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Update app.py
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app.py
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@@ -1,11 +1,8 @@
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import gradio as gr
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from huggingface_hub import InferenceClient
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"""
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client = InferenceClient("Sakalti/Saba1.5-Pro-3B")
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#応答部分
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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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):
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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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"""
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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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#インターフェース
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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="ユーザーの応答と依頼に答えてください。ポジティブに", label="システムメッセージ"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="新規トークン最大"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="温度"),
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concurrency_limit=30 # 例: 同時に4つのリクエストを処理
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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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from huggingface_hub import InferenceClient
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# 使用可能なモデルのリスト
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models = ["Sakalti/Saba1.5-Pro", "Sakalti/Saba2-Preview", "Sakalti/Saba2.1-Preview"]
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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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selected_model
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):
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# 選択したモデルに基づいてInferenceClientを初期化
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client = InferenceClient(selected_model)
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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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# インターフェース
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demo = gr.ChatInterface(
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respond,
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additional_inputs=[
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gr.Dropdown(choices=models, value=models[0], label="モデル"),
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gr.Textbox(value="ユーザーの応答と依頼に答えてください。ポジティブに", label="システムメッセージ"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="新規トークン最大"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="温度"),
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concurrency_limit=30 # 例: 同時に4つのリクエストを処理
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)
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if __name__ == "__main__":
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demo.launch()
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