Create app.py

app.py

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+#importing the necessary libraries
+
+import pandas as pd
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from keybert import KeyBERT
+from keyphrase_vectorizers import KeyphraseCountVectorizer
+
+# Defining a function to read in the text file
+
+def read_in_text(url):
+  with open(url, 'r') as file:
+    article = file.read()
+    return article
+    
+tmp_model = SentenceTransformer('valurank/MiniLM-L6-Keyword-Extraction')
+kw_extractor = KeyBERT(tmp_model)
+
+def get_keybert_results_with_vectorizer(file, number_of_results=20):
+    try:
+        text = read_in_text(file.name)
+        keywords = kw_extractor.extract_keywords(text, vectorizer=KeyphraseCountVectorizer(), stop_words=None, top_n=number_of_results)
+        keywords = [i for i in keywords if i[1] >= 0.25]
+
+        keybert_diversity_phrases = []
+        for i, j in keywords:
+          keybert_diversity_phrases.append(i)
+
+        output_df = pd.DataFrame()
+        output_df['keyword'] = np.array(keybert_diversity_phrases)
+        return output_df.head(20)
+    except Exception:
+        return "Error"
+        
+demo = gr.Interface(get_keybert_results_with_vectorizer, inputs=gr.inputs.File(),
+                    outputs=gr.outputs.Dataframe(),
+                    title = "Keyword Extraction")
+                    
+if __name__ == "__main__":
+    demo.launch(debug=True)