sentiment_analysis.py

import requests
import gradio as gr
from transformers import pipeline

class SentimentAnalysisTool:
    name = "sentiment_analysis"
    description = "This tool analyses the sentiment of a given text input."

    inputs = ["text"]  # Adding an empty list for inputs

    outputs = ["json"]
    
    def __call__(self, inputs: str): 
        return SentimentAnalysisTool.predicto(str)
        
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 parse_output(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(model_id):
    classifier = pipeline("text-classification", model=model_id, return_all_scores=True)
             
def predicto(review):
        classifier = SentimentAnalysisTool.get_prediction(SentimentAnalysisTool.model_id_7)
        prediction = classifier(review)
        print(prediction)
        return SentimentAnalysisTool.parse_output(prediction)