Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import torch
+import transformers
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Download and load the model and tokenizer
+model_name = 'bert-base-uncased'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Define a function to classify a single text
+def classify_text(text):
+    # Tokenize the text and add special tokens
+    inputs = tokenizer.encode_plus(
+        text,
+        add_special_tokens=True,
+        return_tensors='pt',
+        max_length=512
+    )
+    
+    # Get the input IDs and attention mask
+    input_ids = inputs['input_ids']
+    attention_mask = inputs['attention_mask']
+    
+    # Get the predicted label
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask)
+        logits = outputs[0]
+        predicted_label = torch.argmax(logits, dim=1).item()
+        
+    return predicted_label
+
+# Define the Streamlit app
+def main():
+    st.title('Text Classification with BERT')
+    
+    # Allow the user to upload a CSV file
+    uploaded_file = st.file_uploader('Upload a CSV file', type='csv')
+    if uploaded_file is not None:
+        data = pd.read_csv(uploaded_file)
+        
+        # Create a new column for the predicted labels
+        data['predicted_label'] = data['text'].apply(classify_text)
+        
+        st.write(data)
+
+if __name__ == '__main__':
+    main()