_the_tools,
)
from tabs.howto_benchmark import how_to_run

# Feature temporarily disabled til HF support helps us with the Space Error
# from tabs.run_benchmark import run_benchmark_main


demo = gr.Blocks()


# def run_benchmark_gradio(
#     tool_name,
#     model_name,
#     num_questions,
#     openai_api_key,
#     anthropic_api_key,
#     openrouter_api_key,
# ):
#     """Run the benchmark using inputs."""
#     if tool_name is None:
#         return "Please enter the name of your tool."
#     if (
#         openai_api_key is None
#         and anthropic_api_key is None
#         and openrouter_api_key is None
#     ):
#         return "Please enter either OpenAI or Anthropic or OpenRouter API key."

#     result = run_benchmark_main(
#         tool_name,
#         model_name,
#         num_questions,
#         openai_api_key,
#         anthropic_api_key,
#         openrouter_api_key,
#     )

#     if result == "completed":
#         # get the results file in the results directory
#         fns = glob("results/*.csv")

#         print(f"Number of files in results directory: {len(fns)}")

#         # convert to Path
#         files = [Path(file) for file in fns]

#         # get results and summary files
#         results_files = [file for file in files if "results" in file.name]

#         # the other file is the summary file
#         summary_files = [file for file in files if "summary" in file.name]

#         print(results_files, summary_files)

#         # get the path with results
#         results_df = pd.read_csv(results_files[0])
#         summary_df = pd.read_csv(summary_files[0])

#         # make sure all df float values are rounded to 4 decimal places
#         results_df = results_df.round(4)
#         summary_df = summary_df.round(4)

#         return gr.Dataframe(value=results_df), gr.Dataframe(value=summary_df)

#     return gr.Textbox(
#         label="Benchmark Result", value=result, interactive=False
#     ), gr.Textbox(label="Summary", value="")


with demo:
    gr.HTML("<h1>Olas Predict Benchmark</hjson>")
    gr.Markdown(
        "Leaderboard showing the performance of Olas Predict tools on the Autocast dataset and overview of the project."
    )

    with gr.Tabs() as tabs:
        # first tab - leaderboard
        with gr.TabItem("🏅 Benchmark Leaderboard", id=0):

            gr.components.Dataframe(
                value=df,
            )

        # second tab - about
        with gr.TabItem("ℹ️ About"):
            with gr.Row():
                with gr.Accordion("About the Benchmark", open=False):
                    gr.Markdown(about_olas_predict_benchmark)
            with gr.Row():
                with gr.Accordion("About the Tools", open=False):
                    gr.Markdown(about_the_tools)
            with gr.Row():
                with gr.Accordion("About the Autocast Dataset", open=False):
                    gr.Markdown(about_the_dataset)
            with gr.Row():
                with gr.Accordion("About Olas", open=False):
                    gr.Markdown(about_olas_predict)

        # third tab - how to run the benchmark
        with gr.TabItem("🚀 Contribute"):
            gr.Markdown(how_to_run)

        # fourth tab - run the benchmark
        # with gr.TabItem("🔥 Run the Benchmark"):
        #     with gr.Row():
        #         tool_name = gr.Dropdown(
        #             [
        #                 "prediction-offline",
        #                 "prediction-online",
        #                 # "prediction-online-summarized-info",
        #                 # "prediction-offline-sme",
        #                 # "prediction-online-sme",
        #                 "prediction-request-rag",
        #                 "prediction-request-reasoning",
        #                 # "prediction-url-cot-claude",
        #                 # "prediction-request-rag-cohere",
        #                 # "prediction-with-research-conservative",
        #                 # "prediction-with-research-bold",
        #             ],
        #             label="Tool Name",
        #             info="Choose the tool to run",
        #         )
        #         model_name = gr.Dropdown(
        #             [
        #                 "gpt-3.5-turbo-0125",
        #                 "gpt-4-0125-preview",
        #                 "claude-3-haiku-20240307",
        #                 "claude-3-sonnet-20240229",
        #                 "claude-3-opus-20240229",
        #                 "databricks/dbrx-instruct:nitro",
        #                 "nousresearch/nous-hermes-2-mixtral-8x7b-sft",
        #                 # "cohere/command-r-plus",
        #             ],
        #             label="Model Name",
        #             info="Choose the model to use",
        #         )
        #     with gr.Row():
        #         openai_api_key = gr.Textbox(
        #             label="OpenAI API Key",
        #             placeholder="Enter your OpenAI API key here",
        #             type="password",
        #         )
        #         anthropic_api_key = gr.Textbox(
        #             label="Anthropic API Key",
        #             placeholder="Enter your Anthropic API key here",
        #             type="password",
        #         )
        #         openrouter_api_key = gr.Textbox(
        #             label="OpenRouter API Key",
        #             placeholder="Enter your OpenRouter API key here",
        #             type="password",
        #         )
        #     with gr.Row():
        #         num_questions = gr.Slider(
        #             minimum=1,
        #             maximum=340,
        #             value=10,
        #             label="Number of questions to run the benchmark on",
        #         )
        #     with gr.Row():
        #         run_button = gr.Button("Run Benchmark")
        #     with gr.Row():
        #         with gr.Accordion("Results", open=True):
        #             result = gr.Dataframe()
        #     with gr.Row():
        #         with gr.Accordion("Summary", open=False):
        #             summary = gr.Dataframe()

        #     run_button.click(
        #         run_benchmark_gradio,
        #         inputs=[
        #             tool_name,
        #             model_name,
        #             num_questions,
        #             openai_api_key,
        #             anthropic_api_key,
        #             openrouter_api_key,
        #         ],
        #         outputs=[result, summary],
        #     )


demo.queue(default_concurrency_limit=40).launch()