Update app.py

app.py

@@ -42,76 +42,7 @@ if len(text_input) != 0:
 
     st.markdown(f"my input is : { result_0 }")
     # time.sleep(100)
-
-    def remove_punctuations(text):
-        translator = str.maketrans('', '', punctuations_list)
-        return text.translate(translator)
-    
-    def normalize_arabic(text):
-        text = re.sub("[إأآا]", "ا", text)
-        text = re.sub("ى", "ي", text)
-        text = re.sub("ة", "ه", text)
-        text = re.sub("گ", "ك", text)
-        return text
-        
-        
-    def remove_repeating_char(text):
-        return re.sub(r'(.)\1+', r'\1', text)  
-            
-    def processPost(text): 
-        
-        #Replace @username with empty string
-        text = re.sub('@[^\s]+', ' ', text)
-            
-        #Convert www.* or https?://* to " "
-        text = re.sub('((www\.[^\s]+)|(https?://[^\s]+))',' ',text)
-            
-        #Replace #word with word
-        text = re.sub(r'#([^\s]+)', r'\1', text)
-        
-        # remove punctuations
-        text= remove_punctuations(text)
-            
-        # normalize the text
-        text= normalize_arabic(text)
-            
-        # remove repeated letters
-        text=remove_repeating_char(text)
-            
-        return text  
-            
-        
-    df['text'] = df['text'].apply(lambda x: processPost(x))
-    inputt[0] = inputt[0].apply(lambda x: processPost(x))
-        
-    st.markdown(f"my input is : { inputt.iloc[0,0] }")
-    #input=input.apply(lambda x: processPost(x))
-        
-        
-    vectorizer = TfidfVectorizer()
-    vectors = vectorizer.fit_transform(df['text'])
-        
-    text_tfidf = pd.DataFrame(vectors.toarray())
-        
-    traninput = vectorizer.transform(inputt[0])
-    traninput = traninput.toarray()
-    cosine_sim = cosine_similarity(traninput,text_tfidf)
-    top = np.max(cosine_sim)
-        
-        
-    if top >= .85 :
-        prediction = 'الخبر صحيح'
-    elif (top < .85) and (top >= .6) :
-        prediction = 'الخبر مظلل '
-    elif top < .6 :
-        prediction = 'الخبر كاذب '
-        
-        
-    st.markdown(f"most similar news is: { df['text'].iloc[np.argmax(np.array(cosine_sim[0]))] }")
-    st.markdown(f"Source url : {df['link'].iloc[np.argmax(np.array(cosine_sim[0]))]}")
-    st.markdown(f"Credibility rate : { np.max(cosine_sim)}")
-    st.markdown(f"system prediction: { prediction}")
-    df.to_csv('Students.csv', sep ='\t')    
+     
     
     
 st.sidebar.markdown('مواقع اخباريه معتمده ')