init

app.py

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+# !pip install gradio
+# !pip install -q keybert
+
+from keybert import KeyBERT
+import gradio as gr
+
+def greet(text, ngram_min, ngram_max, top_n, diversity, seed_keywords):
+  model_name = 'all-mpnet-base-v2'
+  model = KeyBERT(model_name)
+
+  if ngram_min > ngram_max:
+    return "ngram_min should no greater than ngram_max!"
+  else:
+    keywords = model.extract_keywords(text, keyphrase_ngram_range=(ngram_min, ngram_max), top_n=top_n, use_mmr=True,
+                                      diversity=diversity, seed_keywords=seed_keywords)
+    res = ""
+    for keyword in keywords:
+      res += keyword[0] + "\n"
+    return res
+
+
+demo = gr.Interface(
+    fn=greet,
+    inputs=[gr.Textbox(placeholder="Put the text here and click 'submit' to get the keyphrases", label="Input Text"), gr.Slider(1, 5, step = 1, label="Minimum number of words in a keyphrase"), gr.Slider(1, 5, step = 1, label="Maximum number of words in a keyphrase"),
+            gr.Slider(1, 10, step = 1, label="Number of returned keyphrases"), gr.Slider(0, 1, step = 0.05, label="Diversity of the returned keyphrases"),
+            gr.Textbox(placeholder='This field can be empty', label="Seed Keyword:\nguide the extraction by steering the similarities towards it.")],
+    outputs=[gr.Textbox(label='Extracted Keyphrases')],
+)
+demo.launch(debug=True)