Create app.py

app.py

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+from transformers import LongformerTokenizer, LongformerModel
+import torch
+import gradio as gr
+
+# Load the pre-trained Longformer model and tokenizer
+tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
+model = LongformerModel.from_pretrained('allenai/longformer-base-4096')
+
+def get_longformer_embeddings(sentences):
+    # Tokenize input sentences
+    inputs = tokenizer(sentences, return_tensors='pt', padding=True, truncation=True, max_length=2048)
+    # Get embeddings
+    with torch.no_grad():
+        outputs = model(**inputs)
+    embeddings = outputs.last_hidden_state.mean(dim=1)  # Mean pooling over the sequence
+    return embeddings.numpy().tolist()
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=get_longformer_embeddings,  # Function to call
+    inputs=gr.Textbox(lines=2, placeholder="Enter sentences here, one per line"),  # Input component
+    outputs=gr.JSON(),  # Output component
+    title="Sentence Embeddings with Longformer",  # Interface title
+    description="Enter sentences to get their embeddings with Longformer (up to 2048 tokens)."  # Description
+)
+
+# Launch the interface
+interface.launch()