import gradio as gr
import requests
import json
import os
 
from screenshot import (
    before_prompt,
    prompt_to_generation,
    after_generation,
    js_save,
    js_load_script,
)
from spaces_info import description, examples, initial_prompt_value
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, set_seed


#API_URL = os.getenv("API_URL")
#HF_API_TOKEN = os.getenv("HF_API_TOKEN")


def inference(input_sentence, max_length, sample_or_greedy, seed=42):
    #print("input_sentence", input_sentence)
    if sample_or_greedy == "Sample":
        parameters = {
            "max_new_tokens": max_length,
            "top_p": 0.9,
            "do_sample": True,
            #"seed": seed,
            "early_stopping": False,
            "length_penalty": 0.0,
            "eos_token_id": None,
        }
    else:
        parameters = {
            "max_new_tokens": max_length,
            "do_sample": False,
            #"seed": seed,
            "early_stopping": False,
            "length_penalty": 0.0,
            "eos_token_id": None,
        }

    payload = {"inputs": input_sentence, "parameters": parameters,"options" : {"use_cache": False} }
    model_name = 'bigscience/bloomz-560m'
    pipe = pipeline("text-generation", 
        model = model_name, 
        tokenizer = model_name, 
        max_new_tokens = max_length, 
        do_sample = False, 
        length_penalty = 0.0, 
        early_stopping = False, 
        eos_token_id = None
    )
    res = pipe(input_sentence)



    #data = query(payload)
    #if "error" in data:
    #    return (None, None, f"<span style='color:red'>ERROR: {data['error']} </span>")
    #generation = data[0]["generated_text"].split(input_sentence, 1)[1]

    generation  = res[0]["generated_text"].split(input_sentence, 1)[1]
    print(generation)

    return (
        before_prompt
        + input_sentence
        + prompt_to_generation
        + generation
        + after_generation,
        res[0]["generated_text"],
        "",
    )

    #return generation


if __name__ == "__main__":
    demo = gr.Blocks()
    with demo:
        with gr.Row():
            gr.Markdown(value=description)
        with gr.Row():
            with gr.Column():
                text = gr.Textbox(
                    label="Input",
                    value=" ",  # should be set to " " when plugged into a real API
                )
                tokens = gr.Slider(1, 64, value=32, step=1, label="Tokens to generate")
                sampling = gr.Radio(
                    ["Sample", "Greedy"], label="Sample or greedy", value="Sample"
                )
                '''
                sampling2 = gr.Radio(
                    ["Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5"],
                    value="Sample 1",
                    label="Sample other generations (only work in 'Sample' mode)",
                    type="index",
                )
                '''
                with gr.Row():
                    submit = gr.Button("Submit")
                    load_image = gr.Button("Generate Image")
            with gr.Column():
                text_error = gr.Markdown(label="Log information")
                text_out = gr.Textbox(label="Output")
                display_out = gr.HTML(label="Image")
                display_out.set_event_trigger(
                    "load",
                    fn=None,
                    inputs=None,
                    outputs=None,
                    no_target=True,
                    js=js_load_script,
                )
        with gr.Row():
            #gr.Examples(examples=examples, inputs=[text, tokens, sampling, sampling2])
            gr.Examples(examples=examples, inputs=[text, tokens, sampling])

        submit.click(
            inference,
            #inputs=[text, tokens, sampling, sampling2],
            inputs = [text, tokens, sampling],
            outputs=[display_out, text_out, text_error],
        )

        load_image.click(fn=None, inputs=None, outputs=None, _js=js_save)

    demo.launch()