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Update app.py
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app.py
CHANGED
@@ -1,14 +1,23 @@
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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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-
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"""
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client = InferenceClient("Sakalti/
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def respond(
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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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@@ -23,7 +32,11 @@ def respond(
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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response = ""
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@@ -39,13 +52,13 @@ def respond(
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response += token
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yield response
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"""
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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="あなたは親切なチャットボットです。", 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="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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@@ -59,6 +72,5 @@ demo = gr.ChatInterface(
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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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import base64
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from io import BytesIO
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from PIL import Image
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"""
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Hugging Face Hubの推論APIについての詳細は、以下のドキュメントを参照してください: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
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"""
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client = InferenceClient("Sakalti/SabaVL1-2B") # モデル名を更新
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def encode_image(image):
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buffered = BytesIO()
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image.save(buffered, format="JPEG")
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img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
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return f"data:image/jpeg;base64,{img_str}"
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def respond(
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message,
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image,
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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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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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if image is not None:
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image_url = encode_image(image)
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messages.append({"role": "user", "content": [{"type": "image_url", "image_url": {"url": image_url}}]})
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messages.append({"role": "user", "content": [{"type": "text", "text": message}]})
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response = ""
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response += token
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yield response
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"""
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gradioのChatInterfaceのカスタマイズについては、以下のドキュメントを参照してください: 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.Image(type="pil", label="画像をアップロード"),
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gr.Textbox(value="あなたは親切なチャットボットです。", 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="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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],
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)
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if __name__ == "__main__":
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demo.launch()
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