Upload app.py

app.py

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+import streamlit as st
+import streamlit.components.v1 as com
+#import libraries
+from transformers import AutoModelForSequenceClassification,AutoTokenizer, AutoConfig
+import numpy as np
+#convert logits to probabilities
+from scipy.special import softmax
+
+
+#import the model
+tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
+
+model_path = f"penscola/news-d-bert"
+config = AutoConfig.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+#Set the page configs
+st.set_page_config(page_title='Fake News Detection',page_icon='😎',layout='wide')
+
+#welcome Animation
+st.markdown('<h1> Fake News Detection </h1>',unsafe_allow_html=True)
+
+#Create a form to take user inputs
+with st.form(key='tweet',clear_on_submit=True):
+    text=st.text_area('Copy and paste the news or type one')
+    submit=st.form_submit_button('submit')
+
+#create columns to show outputs
+col2,col3=st.columns(2)
+col2.title('Fake or Legit')
+col3.title('Confidence of this prediction')
+
+if submit:
+    print('submitted')
+    #pass text to preprocessor
+    def preprocess(text):
+    #initiate an empty list 
+        new_text = []
+        #split text by space
+        for t in text.split(" "):
+            #set username to @user
+            t = '@user' if t.startswith('@') and len(t) > 1 else t  
+            #set tweet source to http
+            t = 'http' if t.startswith('http') else t 
+            #store text in the list
+            new_text.append(t)
+            #change text from list back to string
+        return " ".join(new_text) 
+    
+
+    #pass text to model
+
+    #change label id 
+    config.id2label = {0: 'Fake', 1: 'Legit'}
+
+    text = preprocess(text)
+
+    # PyTorch-based models
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+
+    #Process scores
+    ranking = np.argsort(scores)
+    ranking = ranking[::-1]  
+    l = config.id2label[ranking[0]]
+    s = scores[ranking[0]]
+
+    #output
+    if l==1:
+        col2.write('Legit')
+        col3.write(f'{s}%')
+    else:
+        col2.write('Fake')
+        col3.write(f'{s}%')