Update app.py

app.py

@@ -1,59 +1,60 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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
-"""
-client = InferenceClient("Qwen/Qwen2.5-0.5b-Instruct")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    # ストリーミングを無効にして、単一の応答を取得
-    response = client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        temperature=temperature,
-        top_p=top_p,
-    )
-
-    return response.choices[0].message.content
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="ユーザーの質問や依頼にのみ答えてください。ポジティブに答えてください", label="システムプロンプト"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="トークンの最大値"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="温度"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+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 = "Qwen/Qwen2.5-0.5b-Instruct"
+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()