Create app.py

app.py

@@ -0,0 +1,82 @@
+import time
+import streamlit as st
+from annotated_text import annotated_text
+
+from flair.data import Sentence
+from flair.models import SequenceTagger
+
+checkpoints = [
+   "flair/pos-english",
+]
+
+colors = {'ADD': '#b9d9a6', 'AFX': '#eddc92', 'CC': '#95e9d7', 'CD': '#e797db', 'DT': '#9ff48b', 'EX': '#ed92b4', 'FW': '#decfa1', 'HYPH': '#ada7d7', 'IN': '#85fad8', 'JJ': '#8ba4f4', 'JJR': '#e7a498', 'JJS': '#e5c79a', 'LS': '#eb94b6', 'MD': '#e698ae', 'NFP': '#d9d1a6', 'NN': '#96e89f', 'NNP': '#e698c6', 'NNPS': '#ddbfa2', 'NNS': '#f788cd', 'PDT': '#f19c8d', 'POS': '#8ed5f0', 'PRP': '#c4d8a6', 'PRP$': '#e39bdc', 'RB': '#8df1e2', 'RBR': '#d7f787', 'RBS': '#f986f0', 'RP': '#878df8', 'SYM': '#83fe80', 'TO': '#a6d8c9', 'UH': '#d9a6cc', 'VB': '#a1deda', 'VBD': '#8fefe1', 'VBG': '#e3c79b', 'VBN': '#fb81fe', 'VBP': '#d5fe81', 'VBZ': '#8084ff', 'WDT': '#dd80fe', 'WP': '#9ce3e3', 'WP$': '#9fbddf', 'WRB': '#dea1b5', 'XX': '#93b8ec'}
+
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
+def get_model(model_name):
+  return SequenceTagger.load(model_name) # Load the model
+  
+def getPos(s: Sentence):
+  texts = []
+  labels = []
+  for t in s.tokens:
+    for label in t.annotation_layers.keys():
+      texts.append(t.text)
+      labels.append(t.get_labels(label)[0].value)          
+  return texts, labels
+  
+def getDictFromPOS(texts, labels):
+  return [{ "text": t, "label": l } for t, l in zip(texts, labels)]
+
+def getAnnotatedFromPOS(texts, labels):
+  return [(t,l,colors[l]) for t, l in zip(texts, labels)]
+
+def main():
+
+  st.title("🥖 French Part-Of-Speech Tagging")
+
+  checkpoint = st.selectbox("Choose model", checkpoints)
+  model = get_model(checkpoint)
+ 
+  default_text = "This is an example sentence."
+  input_text = st.text_area(
+    label="Original text",
+    value=default_text,
+  )
+
+  start = None
+  if st.button("🧠 Compute"):
+    start = time.time()
+    with st.spinner("Search for Part-Of-Speech Tags 🔍"):
+    
+
+            # Build Sentence
+            s = Sentence(input_text)
+	
+            # predict tags
+            model.predict(s)
+
+            try:
+	
+                texts, labels = getPos(s)
+	                
+                st.header("Labels:")
+                anns = getAnnotatedFromPOS(texts, labels)
+                annotated_text(*anns)
+
+                st.header("JSON:")
+                st.json(getDictFromPOS(texts, labels))
+
+            except Exception as e:
+                st.error("Some error occured!" + str(e))
+                st.stop()
+	
+    st.write("---")
+
+
+	
+    if start is not None:
+        st.text(f"prediction took {time.time() - start:.2f}s")
+	
+	
+if __name__ == "__main__":
+    main()