Added more

app.py

@@ -1,21 +1,32 @@
 import streamlit as st
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 
+def analyze(model_name, text):
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+    return classifier(text)
+
 st.title("Sentiment Analysis App - beta")
-st.header("This app is to analyze the sentiments behind a text. Currently it uses \
+st.write("This app is to analyze the sentiments behind a text. \n Currently it uses \
           pre-trained models without fine-tuning.")
 
+model_descrip = {
+    "distilbert-base-uncased-finetuned-sst-2-english": "This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2.\n \
+        Labels: POSITIVE; NEGATIVE ",
+    "cardiffnlp/twitter-roberta-base-sentiment": "This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark.\n \
+        Labels: 0 -> Negative; 1 -> Neutral; 2 -> Positive",
+    "finiteautomata/bertweet-base-sentiment-analysis": "Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets. \n \
+        Labels: POS; NEU; NEG"
+}
+
+
 user_input = st.text_input("Enter your text:", value="Missing Sophie.Z...")
 user_model = st.selectbox("Please select a model:", 
-                          ("distilbert-base-uncased-finetuned-sst-2-english",
-                           "cardiffnlp/twitter-roberta-base-sentiment", 
-                           "finiteautomata/bertweet-base-sentiment-analysis"))
+                          model_descrip)
 
-def analyze(model_name, text):
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
-    return classifier(text)
+st.write("### Model Description:")
+st.write(model_descrip[user_model])
 
 
 if st.button("Analyze"):
@@ -23,6 +34,7 @@ if st.button("Analyze"):
         st.write("Please enter a text.")
     else:
         with st.spinner("Hang on.... Analyzing..."):
-            st.write(analyze(user_model, user_input))
+            result = analyze(user_model, user_input)
+            st.write(f"Result: \nLabel: {result[0]['label']} Score: {result[0]['score']}")
 else:
     st.write("Go on! Try the app!")

milestone3.py

@@ -9,6 +9,7 @@ model = AutoModelForSequenceClassification.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+print(classifier.__class__)
 res = classifier(["I am very happy now.", "Not happy now."])
 
 for result in res: