Update app.py

app.py

@@ -1,30 +1,30 @@
-from transformers import BertTokenizer, BertForSequenceClassification
-import torch
-import streamlit as st
-
-tokenizer = BertTokenizer.from_pretrained(
-    "ashish-001/Bert-Amazon-review-sentiment-classifier")
-model = BertForSequenceClassification.from_pretrained(
-    "ashish-001/Bert-Amazon-review-sentiment-classifier")
-
-
-def classify_text(text):
-    inputs = tokenizer(
-        text,
-        max_length=256,
-        truncation=True,
-        padding="max_length",
-        return_tensors="pt"
-    )
-    output = model(**inputs)
-    logits = output.logits
-    probs = torch.nn.functional.sigmoid(logits)
-    return probs
-
-
-st.title("Amazon Review Sentiment classifier")
-data = st.text_area("Enter or paste a review")
-if st.button('Predict'):
-    prediction = classify_text(data)
-    st.header(
-        f"Negative Confidence: {prediction[0]}, Positive Confidence: {prediction[1]}")
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+import streamlit as st
+
+tokenizer = BertTokenizer.from_pretrained(
+    "ashish-001/Bert-Amazon-review-sentiment-classifier")
+model = BertForSequenceClassification.from_pretrained(
+    "ashish-001/Bert-Amazon-review-sentiment-classifier")
+
+
+def classify_text(text):
+    inputs = tokenizer(
+        text,
+        max_length=256,
+        truncation=True,
+        padding="max_length",
+        return_tensors="pt"
+    )
+    output = model(**inputs)
+    logits = output.logits
+    probs = torch.nn.functional.sigmoid(logits)
+    return probs
+
+
+st.title("Amazon Review Sentiment classifier")
+data = st.text_area("Enter or paste a review")
+if st.button('Predict'):
+    prediction = classify_text(data)
+    st.header(
+        f"{prediction}")