update app.py

app.py

@@ -1,18 +1,39 @@
 import streamlit as st  #Web App
 from transformers import pipeline
+from pysentimiento import create_analyzer
+
+
+
+model = st.selectbox("Which pretrained model would you like to use?",("DistilBERT","twitter-XLM-roBERTa-base","bertweet-sentiment-analysis"))
 
 #title
 st.title("Sentiment Analysis - Classify Sentiment of text")
 
 data = []
 text = st.text_input("Enter text here:","Artificial Intelligence is useful")
-if st.button("Run Sentiment Analysis of Text"): 
-    data.append(text)
-    sentiment_pipeline = pipeline("sentiment-analysis")
-    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))
+data.append(text)
+if model == "DistilBERT":
+    #1
+    if st.button("Run Sentiment Analysis of Text"): 
+        model_path = "distilbert-base-uncased-finetuned-sst-2-english"
+        sentiment_pipeline = pipeline("sentiment-analysis",model=model_path, tokenizer=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-roBERTa-base":
+    #2
+    model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
+    sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
+    result = sentiment_task(text)
+    st.write(result)
+
+elif model == "bertweet-sentiment-analysis": 
+    #3 
+    analyzer = create_analyzer(task="sentiment", lang="en")
+    result = analyzer.predict(text)
+    st.write(result)
+