Create app.py

app.py

@@ -0,0 +1,38 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TextClassificationPipeline
+import operator
+import matplotlib.pyplot as plt
+import pandas as pd
+
+def get_sentiment(out):
+  d = dict()
+  for k in out:
+    print(k)
+    label = k['label']
+    score = k['score']
+    d[label] = score
+
+  winning_lab = max(d.items(), key=operator.itemgetter(1))[0]
+  winning_score = d[winning_lab]
+
+  df = pd.DataFrame.from_dict(d, orient = 'index')
+  return  df #winning_lab, winning_score
+  
+model_name =  "Anthos23/FS-distilroberta-fine-tuned"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
+text = st.text_area(f'Ciao! This app uses {model_name}.\nEnter your text  to test it ❤️')
+
+
+if text:
+  out = pipe(text)
+  df = get_sentiment(out[0])
+  fig, ax = plt.subplots()
+  c = ['#C34A36', '#FFC75F', '#008F7A']
+  ax.bar(df.index, df[0], color=c, width=0.4)
+  
+  st.pyplot(fig)
+  
+  #st.json(get_sentiment(out[0][0]))