update

app.py

@@ -3,48 +3,53 @@ from transformers import pipeline
 from pysentimiento import create_analyzer
 
 #title
-st.title("Sentiment Analysis - Classify Sentiment of text")
+st.title("Toxic Tweets")
 
 model = st.selectbox("Which pretrained model would you like to use?",("roberta-large-mnli","twitter-XLM-roBERTa-base","bertweet-sentiment-analysis"))
 
-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"
+d = {'col1':[1,2],'col2':[3,4]}
+data = pd.DataFrame(data=d)
+st.table(data)
+
+
+# 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))
+#         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))