update code

app.py

@@ -1,63 +1,30 @@
 import streamlit as st
-
-from transformers import PerceiverTokenizer, PerceiverForMaskedLM
 import transformers
+import matplotlib.pyplot as plt
+
 
 @st.cache(allow_output_mutation=True, show_spinner=False)
 def get_pipe():
-    model = PerceiverForMaskedLM.from_pretrained("deepmind/language-perceiver")
-    tokenizer = PerceiverTokenizer.from_pretrained("deepmind/language-perceiver")
+    model_name = "joeddav/distilbert-base-uncased-go-emotions-student"
+    model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name)
+    tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
     pipe = transformers.pipeline('text-classification', model=model, tokenizer=tokenizer,
                                  return_all_scores=True, truncation=True)
     return pipe
 
 
-
-@st.cache
-def load_labels():
-    return [
-        "admiration",
-        "amusement",
-        "anger",
-        "annoyance",
-        "approval",
-        "caring",
-        "confusion",
-        "curiosity",
-        "desire",
-        "disappointment",
-        "disapproval",
-        "disgust",
-        "embarrassment",
-        "excitement",
-        "fear",
-        "gratitude",
-        "grief",
-        "joy",
-        "love",
-        "nervousness",
-        "optimism",
-        "pride",
-        "realization",
-        "relief",
-        "remorse",
-        "sadness",
-        "surprise",
-        "neutral"
-    ]
-
 def sort_predictions(predictions):
     return sorted(predictions, key=lambda x: x['score'], reverse=True)
 
 
-st.set_page_config(page_title="Emotion Prediction")
-st.title("Emotion Prediction")
-st.write("Type text into the text box and then press 'Predict' to get the predicted emotion.")
+st.set_page_config(page_title="Affect scores")
+st.title("Affect scores")
+st.write("Type text into the text box and then press 'Compute' to generate affect scores.")
 
-default_text = "I really love using HuggingFace Spaces!"
+default_text = "It's about a startup taking on a big yet creative challenge, with ups and downs along the way."
 
 text = st.text_area('Enter text here:', value=default_text)
-submit = st.button('Predict')
+submit = st.button('Generate')
 
 with st.spinner("Loading model..."):
     pipe = get_pipe()
@@ -74,7 +41,7 @@ if (submit and len(text.strip()) > 0) or len(text.strip()) > 0:
     ax.tick_params(rotation=90)
     ax.set_ylim(0, 1)
 
-    st.header('Prediction:')
+    st.header('Result:')
     st.pyplot(fig)
 
     prediction = dict([(p['label'], p['score']) for p in prediction])