integration of the key-phrase extraction model with Gradio

app.py

@@ -1,5 +1,50 @@
 import os
-import gradio as gradio
+import gradio as gr
+
+from transformers import (
+    TokenClassificationPipeline,
+    AutoModelForTokenClassification,
+    AutoTokenizer,
+)
+from transformers.pipelines import AggregationStrategy
+import numpy as np
+
+class KeyphraseExtractionPipeline(TokenClassificationPipeline):
+    def __init__(self, model, *args, **kwargs):
+        super().__init__(
+            model=AutoModelForTokenClassification.from_pretrained(model),
+            tokenizer=AutoTokenizer.from_pretrained(model),
+            *args,
+            **kwargs
+        )
+
+    def postprocess(self, model_outputs):
+        results = super().postprocess(
+            model_outputs=model_outputs,
+            aggregation_strategy=AggregationStrategy.SIMPLE,
+        )
+        return np.unique([result.get("word").strip() for result in results])
+       
+# Load pipeline
+model_name = "ml6team/keyphrase-extraction-kbir-inspec"
+extractor = KeyphraseExtractionPipeline(model=model_name)
+
+# Inference
+def keyphrases_out(input):
+    input = input.replace("\n", " ")
+    keyphrases = extractor(input)
+    out = "The Key Phrases in your text are:\n\n"
+    for k in keyphrases:
+        out += k + "\n"
+    return out
+
+demo = gr.Interface(fn=keyphrases_out, inputs = "text", outputs = "text")
+
+demo.launch()
+
+
+
+
 
 
 

flagged/log.csv

@@ -0,0 +1,32 @@
+text,output,flag,username,timestamp
+"'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.",,,,2023-02-25 21:18:36.680367
+"'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.",,,,2023-02-25 21:18:38.087039
+,,,,2023-02-25 21:18:40.419138