update app.py

app.py

@@ -78,61 +78,6 @@ else:
             score = result[0]["score"]
             st.write("The classification of the given text is " + label + " with a score of " + str(score))
    
-# main_class = [(23.93,0),(78.987,0)]
-# toxic_type = []
-
-# if model == "roberta-large-mnli":
-#     #1
-#     if st.button("Run Sentiment Analysis of Text"): 
-#         model_path = "roberta-large-mnli"
-#         sentiment_pipeline = pipeline(model=model_path)
-#         result = sentiment_pipeline(data)
-#         label = result[0]["label"]
-#         score = result[0]["score"]
-#         d = {'tweet':[model_path],'classification':[label],'score':[score]}
-#         dataframe = pd.DataFrame(data=d)
-#         st.table(dataframe)
-        #st.write("The classification of the given text is " + label + " with a score of " + str(score))
-
-
-# data = []
-# text = st.text_input("Enter text here:","Artificial Intelligence is useful")
-# data.append(text)
-# if model == "roberta-large-mnli":
-#     #1
-#     if st.button("Run Sentiment Analysis of Text"): 
-#         model_path = "roberta-large-mnli"
-#         sentiment_pipeline = pipeline(model=model_path)
-#         result = sentiment_pipeline(data)
-#         label = result[0]["label"]
-#         score = result[0]["score"]
-#         st.write("The classification of the given text is " + label + " with a score of " + str(score))
-# elif model == "twitter-XLM-roBERTa-base":
-#     #2
-#     if st.button("Run Sentiment Analysis of Text"): 
-#         model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
-#         sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
-#         result = sentiment_task(text)
-#         label = result[0]["label"].capitalize()
-#         score = result[0]["score"]
-#         st.write("The classification of the given text is " + label + " with a score of " + str(score))
-
-# elif model == "bertweet-sentiment-analysis": 
-#     #3 
-#     if st.button("Run Sentiment Analysis of Text"): 
-#         analyzer = create_analyzer(task="sentiment", lang="en")
-#         result = analyzer.predict(text)
-#         if result.output == "POS": 
-#             label = "POSITIVE"
-#         elif result.output == "NEU": 
-#             label = "NEUTRAL"
-#         else: 
-#             label = "NEGATIVE"
-        
-#         neg = result.probas["NEG"]
-#         pos = result.probas["POS"]
-#         neu = result.probas["NEU"]
-#         st.write("The classification of the given text is " + label + " with the scores broken down as: Positive - " + str(pos) + ", Neutral - " + str(neu) + ", Negative - " + str(neg))