Update sentiment_analysis.py

sentiment_analysis.py

@@ -1,4 +1,6 @@
 import requests
+import gradio as gr
+from transformers import pipeline
 
 class SentimentAnalysisTool:
     name = "sentiment_analysis"
@@ -8,33 +10,34 @@ class SentimentAnalysisTool:
 
     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 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)
-    
     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)
+