spacy

app.py

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+import os
+
+import gradio as gr
+from spacy.lang.en import English
+from transformers import AutoTokenizer
+
+# download spacy model ---
+os.system('python -m spacy download en_core_web_sm')
+
+
+deberta_v3_tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v3-base")
+mistral_tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+en_tokenizer = English().tokenizer
+
+
+def tokenize_with_spacy(text, tokenizer=en_tokenizer):
+    tokenized_text = tokenizer(text)
+    tokens = [token.text for token in tokenized_text]
+    return tokens
+
+
+def tokenize_with_hf(text, tokenizer=deberta_v3_tokenizer):
+    tokenized_text = tokenizer.tokenize(text)
+    return tokenized_text
+
+
+def tokenize(text):
+    s = tokenize_with_spacy(text)
+    d = tokenize_with_hf(text)
+    m = tokenize_with_hf(text, tokenizer=mistral_tokenizer)
+    return s, d, m
+
+
+with gr.Blocks() as demo:
+    input_text = gr.Textbox(lines=2, placeholder="Input text...")
+    submit_btn = gr.Button(label="Submit")
+
+    spacy_display = gr.JSON(label="Spacy")
+    deb_display = gr.JSON(label="DeBERTa-V3")
+    mistral_display = gr.JSON(label="Mistral")
+
+    # callback ---
+    submit_btn.click(
+        fn=tokenize,
+        outputs=[spacy_display, deb_display, mistral_display],
+    )
+
+# launch app --------
+demo.launch()

requirements.txt

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+transformers
+spacy