Update app.py

app.py

@@ -6,44 +6,10 @@ def mask(text):
   output = mask_model(text)
   return output[0]['sequence']
 
-with gr.Blocks() as demo:
+# Gradio UI
+with gr.Blocks() as ui:
   gr.Markdown("BERT Test - Zircon.tech")
-#tab single model
-  with gr.Tab("Single-Models"):
-    with gr.Row(equal_height=True):
-        with gr.Column(scale=1):
-         textbox_mask = gr.Textbox(label="Give me a sentance and use the word [MASK] to predict what will go there")
-         mask_button = gr.Button("Predict")
-         textbox_mask_output = gr.Textbox()
-
-         mask_button.click(fn=mask,
-                inputs = textbox_mask,
-                outputs = textbox_mask_output)
-demo.launch(debug=True)
-
-    
-# # Load the model and tokenizer
-# tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
-# model = BertModel.from_pretrained("google-bert/bert-base-uncased")
-
-# def predict(input_text):
-#     # Preprocess the input text
-#     inputs = tokenizer(input_text, return_tensors="pt")
-#     # Get the model's output
-#     outputs = model(**inputs)
-#     # Extract the last hidden state
-#     last_hidden_state = outputs.last_hidden_state[:, 0, :]
-#     # Return a response based on the output
-#     return {"response": last_hidden_state.detach().numpy().tolist()}
-
-# # Create a Gradio interface
-# iface = gr.Interface(
-#     fn=predict,
-#     inputs="text",
-#     outputs="text",
-#     title="BERT-based Text Analyzer",
-#     description="Enter some text to analyze with BERT!"
-# )
-
-# # Launch the interface
-# iface.launch()
+  gr.Button("Predict").click(fn=mask,
+  inputs = gr.Textbox(label="Give me a sentance and use the word [MASK] to predict what will go there"),
+  outputs = gr.Textbox())
+ui.launch(share=True)