Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# モデルとトークナイザーの読み込み
+model_name = "Sakalti/Baku"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, ignore_mismatched_sizes=True)
+
+# 応答を生成する関数
+def respond(message, history, max_tokens, temperature, top_p):
+    # 入力履歴と新しいメッセージを連結
+    if history is None:
+        history = []
+
+    input_text = ""
+    for user_message, bot_response in history:
+        input_text += f"User: {user_message}\nAssistant: {bot_response}\n"
+    input_text += f"User: {message}\nAssistant:"
+
+    # トークナイズ
+    inputs = tokenizer(input_text, return_tensors="pt")
+
+    # モデルによる応答生成
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs.input_ids,
+            max_length=inputs.input_ids.shape[1] + max_tokens,
+            do_sample=True,
+            top_p=top_p,
+            temperature=temperature,
+        )
+
+    # 応答をデコード
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # 最後のユーザー入力以降の応答部分を抽出
+    response = response.split("Assistant:")[-1].strip()
+
+    # 応答と履歴を更新
+    history.append((message, response))
+    return response, history
+
+# Gradioインターフェースの設定
+with gr.Blocks() as demo:
+    gr.Markdown("## AIチャット")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="あなたのメッセージ", placeholder="ここにメッセージを入力...")
+    max_tokens = gr.Slider(1, 2048, value=512, step=1, label="Max new tokens")
+    temperature = gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature")
+    top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+    send_button = gr.Button("送信")
+    clear = gr.Button("クリア")
+
+    def clear_history():
+        return [], []
+
+    send_button.click(respond, inputs=[msg, chatbot, max_tokens, temperature, top_p], outputs=[chatbot, chatbot])
+    clear.click(clear_history, outputs=[chatbot])
+
+demo.launch()