Update app.py

app.py

@@ -12,13 +12,13 @@ import streamlit as st
 import requests
 import torch
 
-st.markdown("## Prediction of Fakeness by Given URL")
+st.markdown("## Prediction of Misinformation by given URL")
 background = Image.open('logo.jpg')
 st.image(background)
 
 st.markdown(f"### Article URL")
 text = st.text_area("Insert some url here", 
-        value="https://en.globes.co.il/en/article-yandex-looks-to-expand-activities-in-israel-1001406519")
+        value="https://www.livelaw.in/news-updates/supreme-court-collegium-recommends-appointment-advocate-praveen-kumar-giri-judge-allahabad-high-court-279470")
 
 # @st.cache(allow_output_mutation=True)
 # def get_models_and_tokenizers():
@@ -81,11 +81,11 @@ tokens_info = tokenizer(concated_text, truncation=True, return_tensors="pt")
 with torch.no_grad():
     raw_predictions = model(**tokens_info)
 softmaxed = int(torch.nn.functional.softmax(raw_predictions.logits[0], dim=0)[1] * 100)
-st.markdown("### Fakeness Prediction")
+st.markdown("### Truthteller Predicts..")
 st.progress(softmaxed)
 st.markdown(f"This is fake by *{softmaxed}%*!")
 if (softmaxed > 70):
-    st.error('We would not trust this text!')
+    st.error('We would not trust this text! This is misleading..')
 elif (softmaxed > 40):
     st.warning('We are not sure about this text!')
 else: