Upload 2 files

app.py

@@ -0,0 +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]}")

requirements.txt

@@ -0,0 +1,2 @@
+torch==2.4.1
+transformers==4.35.2