Delete sentiment_analysis.py

sentiment_analysis.py

@@ -1,46 +0,0 @@
-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)