testing pipelien

app.py

@@ -1,4 +1,48 @@
 import streamlit as st
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+from transformers import PerceiverTokenizer, PerceiverForMaskedLM
+
+
+@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")
+    pipe = transformers.pipeline('text-classification', model=model, tokenizer=tokenizer,
+                                 return_all_scores=True, truncation=True)
+    return pipe
+
+
+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.")
+
+default_text = "I really love using HuggingFace Spaces!"
+
+text = st.text_area('Enter text here:', value=default_text)
+submit = st.button('Predict')
+
+with st.spinner("Loading model..."):
+    pipe = get_pipe()
+
+if (submit and len(text.strip()) > 0) or len(text.strip()) > 0:
+
+    prediction = pipe(text)[0]
+    prediction = sort_predictions(prediction)
+
+    fig, ax = plt.subplots()
+    ax.bar(x=[i for i, _ in enumerate(prediction)],
+           height=[p['score'] for p in prediction],
+           tick_label=[p['label'] for p in prediction])
+    ax.tick_params(rotation=90)
+    ax.set_ylim(0, 1)
+
+    st.header('Prediction:')
+    st.pyplot(fig)
+
+    prediction = dict([(p['label'], p['score']) for p in prediction])
+    st.header('Raw values:')
+    st.json(prediction)