Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Gradio Text Summary App
-emoji: 📊
-colorFrom: indigo
-colorTo: purple
+title: Gradio_Text_Summary_App
+app_file: gradio_exit.py
 sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
+sdk_version: 3.38.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

gradio_exit.py

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+#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()
+
+
+

requirements.txt

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+gradio==3.43.2
+ipython==8.14.0
+Pillow==9.5.0
+Pillow==10.0.0
+python-dotenv==1.0.0
+Requests==2.31.0