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sultan-hassan
commited on
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Parent(s):
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Update app.py
Browse files
app.py
CHANGED
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import gradio as gr
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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("sultan-hassan/CosmoGemma_2b_en")
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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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):
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messages = [{"role": "system", "content": system_message}]
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for message in client.chat_completion(
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messages,
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max_tokens=max_tokens,
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stream=True,
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temperature=temperature,
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top_p=top_p,
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):
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token = message.choices[0].delta.content
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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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respond,
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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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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(
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minimum=0.1,
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maximum=1.0,
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value=0.95,
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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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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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import os
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os.environ["KERAS_BACKEND"] = "tensorflow"
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import keras
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import keras_nlp
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gemma_lm = keras_nlp.models.CausalLM.from_preset("hf://sultan-hassan/CosmoGemma_2b_en")
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def launch(input):
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template = "Instruction:\n{instruction}\n\nResponse:\n{response}"
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prompt = template.format(
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instruction=input,
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response="",
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)
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out = gemma_lm.generate(prompt, max_length=256)
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ind = out.index('Response') + len('Response')+2
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return out[ind:]
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iface = gr.Interface(launch,
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inputs="text",
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outputs="text")
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iface.launch()
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