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app.py

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+import gradio as gr
+
+import nltk
+import simplemma
+from nltk.tokenize import word_tokenize
+from nltk.tokenize import sent_tokenize
+from nltk.probability import FreqDist
+from simplemma import text_lemmatizer
+nltk.download('punkt')
+
+file = "text.txt"
+
+spacy_model = 'https://huggingface.co/spacy/it_core_news_sm'
+
+import spacy
+nlp_IT = spacy.load(spacy_model)
+
+def get_lists(file):
+  with open(file, 'r', encoding='utf-8') as f:
+    text = f.read()
+
+  word_tokenized_text = word_tokenize(text, language='italian')
+  word_tokenized_text_lower = [word.lower() for word in word_tokenized_text]
+
+  sent_tokenized_text = sent_tokenize(text, language='italian')
+  sent_tokenized_text_lower = [sent.lower() for sent in sent_tokenized_text]
+
+  return word_tokenized_text, word_tokenized_text_lower, sent_tokenized_text, sent_tokenized_text_lower
+
+#words, words_lower, sentences, sentences = get_lists(file)
+
+
+
+demo = gr.Interface(
+    sentence_builder,
+    [
+        gr.Textbox(),
+        gr.Radio(["park", "zoo", "road"]),
+        gr.CheckboxGroup(["ran", "swam", "ate", "slept"]),
+        gr.Checkbox(label="Is it the morning?"),
+    ],
+    "text",
+    examples=[
+        ["cats", "park", ["ran", "swam"], True],
+        ["dog", "zoo", ["ate", "swam"], False],
+        ["bird", "road", ["ran"], False],
+        ["cat", "zoo", ["ate"], True],
+    ],
+)
+
+demo.launch()