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Update app.py
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app.py
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@@ -1,19 +1,20 @@
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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import gradio as gr
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# モデルとトークナイザーの読み込み
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model = AutoModelForCausalLM.from_pretrained(model_name)
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return model, tokenizer
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model, tokenizer = load_model()
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# 応答を生成する関数
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def respond(message, history, max_tokens, temperature, top_p):
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# 入力履歴と新しいメッセージを連結
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input_text = ""
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for user_message, bot_response in history:
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input_text += f"User: {user_message}\nAssistant: {bot_response}\n"
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@@ -55,7 +56,7 @@ with gr.Blocks() as demo:
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def clear_history():
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return [], []
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send_button.click(respond, inputs=[msg, chatbot, max_tokens, temperature, top_p], outputs=[chatbot, chatbot])
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clear.click(clear_history, outputs=[chatbot])
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demo.launch()
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!pip install torch transformers gradio
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# モデルとトークナイザーの読み込み
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model_name = "EleutherAI/pythia-1b-deduped"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# 応答を生成する関数
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def respond(message, history, max_tokens, temperature, top_p):
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# 入力履歴と新しいメッセージを連結
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if history is None:
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history = []
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input_text = ""
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for user_message, bot_response in history:
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input_text += f"User: {user_message}\nAssistant: {bot_response}\n"
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def clear_history():
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return [], []
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send_button.click(respond, inputs=[msg, chatbot, max_tokens, temperature, top_p], outputs=[chatbot, chatbot], _js=None)
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clear.click(clear_history, outputs=[chatbot])
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demo.launch()
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