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Update app.py
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app.py
CHANGED
@@ -1,4 +1,5 @@
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import os
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if os.environ.get("SPACES_ZERO_GPU") is not None:
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import spaces
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else:
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@@ -11,7 +12,7 @@ else:
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@spaces.GPU
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def fake_gpu():
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-
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import numpy as np
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import torch
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@@ -58,7 +59,7 @@ def get_next_token_predictions(text, model_name, top_k=10):
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return top_k_tokens, top_k_probs.tolist()
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def predict_next_token(
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# Add custom token if provided
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if custom_token:
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text += custom_token
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@@ -69,7 +70,7 @@ def predict_next_token(text, model_name, custom_token=""):
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# Format predictions
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predictions = "\n".join([f"'{token}' : {prob:.4f}" for token, prob in zip(tokens, probs)])
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return
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# Create the interface
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with gr.Blocks() as demo:
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@@ -77,31 +78,26 @@ with gr.Blocks() as demo:
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gr.Markdown("""
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This application allows you to interactively generate text using various transformer models.
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Select a model, start typing or choose from the predicted tokens, and see how the model continues your text!
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""")
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with gr.Row():
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text_input = gr.Textbox(
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lines=5,
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label="Text",
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placeholder="Type your text here...",
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value="The quick brown fox"
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)
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with gr.Row():
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model_dropdown = gr.Dropdown(
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choices=list(AVAILABLE_MODELS.keys()),
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value="distilgpt2",
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label="Select Model"
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)
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with gr.Row():
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)
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with gr.Row():
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token_dropdown = gr.Dropdown(
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@@ -115,29 +111,11 @@ with gr.Blocks() as demo:
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label="Token probabilities"
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)
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# Set up event
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predict_next_token,
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inputs=[text_input, model_dropdown, custom_input],
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outputs=[text_input, token_dropdown, predictions_output]
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)
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model_dropdown.change(
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predict_next_token,
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inputs=[text_input, model_dropdown, custom_input],
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outputs=[text_input, token_dropdown, predictions_output]
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)
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custom_input.change(
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predict_next_token,
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inputs=[text_input, model_dropdown, custom_input],
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outputs=[text_input, token_dropdown, predictions_output]
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)
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token_dropdown.change(
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predict_next_token,
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inputs=[
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outputs=[
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)
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demo.queue().launch()
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import os
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# Handle Spaces GPU
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if os.environ.get("SPACES_ZERO_GPU") is not None:
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import spaces
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else:
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@spaces.GPU
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def fake_gpu():
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pass
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import numpy as np
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import torch
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return top_k_tokens, top_k_probs.tolist()
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def predict_next_token(model_name, text, custom_token=""):
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# Add custom token if provided
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if custom_token:
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text += custom_token
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# Format predictions
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predictions = "\n".join([f"'{token}' : {prob:.4f}" for token, prob in zip(tokens, probs)])
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return gr.update(choices=[f"'{t}'" for t in tokens]), predictions
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# Create the interface
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with gr.Blocks() as demo:
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gr.Markdown("""
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This application allows you to interactively generate text using various transformer models.
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Select a model, enter your text, and click predict to see the possible next tokens and their probabilities.
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""")
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with gr.Row():
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model_dropdown = gr.Dropdown(
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choices=list(AVAILABLE_MODELS.keys()),
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value="distilgpt2",
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label="Select Model"
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)
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with gr.Row():
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text_input = gr.Textbox(
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lines=5,
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label="Text",
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placeholder="Type your text here...",
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value="The quick brown fox"
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)
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with gr.Row():
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predict_button = gr.Button("Predict")
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with gr.Row():
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token_dropdown = gr.Dropdown(
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label="Token probabilities"
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)
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# Set up predict button event handler
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predict_button.click(
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predict_next_token,
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inputs=[model_dropdown, text_input],
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outputs=[token_dropdown, predictions_output]
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)
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demo.queue().launch()
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