Create app.py

app.py

@@ -0,0 +1,34 @@
+from hazm import *
+import gradio as gr
+from sklearn.decomposition import LatentDirichletAllocation
+from sklearn.feature_extraction.text import CountVectorizer
+lda = LatentDirichletAllocation(n_components=4,random_state=101)
+normalizer=Normalizer()
+lemmatizer=Lemmatizer()
+stemmer=Stemmer()
+vectorzer=CountVectorizer(analyzer='word', ngram_range=(1,1))
+def compute_seo_score(normalized_text,keywords):
+  tokens=sent_tokenize(normalized_text)
+  x=vectorzer.fit_transform([normalized_text])
+  features=lda.fit(x)
+  key_words=[vectorzer.get_feature_names_out()[index] for index in features.components_.argsort()[-10:]]
+  query_terms=keywords.split('-')
+  score=0
+  for i in range(len(key_words)):
+    for query in query_terms:
+      keyterms=key_words[i]
+      if query in [lemmatizer.lemmatize(word) for word in key_words[i]]:
+        score+=1
+  final_score=score/4
+  return {'Estimated_number':score/100,
+          'score':final_score/100}
+def Normalize_text(text,keywords):
+  normalized_text=normalizer.normalize(text)
+  label=compute_seo_score(normalized_text,keywords)
+  return normalized_text,label
+demo = gr.Interface(
+    fn=Normalize_text,
+    inputs=["text","text"],
+    outputs=["text","label"],
+)
+demo.launch()