Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelWithHeads, AutoTokenizer
+import torch
+
+# Load pre-trained BERT model with adapter support
+st.title("Adapter Transformers for Text Classification")
+
+@st.cache_resource
+def load_model():
+    model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+    
+    # Add and activate an adapter
+    adapter_name = "my_adapter"
+    model.add_adapter(adapter_name)
+    model.train_adapter(adapter_name)
+    model.set_active_adapters(adapter_name)
+    
+    # Add a classification head (binary classification)
+    model.add_classification_head(adapter_name, num_labels=2)
+    return model, tokenizer
+
+# Load the model
+model, tokenizer = load_model()
+
+# Streamlit input
+input_text = st.text_input("Enter text for classification:", "Steve Jobs founded Apple")
+
+if input_text:
+    # Tokenize the input
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    
+    # Make the prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class = logits.argmax(dim=-1).item()
+
+    # Display the prediction
+    if predicted_class == 0:
+        st.write("Prediction: Negative")
+    else:
+        st.write("Prediction: Positive")