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app.py

@@ -1,18 +1,21 @@
 import streamlit as st
 from transformers import pipeline
+st.cache_resource(ttl=300)
 
 pipe = pipeline(task="text-classification",model="yartyjung/Fake-Review-Detector")
-
-st.title("Review-Detector")
-text = st.text_input("your :red[suspicious] review here :sunglasses:",value="")
-
-if text is not None:
-    predictions = pipe(text)
-    st.text(predictions)
-    if predictions[0]['label'] == 'fake':
-        for p in predictions:
-            st.subheader(f":red[FAKE] :blue[{ round(p['score'] * 100, 1)} %]")
-    elif predictions[0]['label'] == 'real':
-        for p in predictions:
-            st.subheader(f":green[REAL] :blue[{ round(p['score'] * 100, 1)} %]")
-    st.markdown(":red[***disclaimer***   This is a prediction by an _AI_, which might turn out incorrect.]")
+st.title(":rainbow[Review-Detector]")
+st.divider()
+text = st.text_input("Your :red[suspicious] review here :sunglasses:",value="")
+if st.button("predict"):
+    if text is not None:
+        predictions = pipe(text)
+        if predictions[0]['label'] == 'fake':
+            for p in predictions:
+                st.subheader(f":red[FAKE]   :blue[{ round(p['score'] * 100, 1)} %]")
+        elif predictions[0]['label'] == 'real':
+            for p in predictions:
+                st.subheader(f":green[REAL]   :blue[{ round(p['score'] * 100, 1)} %]")
+        st.divider()
+        st.markdown(":red[***disclaimer***   This is a prediction by an _AI_, which might turn out incorrect.]")
+        url = "https://huggingface.co/yartyjung/Fake-Review-Detector"
+        st.markdown(":yellow[check out model at this [link](%s)]" % url)

requirements.txt

@@ -1,2 +1,2 @@
-transformers
+transformers
 torch