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import requests
import gradio as gr
from transformers import pipeline
from transformers import Tool
class SentimentAnalysisTool(Tool):
name = "sentiment_analysis"
description = "This tool analyses the sentiment of a given text."
inputs = ["text"] # Adding an empty list for inputs
outputs = ["json"]
model_id_1 = "nlptown/bert-base-multilingual-uncased-sentiment"
model_id_2 = "microsoft/deberta-xlarge-mnli"
model_id_3 = "distilbert-base-uncased-finetuned-sst-2-english"
model_id_4 = "lordtt13/emo-mobilebert"
model_id_5 = "juliensimon/reviews-sentiment-analysis"
model_id_6 = "sbcBI/sentiment_analysis_model"
model_id_7 = "models/oliverguhr/german-sentiment-bert"
def __call__(self, text: str):
return self.predicto(text)
def parse_output(self, output_json):
list_pred = []
for i in range(len(output_json[0])):
label = output_json[0][i]['label']
score = output_json[0][i]['score']
list_pred.append((label, score))
return list_pred
def get_prediction(self, model_id):
classifier = pipeline("text-classification", model=model_id, return_all_scores=True)
return classifier
def predicto(self, review):
classifier = self.get_prediction(self.model_id_3)
prediction = classifier(review)
print(prediction)
return self.parse_output(prediction)
# Create an instance of the SentimentAnalysisTool class
sentiment_analysis_tool = SentimentAnalysisTool()
# Create the Gradio interface
#gr.Interface(fn=sentiment_analysis_tool, inputs=sentiment_analysis_tool.inputs, outputs=sentiment_analysis_tool.outputs).launch(share=True)
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