Upload app.py

app.py

@@ -0,0 +1,59 @@
+import os
+import sys
+import re
+
+from flair.models import SequenceTagger
+from flair.data import Sentence
+
+flair_ner_model_path = "flair_model"
+assert os.path.exists(flair_ner_model_path)
+loaded_model: SequenceTagger = SequenceTagger.load(os.path.join(flair_ner_model_path ,"best-model.pt"))
+
+def one_item_process(r, loaded_model):
+    #assert type(r) == type(pd.Series())
+    zh = r["question"]
+    zh = zh.replace(" ", "").strip()
+    sentence = Sentence(" ".join(list(zh)))
+    loaded_model.predict(sentence)
+    sentence_str = str(sentence)
+    ask_spans = re.findall(r'\["(.+?)"/ASK\]', sentence_str)
+    sentence = re.findall(r'Sentence: "(.+?)"', sentence_str)
+    if ask_spans:
+        ask_spans = ask_spans[0]
+    else:
+        ask_spans = ""
+    if sentence:
+        sentence = sentence[0]
+    else:
+        sentence = ""
+    ask_spans, sentence = map(lambda x: x.replace(" ", "").strip(), [ask_spans, sentence])
+    return ask_spans, sentence
+
+import gradio as gr
+
+example_sample = [
+    "宁波在哪个省份？",
+    "美国的通货是什么？",
+]
+
+def demo_func(question):
+    assert type(question) == type("")
+    ask_spans, sentence = one_item_process(
+        {"question": question},
+        loaded_model
+    )
+    return {
+        "Question words": ask_spans
+    }
+
+
+demo = gr.Interface(
+        fn=demo_func,
+        inputs="text",
+        outputs="json",
+        title=f"Chinese Question Words extractor 🐱 demonstration",
+        examples=example_sample if example_sample else None,
+        cache_examples = False
+    )
+
+demo.launch(server_name=None, server_port=None)