import gradio as gr
import re
import requests
import json
import os
from screenshot import BG_COMP, BOX_COMP, GENERATION_VAR, PROMPT_VAR, main
from pathlib import Path

title = "BLOOM"
description = """Gradio Demo for BLOOM. To use it, simply add your text, or click one of the examples to load them.
Tips:
- Do NOT talk to BLOOM as an entity, it's not a chatbot but a webpage/blog/article completion model.
- For the best results: MIMIC a few sentences of a webpage similar to the content you want to generate.
Start a paragraph as if YOU were writing a blog, webpage, math post, coding article and BLOOM will generate a coherent follow-up. Longer prompts usually give more interesting results.
Options:
- sampling: imaginative completions (may be not super accurate e.g. math/history)
- greedy: accurate completions (may be more boring or have repetitions)
"""

API_URL = "https://hfbloom.ngrok.io/generate"
HF_API_TOKEN = os.getenv("HF_API_TOKEN")

hf_writer = gr.HuggingFaceDatasetSaver(HF_API_TOKEN, "huggingface/bloom_internal_prompts", organization="huggingface")


examples = [
    ['A "whatpu" is a small, furry animal native to Tanzania. An example of a sentence that uses the word whatpu is: We were traveling in Africa and we saw these very cute whatpus. To do a "farduddle" means to jump up and down really fast. An example of a sentence that uses the word farduddle is:', 32, "Sample", False, "Sample 1"],
    ['A poem about the beauty of science by Alfred Edgar Brittle\nTitle: The Magic Craft\nIn the old times', 50, "Sample", False,  "Sample 1"],
    ['استخراج العدد العاملي في لغة بايثون:', 30, "Greedy", False, "Sample 1"],
    ["Pour déguster un ortolan, il faut tout d'abord", 32, "Sample", False, "Sample 1"],
    ['Traduce español de España a español de Argentina\nEl coche es rojo - el auto es rojo\nEl ordenador es nuevo - la computadora es nueva\nel boligrafo es negro -', 16, "Sample", False, "Sample 1"],
    ['Estos ejemplos quitan vocales de las palabras\nEjemplos:\nhola - hl\nmanzana - mnzn\npapas - pps\nalacran - lcrn\npapa -', 16, "Sample",False,  "Sample 1"],
    ["Question: If I put cheese into the fridge, will it melt?\nAnswer:", 32, "Sample", False, "Sample 1"],
    ["Math exercise - answers:\n34+10=44\n54+20=", 16, "Greedy", False,  "Sample 1"],
    ["Question: Where does the Greek Goddess Persephone spend half of the year when she is not with her mother?\nAnswer:", 24, "Greedy", False, "Sample 1"],
    ["spelling test answers.\nWhat are the letters in « language »?\nAnswer: l-a-n-g-u-a-g-e\nWhat are the letters in « Romanian »?\nAnswer:", 24, "Greedy", False, "Sample 1"],
]

def query(payload):
    print(payload)
    response = requests.request("POST", API_URL, json=payload)
    print(response)
    return json.loads(response.content.decode("utf-8"))
    
def inference(input_sentence, max_length, sample_or_greedy, raw_text=False, seed=42):
    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}

    data = query(
        payload
    )
    
    if raw_text:
        return None, data[0]['generated_text']
    
    width, height = 3246, 3246
    assets_path = "assets"
    font_mapping = {
        "latin characters (faster)": "DejaVuSans.ttf",
        "complete alphabet (slower)":"GoNotoCurrent.ttf"
    }
    working_dir = Path(__file__).parent.resolve()
    font_path = str(working_dir / font_mapping["complete alphabet (slower)"])
    img_save_path = str(working_dir / "output.jpeg")
    colors = {
        BG_COMP: "#000000",
        PROMPT_VAR: "#FFFFFF",
        GENERATION_VAR: "#FF57A0",
        BOX_COMP: "#120F25",
    }
    
    new_string = data[0]['generated_text'].split(input_sentence, 1)[1]

    _, img = main(
        input_sentence,
        new_string,
        width,
        height,
        assets_path=assets_path,
        font_path=font_path,
        colors=colors,
        frame_to_box_margin=200,
        text_to_text_box_margin=50,
        init_font_size=150,
        right_align=False,
    )
    return img, data[0]['generated_text']


gr.Interface(
    inference, 
    [
        gr.inputs.Textbox(label="Input"),
        gr.inputs.Slider(1, 64, default=32, step=1, label="Tokens to generate"),
        gr.inputs.Radio(["Sample", "Greedy"], label="Sample or greedy", default="Sample"),
        gr.Checkbox(label="Just output raw text"),
        gr.inputs.Radio(["Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5"], default="Sample 1", label="Sample other generations (only work in 'Sample' mode", type="index"),
    ], 
    ["image", "text"],
    examples=examples,
    # article=article,
    title=title,
    description=description,
    flagging_callback=hf_writer,
    allow_flagging=True,
).launch()