gradio_exit.py

#https://stackoverflow.com/questions/75286784/how-do-i-gracefully-close-terminate-gradio-from-within-gradio-blocks
#https://levelup.gitconnected.com/bringing-your-ai-models-to-life-an-introduction-to-gradio-ae051ca83edf
#Bringing Your AI Models to Life: An Introduction to Gradio - How to demo your ML model quickly without any front-end hassle.
#pipreqs . --encoding=utf-8

#https://huggingface.co/blog/inference-endpoints
#Getting Started with Hugging Face Inference Endpoints

#https://www.gradio.app/guides/using-hugging-face-integrations
Using Hugging Face Integrations

#在app.py的根目录下cmd命令行窗口中运行：gradio deploy，将会出现如下提示：
#To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .
#Token can be pasted using 'Right-Click'.
#Token:先将HF_API_TOKEN拷贝到剪贴板之后，邮件单击即可
#Add token as git credential? (Y/n) n
#Token is valid (permission: write).
#Your token has been saved to C:\Users\lenovo\.cache\huggingface\token
#Login successful
#Creating new Spaces Repo in 'D:\ChatGPTApps\Gradio_HF_Apps'. Collecting metadata, press Enter to accept default value.
#Enter Spaces app title [Gradio_HF_Apps]:


import os
import io
import requests
import json
from IPython.display import Image, display, HTML
from PIL import Image
import base64 
from dotenv import load_dotenv, find_dotenv

_ = load_dotenv(find_dotenv()) # read local .env file
hf_api_key ="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"

#model_id = "sentence-transformers/all-MiniLM-L6-v2"
model_id = "shleifer/distilbart-cnn-12-6"
#hf_token = "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"

#ENDPOINT_URL='https://api-inference.huggingface.co/models/DunnBC22/flan-t5-base-text_summarization_data'
api_url ='https://api-inference.huggingface.co/models/DunnBC22/flan-t5-base-text_summarization_data'
#api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
#pipeline的api-inference/Endpoint是对应于transformers（用于生成向量Embeddings）的！
#headers = {"Authorization": f"Bearer {hf_token}"}
headers = {"Authorization": f"Bearer {hf_api_key}"}

#def query(texts):
#    response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
#    return response.json()

#可以不使用ENDPOINT_URL，而是使用api_url（或者两个是一回事？）：https://huggingface.co/blog/getting-started-with-embeddings
def get_completion(inputs, parameters=None, ENDPOINT_URL=api_url): 
    headers = {
      "Authorization": f"Bearer {hf_api_key}",
      "Content-Type": "application/json"
    }
    data = { "inputs": inputs }
    if parameters is not None:
        data.update({"parameters": parameters})
    response = requests.request("POST",
                                ENDPOINT_URL, headers=headers,
                                data=json.dumps(data)
                               )
    return json.loads(response.content.decode("utf-8"))

text = (
    '''Alexander III of Macedon (Ancient Greek: Ἀλέξανδρος, romanized: Alexandros; 20/21 July 356 BC – 10/11 June 323 BC), '''
    '''commonly known as Alexander the Great,[a] was a king of the ancient Greek kingdom of Macedon.[a] '''
    '''He succeeded his father Philip II to the throne in 336 BC at the age of 20, '''
    '''and spent most of his ruling years conducting a lengthy military campaign throughout Western Asia and Egypt. '''
    '''By the age of 30, he had created one of the largest empires in history, stretching from Greece to northwestern India.[2] '''
    '''He was undefeated in battle and is widely considered to be one of history's greatest and most successful military commanders.[3][4]'''
)

get_completion(text)   #api_url中的model_id = "sentence-transformers/all-MiniLM-L6-v2"时，返回的结果是浮点数（即向量Embeddings）

import gradio as gr

def summarize(input):
    output = get_completion(input)
    return output[0]['summary_text']

gr.close_all()

demo = gr.Interface(fn=summarize, 
                    inputs=[gr.Textbox(label="Text to summarize", lines=6)],
                    outputs=[gr.Textbox(label="Result", lines=3)],
                    title="Text summarization with distilbart-cnn",
                    description="Summarize any text using the `shleifer/distilbart-cnn-12-6` model under the hood!"
                   )

#demo.launch(share=True, server_port=int(os.environ['PORT2']))
#demo.launch(share=True)
demo.launch()