first app

.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,40 @@
+import gradio as gr
+from kwextractor import KeyWordExtractor
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+kw_ex=KeyWordExtractor()
+
+
+def generate_kws(context,num_kw):
+    
+    context=context.strip()
+    if context:
+        try:
+            num_kw=int(num_kw)
+        except ValueError:
+            num_kw=None
+        return kw_ex.extract(context, num_kw) or ""
+        
+        return result
+    else:
+        raise gr.Error("Please enter text in inputbox!!!!")
+        
+
+
+inputs=gr.Textbox(value=article_value, lines=5, label="Input Context",elem_id="inp_div")
+nkws = gr.Textbox(label="Number of keywords to extract",elem_id="inp_div")
+outputs=gr.Textbox(label="Generated Keywords",lines=6,elem_id="inp_div")
+
+demo = gr.Interface(
+    generate_kws,
+    [inputs,nkws],
+    outputs,
+    title="Keyword Extraction Model",
+    css=".gradio-container {background-color: lightgray} #inp_div {background-color: #7FB3D5;}",
+    article="""<p style='text-align: center;'>Feel free to give us your <a href="https://www.pragnakalp.com/contact/" target="_blank">feedback</a> on this Keyword Extraction demo.</p>
+    """
+    
+)
+demo.launch()        

.ipynb_checkpoints/kwextractor-checkpoint.py

@@ -0,0 +1,38 @@
+import torch
+import csv, os, sys
+import argparse
+
+
+from keybert import KeyBERT
+from sentence_transformers import SentenceTransformer
+
+
+class KeyWordExtractor():
+
+    def __init__(self):
+
+        KWE_PRETRAINED = 'medmediani/Arabic-KW-Mdel/model'
+        self.SEQ_LENGTH = 512
+        self.MAX_KW_NGS=3
+        self.NKW=3
+
+        #self.device = torch.device('cpu')
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        
+        sentence_model = SentenceTransformer(KWE_PRETRAINED)
+        self.kw_model = KeyBERT(model=sentence_model)
+        
+        
+        self.kw_model.to(self.device)
+        
+    def extract(self, ctxt, nkws=None, max_kw_ngs=None):
+        nkws= nkws if nkws is not None else self.NKW
+        max_kw_ngs=max_kw_ngs if max_kw_ngs is not None else self.MAX_KW_NGS
+        
+        kw=self.kw_model.extract_keywords(ctxt, keyphrase_ngram_range=(1, max_kw_ngs),
+                                          top_n=nkws,
+                                     #use_maxsum=True,nr_candidates=20, top_n=5,
+                                     #use_mmr=True, diversity=0.1,
+                                     stop_words=None)
+        return ", ".join(w for w,_ in kw)
+    

app.py

@@ -1,7 +1,40 @@
 import gradio as gr
+from kwextractor import KeyWordExtractor
 
 def greet(name):
     return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+kw_ex=KeyWordExtractor()
+
+
+def generate_kws(context,num_kw):
+    
+    context=context.strip()
+    if context:
+        try:
+            num_kw=int(num_kw)
+        except ValueError:
+            num_kw=None
+        return kw_ex.extract(context, num_kw) or ""
+        
+        return result
+    else:
+        raise gr.Error("Please enter text in inputbox!!!!")
+        
+
+
+inputs=gr.Textbox(value=article_value, lines=5, label="Input Context",elem_id="inp_div")
+nkws = gr.Textbox(label="Number of keywords to extract",elem_id="inp_div")
+outputs=gr.Textbox(label="Generated Keywords",lines=6,elem_id="inp_div")
+
+demo = gr.Interface(
+    generate_kws,
+    [inputs,nkws],
+    outputs,
+    title="Keyword Extraction Model",
+    css=".gradio-container {background-color: lightgray} #inp_div {background-color: #7FB3D5;}",
+    article="""<p style='text-align: center;'>Feel free to give us your <a href="https://www.pragnakalp.com/contact/" target="_blank">feedback</a> on this Keyword Extraction demo.</p>
+    """
+    
+)
+demo.launch()        

kwextractor.py

@@ -0,0 +1,38 @@
+import torch
+import csv, os, sys
+import argparse
+
+
+from keybert import KeyBERT
+from sentence_transformers import SentenceTransformer
+
+
+class KeyWordExtractor():
+
+    def __init__(self):
+
+        KWE_PRETRAINED = 'medmediani/Arabic-KW-Mdel/model'
+        self.SEQ_LENGTH = 512
+        self.MAX_KW_NGS=3
+        self.NKW=3
+
+        #self.device = torch.device('cpu')
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        
+        sentence_model = SentenceTransformer(KWE_PRETRAINED)
+        self.kw_model = KeyBERT(model=sentence_model)
+        
+        
+        self.kw_model.to(self.device)
+        
+    def extract(self, ctxt, nkws=None, max_kw_ngs=None):
+        nkws= nkws if nkws is not None else self.NKW
+        max_kw_ngs=max_kw_ngs if max_kw_ngs is not None else self.MAX_KW_NGS
+        
+        kw=self.kw_model.extract_keywords(ctxt, keyphrase_ngram_range=(1, max_kw_ngs),
+                                          top_n=nkws,
+                                     #use_maxsum=True,nr_candidates=20, top_n=5,
+                                     #use_mmr=True, diversity=0.1,
+                                     stop_words=None)
+        return ", ".join(w for w,_ in kw)
+    

requirements.txt

@@ -0,0 +1,4 @@
+gradio==3.28.1
+keybert==0.7.0
+sentence_transformers==2.2.2
+torch==1.12.1+cu113