Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+# 使用可能なモデルのリスト
+models = ["Qwen/Qwen2.5-7B-Instruct", "Qwen/Qwen2.5-14B-Instruct", "Qwen/Qwen2.5-32B-Instruct"]
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    selected_model
+):
+    # 型変換: selected_modelを文字列に変換
+    selected_model = str(selected_model)
+    
+    # 選択したモデルに基づいてPipelineを初期化
+    pipe = pipeline(task=Tasks.text_generation, model=selected_model)
+
+    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})
+
+    response = ""
+
+    # モデルの推論
+    for message in pipe(messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, stream=True):
+        token = message.get("choices", [{}])[0].get("delta", {}).get("content", "")
+        response += token
+
+    return response
+
+# インターフェース
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="あなたはフレンドリーなチャットボットです。", label="システムメッセージ"),
+        gr.Slider(minimum=1, maximum=2048, value=768, step=1, label="新規トークン最大"),
+        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 (核 sampling)"),
+        gr.Dropdown(choices=models, value=models[0], label="モデル"),
+    ],
+    concurrency_limit=30  # 例: 同時に30つのリクエストを処理
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)