Update app.py
Browse files
app.py
CHANGED
@@ -4,8 +4,13 @@ from huggingface_hub import InferenceClient
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"""
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For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
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"""
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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def respond(
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message,
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@@ -14,7 +19,12 @@ def respond(
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max_tokens,
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temperature,
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top_p,
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):
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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@@ -39,12 +49,45 @@ def respond(
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response += token
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yield response
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"""
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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"""
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demo = gr.ChatInterface(
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additional_inputs=[
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gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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@@ -56,9 +99,9 @@ demo = gr.ChatInterface(
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step=0.05,
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label="Top-p (nucleus sampling)",
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),
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],
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)
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-
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if __name__ == "__main__":
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demo.launch()
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"""
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For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
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"""
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# Default client with the first model
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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# Function to switch between models based on selection
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def switch_client(model_name: str):
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return InferenceClient(model_name)
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def respond(
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message,
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max_tokens,
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temperature,
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top_p,
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model_name # Add this parameter for model selection
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):
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# Switch client based on model selection
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global client
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client = switch_client(model_name)
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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response += token
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yield response
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# Adding the model name at the end of the response
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yield f"\n\n[Response generated by model: {model_name}]"
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# Model names and their custom display names
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model_choices = [
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("HuggingFaceH4/zephyr-7b-beta", "Lake [Test]"),
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("google/mt5-base", "Lake 1 Base"),
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("google/mt5-large", "Lake 1 Advanced")
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]
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# Convert model choices into just the model names for the dropdown
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model_names = [model[0] for model in model_choices]
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# Function to handle model selection and display name for the model
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def respond_with_model_name(
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message,
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history: list[tuple[str, str]],
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system_message,
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max_tokens,
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temperature,
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top_p,
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model_name
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):
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# Find the display name of the selected model
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model_display_name = dict(model_choices)[model_name]
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# Call the existing respond function
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response = list(respond(message, history, system_message, max_tokens, temperature, top_p, model_name))
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# Add model name at the end of the response
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response[-1] += f"\n\n[Response generated by: {model_display_name}]"
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return response
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"""
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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"""
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demo = gr.ChatInterface(
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respond_with_model_name,
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additional_inputs=[
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gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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step=0.05,
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label="Top-p (nucleus sampling)",
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),
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gr.Dropdown(model_names, label="Select Model", value=model_names[0]) # Model selection dropdown
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],
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)
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if __name__ == "__main__":
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demo.launch()
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