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YAT / app.py

Update app.py

1d34fd3 11 months ago

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	import requests
	import os

	#os.environ["hf_api_key"] = {hf_api_key}

	from fastapi import FastAPI

	app = FastAPI()

	class HuggingFaceAPI:
	def __init__(self, token):
	self.token = token

	def send_request(self, url, method, body):
	headers = {
	"Authorization": f"Bearer {self.token}",
	"Content-Type": "application/json"
	}

	if method == "GET":
	response = requests.get(url, headers=headers)
	elif method == "POST":
	response = requests.post(url, headers=headers, json=body)
	else:
	raise ValueError(f"Unsupported HTTP method: {method}")

	response.raise_for_status()
	return response.json()

	def text_translation(self, text, target_language):
	source_language = self.language_detection(text)
	url = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-"+source_language+"-"+target_language
	method = "POST"
	body = {
	"inputs": text
	}
	return self.send_request(url, method, body)


	def text_translation(self, text, source_language, target_language):
	#return ""
	url = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-"+source_language+"-"+target_language
	method = "POST"
	body = {
	"inputs": text
	}
	return self.send_request(url, method, body)

	def language_detection(self, text):
	url = "https://api-inference.huggingface.co/models/papluca/xlm-roberta-base-language-detection"
	method = "POST"
	body = {
	"inputs": text
	}
	return self.send_request(url, method, body)

	# ... existing API endpoints ...

	@app.post("/hf-inference/language_detection")
	async def language_detection_api(text: str):
	language_detection_response = api.language_detection(text)
	return language_detection_response

	@app.post("/hf-inference/text_translation")
	async def text_translation_api(text: str, source_language:str, target_language: str):
	text_translation_response = api.text_translation(text, source_language, target_language)
	return text_translation_response

	@app.post("/hf-inference/text_translation")
	async def text_translation_api(text: str, target_language: str):
	text_translation_response = api.text_translation(text, target_language)
	return text_translation_response

	### ENd of Fast API endpoints

	api = HuggingFaceAPI( os.environ.get("hf_api_key") )

	# Define the function to be called when inputs are provided
	def hf_inference_translate(prompt="Wie kann ich Ihnen helfen?", target_language="en"):
	print(prompt)
	# Call the respective API methods
	# Get the input language
	chat_response_languagedetected = ""
	chat_response_languagedetected = api.language_detection(prompt)
	print(chat_response_languagedetected[0][0]['label'])
	# Translate based on input prompt, detected language and chosen target language
	text_translation_response = api.text_translation(prompt, chat_response_languagedetected[0][0]['label'], target_language)
	print(text_translation_response)
	# Extract the labels and scores from the result
	label_scores = {entry['label']: entry['score'] for entry in chat_response_languagedetected[0][:3]}
	print(label_scores)
	# Return the API responses #
	return text_translation_response[0]['translation_text'],label_scores

	text = "Hallo, ich bin Christof. Wie geht es dir?"
	#text = "Меня зовут Вольфганг и я живу в Берлине"
	translation_response = hf_inference_translate(text, "en")
	print(translation_response)



	import gradio as gr
	import requests


	iface = gr.Interface(
	fn=hf_inference_translate,
	inputs=[
	gr.inputs.Textbox(label="Input", lines=5, placeholder="Enter text to translate"),
	gr.inputs.Dropdown(["en", "fr", "de", "es", "ru"], default="de", label="Select target language")
	],
	outputs=[
	gr.outputs.Textbox(label="Translated text"),
	gr.outputs.Label(label="Detected languages", num_top_classes=3)
	],
	title="🧐 Translation Interface",
	description="Type something in any language below and then click Run to see the output in the chosen target language.",
	examples=[["Wie geht es Dir?", "fr"], ["Do you need help?", "de"], ["J'ai besoin d'aide ?", "en"]],
	article="## Text Examples",
	article_description="Use examples",
	#live=True,
	debug=True,
	cache_examples=True
	)



	# Create a Gradio interface
	#queue
	iface.queue(concurrency_count=3)
	# Run the Gradio interface
	#iface.launch(share=True)
	iface.launch(debug=True)