Upload tool

app.py

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+from transformers import launch_gradio_demo
+from text_sentiment_analyzer import SentAnalClassifierTool
+
+launch_gradio_demo(SentAnalClassifierTool)

requirements.txt

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+torch
+transformers
+trainDistilBERT

text_sentiment_analyzer.py

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+"""
+A script for a text sentiment analysis tool for the 🤗 Transformers Agent library.
+"""
+
+from transformers import Tool
+from transformers.tools.base import get_default_device
+from transformers import pipeline
+from transformers import DistilBertTokenizerFast
+from trainDistilBERT import DistilBertForMulticlassSequenceClassification
+import torch
+
+
+
+class SentAnalClassifierTool(Tool):
+	"""
+	A tool for sentiment analysis
+	"""
+	ckpt = "ongknsro/ACARISBERT-DistilBERT"
+	name = "text_sentiment_analyzer"
+	description = (
+		"This is a tool that returns a sentiment label for a given text sequence. "
+		"It takes the raw text as input, and "
+		"returns a sentiment label as output."
+	)
+
+	inputs = ["text"]
+	outputs = ["text"]
+
+	def __init__(self, device=None, **hub_kwargs) -> None:
+		super().__init__()
+
+		self.device = device
+		self.pipeline = None
+		self.hub_kwargs = hub_kwargs
+
+	def setup(self):
+		if self.device is None:
+			self.device = get_default_device()
+
+		self.tokenizer = DistilBertTokenizerFast.from_pretrained(self.ckpt)
+
+		self.model = DistilBertForMulticlassSequenceClassification.from_pretrained(self.ckpt).to(self.device)
+
+		self.pipeline = pipeline("sentiment-analysis", model=self.model, tokenizer=self.tokenizer, top_k=None, device=0)
+
+		self.is_initialized = True
+
+	def __call__(self, task: str):
+		if not self.is_initialized:
+			self.setup()
+
+		outputs = self.pipeline(task)
+		labels = [item["label"] for item in outputs[0]]
+		logits = [item["score"] for item in outputs[0]]
+		probs = torch.softmax(torch.tensor(logits), dim=0)
+		label = labels[torch.argmax(probs).item()]
+
+		return label

tool_config.json

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+{
+  "description": "This is a tool that returns a sentiment label for a given text sequence. It takes the raw text as input, and returns a sentiment label as output.",
+  "name": "text_sentiment_analyzer",
+  "tool_class": "text_sentiment_analyzer.SentAnalClassifierTool"
+}