Update sentiment_analysis.py

sentiment_analysis.py

@@ -8,12 +8,8 @@ class SentimentAnalysisTool(Tool):
     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 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"
@@ -22,25 +18,29 @@ class SentimentAnalysisTool(Tool):
     model_id_6 = "sbcBI/sentiment_analysis_model"
     model_id_7 = "models/oliverguhr/german-sentiment-bert"
     
-    def parse_output(output_json):  
-        list_pred=[]
+    def __call__(self, inputs: str): 
+        return self.predicto(inputs)
+        
+    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(model_id):
+    def get_prediction(self, model_id):
         classifier = pipeline("text-classification", model=model_id, return_all_scores=True)
-                 
-    def predicto(review):
-            classifier = get_prediction(model_id_3)
-            prediction = classifier(review)
-            print(prediction)
-            return parse_output(prediction)
-
+        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()