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app.py

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+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import streamlit as st
+
+model = AutoModelForSequenceClassification.from_pretrained('paramasivan27/RetailProductClassification_bert-base-uncased')
+tokenizer = AutoTokenizer.from_pretrained('paramasivan27/RetailProductClassification_bert-base-uncased')
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    outputs = model(**inputs)
+    predicted_class = torch.argmax(outputs.logits, dim=1).item()
+    return predicted_class
+
+# Streamlit app layout
+st.title("Retail Product Classification")
+st.write("Enter the product title and description to classify the product.")
+
+# Input fields for title and description
+title = st.text_input("Product Title")
+description = st.text_area("Product Description")
+
+# Combine title and description
+if st.button("Classify Product"):
+    if title and description:
+        combined_text = f"{title}. {description}"
+        predicted_class = predict(combined_text)
+        st.write(f"Predicted Class: {predicted_class}")
+    else:
+        st.write("Please enter both title and description.")

requirements.txt

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+transformers
+torch
+datasets
+scikit-learn