import os
import gradio as gr
import requests


examples = [
  ["Please answer to the following question. Who is going to be the next Ballon d'or?"],
  ["Q: Can Barack Obama have a conversation with George Washington? Give the rationale before answering."],
  ["Summarize the following text: Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and was rushed to the hospital. Since she was diagnosed with a brain injury, the doctor told Peter to stay besides her until she gets well. Therefore, Peter stayed with her at the hospital for 3 days without leaving."],
  ["Please answer the following question: What is the boiling point of water?"],
  ["Answer the following question by detailing your reasoning: Are Pokemons alive?"],
  ["Translate to German: How old are you?"],
  ["Generate a cooking recipe to make bolognese pasta:"],
  ["Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"],
  ["Premise:  At my age you will probably have learnt one lesson. Hypothesis:  It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?"],
  ["Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch and bought 6 more, how many apples do they have?"],
  ["""Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?
    A: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.
    Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?"""]
]

title = "Upword. Models Playground."

token = os.environ["token"]

urls = {
    'flan-t5': os.environ["url"],
    # 'flan-t5': "https://api-inference.huggingface.co/models/philschmid/flan-t5-xxl-sharded-fp16",
    'bart-large-cnn': "https://api-inference.huggingface.co/models/facebook/bart-large-cnn",
    # 'pegasus': "https://api-inference.huggingface.co/models/google/pegasus-xsum"
}


def inference(text, min_length, max_length):
    headers = {"Authorization": f"Bearer {token}"}
    payload = {
        "inputs": text,
        "parameters": {
            "min_length": min_length,
            "max_length": max_length,
            "do_sample": False
        }
    }
    responses = dict()
    for model, url in urls.items():
        responses[model] = requests.post(url, headers=headers, json=payload)

    output_flan = responses['flan-t5'].json()[0]['generated_text']
    output_bart = responses['bart-large-cnn'].json()[0]['summary_text']
    # output_gpt = responses['pegasus'].json()[0]['summary_text']
    return [output_flan, output_bart]


io = gr.Interface(
  inference,
  inputs=[
      gr.Textbox(label='Input', lines=3),
      gr.Slider(minimum=1, maximum=160, value=20, label="min_length"),
      gr.Slider(minimum=1, maximum=160, value=80, label="max_length")
  ],
  outputs=[
    gr.Textbox(lines=3, label="Flan T5-XXL"),
    gr.Textbox(lines=3, label="BART-Large-CNN"),
    gr.Textbox(lines=3, label="Pegasus")
  ],
  title=title,
  examples=examples
)
io.launch()