debug

app.py

@@ -1,8 +1,6 @@
 import os
 import gradio as gr
 import wikipediaapi as wk
-
-
 from transformers import (
     TokenClassificationPipeline,
     AutoModelForTokenClassification,
@@ -11,6 +9,7 @@ from transformers import (
 from transformers.pipelines import AggregationStrategy
 import numpy as np
 
+# =====[ DEFINE PIPELINE ]===== #
 class KeyphraseExtractionPipeline(TokenClassificationPipeline):
     def __init__(self, model, *args, **kwargs):
         super().__init__(
@@ -26,13 +25,35 @@ class KeyphraseExtractionPipeline(TokenClassificationPipeline):
             aggregation_strategy=AggregationStrategy.SIMPLE,
         )
         return np.unique([result.get("word").strip() for result in results])
-       
-# Load pipeline
+
+# =====[ LOAD PIPELINE ]===== #
 model_name = "ml6team/keyphrase-extraction-kbir-inspec"
 extractor = KeyphraseExtractionPipeline(model=model_name)
 
+text = """
+Keyphrase extraction is a technique in text analysis where you extract the
+important keyphrases from a document. Thanks to these keyphrases humans can
+understand the content of a text very quickly and easily without reading it
+completely. Keyphrase extraction was first done primarily by human annotators,
+who read the text in detail and then wrote down the most important keyphrases.
+The disadvantage is that if you work with a lot of documents, this process
+can take a lot of time. 
+
+Here is where Artificial Intelligence comes in. Currently, classical machine
+learning methods, that use statistical and linguistic features, are widely used
+for the extraction process. Now with deep learning, it is possible to capture
+the semantic meaning of a text even better than these classical methods.
+Classical methods look at the frequency, occurrence and order of words
+in the text, whereas these neural approaches can capture long-term
+semantic dependencies and context of words in a text.
+""".replace("\n", " ")
+
+keyphrases = extractor(text)
+
+print(keyphrases)
 
-def keyphrases_out(input): #Not used but might be useful
+
+def keyphrases_out(input):
     input = input.replace("\n", " ")
     keyphrases = extractor(input)
     out = "The Key Phrases in your text are:\n\n"
@@ -44,16 +65,22 @@ def wikipedia_search(input):
     input = input.replace("\n", " ")
     keyphrases = extractor(input)
     wiki = wk.Wikipedia('en')
-    for k in keyphrases:
-        page = wiki.page(k)
-        if page.exists():
-            break
+
+    page = wiki.page("")
     return page.summary
 
 
-demo = gr.Interface(fn=wikipedia_search, inputs = "text", outputs = "text")
 
-demo.launch()
+
+    # for k in keyphrases:
+    #     page = wiki.page(k)
+    #     if page.exists():
+    #         break
+    # return page.summary
+
+# =====[ DEFINE INTERFACE ]===== #'
+# demo = gr.Interface(fn=wikipedia_search, inputs = "text", outputs = "text")
+# demo.launch(share=True)