Create app.py

app.py

@@ -0,0 +1,49 @@
+import streamlit as st
+import pandas as pd
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+
+# Load the BERT tokenizer and model
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
+
+# Define the prediction function
+def predict_sentiment(text):
+    # Tokenize the text
+    encoded_text = tokenizer.encode_plus(
+        text, 
+        max_length=128, 
+        padding='max_length', 
+        truncation=True, 
+        return_attention_mask=True, 
+        return_tensors='pt'
+    )
+
+    # Make the prediction
+    output = model(encoded_text['input_ids'], attention_mask=encoded_text['attention_mask'])
+    prediction = torch.argmax(output.logits, dim=1).item()
+    
+    # Return the predicted sentiment
+    if prediction == 1:
+        return "Positive"
+    else:
+        return "Negative"
+
+# Define the Streamlit app
+def app():
+    st.title("BERT Sentiment Analysis")
+    st.write("Upload a CSV file with a 'text' column and I'll predict the sentiment for each row.")
+
+    # Get user input
+    file = st.file_uploader("Upload CSV file", type=["csv"])
+    if file is not None:
+        df = pd.read_csv(file)
+
+        # Make the predictions and add them to the dataframe
+        df['sentiment'] = df['text'].apply(predict_sentiment)
+
+        # Display the results
+        st.write(df)
+
+if __name__ == '__main__':
+    app()