UPDATE ARENA

app.py

@@ -1,19 +1,45 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from collections import defaultdict, Counter
+import random
+import threading
+import time
+import os
 
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
+# 比較するLLMのリストを定義
+llm_list = [
+    "HuggingFaceH4/zephyr-7b-beta",
+    "AXCXEPT/EZO-Common-9B-gemma-2-it",
+    # "モデル名1",
+    # "モデル名2",
+]
 
+# 各LLMの出現回数を管理
+llm_counts = defaultdict(int)
 
-def respond(
+# 各LLMのInferenceClientを作成
+clients = {llm: InferenceClient(llm) for llm in llm_list}
+
+# LLMを等しくランダムに選択する関数
+def select_llms():
+    min_count = min(llm_counts.values()) if llm_counts else 0
+    candidates = [llm for llm in llm_list if llm_counts[llm] == min_count]
+    if len(candidates) < 2:
+        candidates = llm_list
+    selected_llms = random.sample(candidates, 2)
+    for llm in selected_llms:
+        llm_counts[llm] += 1
+    return selected_llms
+
+# 各LLMに対する応答を生成する関数
+def respond_llm(
     message,
     history: list[tuple[str, str]],
     system_message,
     max_tokens,
     temperature,
     top_p,
+    llm_client,
 ):
     messages = [{"role": "system", "content": system_message}]
 
@@ -27,38 +53,110 @@ def respond(
 
     response = ""
 
-    for message in client.chat_completion(
+    for message in llm_client.chat_completion(
         messages,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        token = message.choices[0].delta.content
-
+        token = message.choices[0].delta.get("content", "")
         response += token
         yield response
 
+# 投票結果を保存するファイルのパス
+VOTE_FILE = "votes.txt"
+
+# 投票結果を保存する関数
+def save_vote(selected_llm):
+    # 投票結果をファイルに保存
+    with open(VOTE_FILE, "a") as f:
+        f.write(f"{selected_llm}\n")
+    return gr.update(visible=True, value="投票ありがとうございました！")
+
+# リーダーボードを更新する関数
+def update_leaderboard():
+    try:
+        with open(VOTE_FILE, "r") as f:
+            votes = f.readlines()
+        vote_counts = Counter(vote.strip() for vote in votes)
+        leaderboard = sorted(vote_counts.items(), key=lambda x: x[1], reverse=True)
+        leaderboard_text = "## リーダーボード\n\n"
+        for llm, count in leaderboard:
+            leaderboard_text += f"- {llm}: {count}票\n"
+    except FileNotFoundError:
+        leaderboard_text = "まだ投票がありません。"
+    return leaderboard_text
+
+# Gradioインターフェースの構築
+def chat_interface():
+    llm1, llm2 = select_llms()
+    client1 = clients[llm1]
+    client2 = clients[llm2]
+
+    with gr.Blocks() as demo:
+        gr.Markdown("## LLM比較アリーナ")
+
+        with gr.Row():
+            gr.Markdown(f"### LLM1: {llm1}")
+            gr.Markdown(f"### LLM2: {llm2}")
 
-"""
-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="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+        with gr.Row():
+            with gr.Column():
+                chat1 = gr.ChatInterface(
+                    lambda message, history, system_message, max_tokens, temperature, top_p:
+                        respond_llm(message, history, system_message, max_tokens, temperature, top_p, client1),
+                    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=512, step=1, label="最大トークン数"),
+                        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="トップP"),
+                    ],
+                )
+            with gr.Column():
+                chat2 = gr.ChatInterface(
+                    lambda message, history, system_message, max_tokens, temperature, top_p:
+                        respond_llm(message, history, system_message, max_tokens, temperature, top_p, client2),
+                    additional_inputs=[
+                        gr.Textbox(value="あなたはフレンドリーなチ��ットボットです。", label="システムメッセージ"),
+                        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="最大トークン数"),
+                        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="トップP"),
+                    ],
+                )
 
+        # 投票セクション
+        with gr.Row():
+            vote = gr.Radio([llm1, llm2], label="どちらの応答が良かったですか？")
+            submit = gr.Button("投票")
+            result = gr.Textbox(label="", visible=False)
+
+        submit.click(save_vote, inputs=vote, outputs=result)
+
+        # リーダーボードの表示
+        leaderboard = gr.Markdown(update_leaderboard())
+
+    return demo
+
+# リーダーボードを定期的に更新するスレッド
+def refresh_leaderboard(leaderboard_component):
+    while True:
+        leaderboard_text = update_leaderboard()
+        leaderboard_component.value = leaderboard_text
+        time.sleep(60)  # 60秒ごとに更新
 
 if __name__ == "__main__":
+    demo = chat_interface()
+
+    # リーダーボードコンポーネントを取得
+    leaderboard_component = None
+    for component in demo.blocks:
+        if isinstance(component, gr.Markdown) and "リーダーボード" in component.value:
+            leaderboard_component = component
+            break
+
+    # リーダーボード更新スレッドの開始
+    if leaderboard_component:
+        threading.Thread(target=refresh_leaderboard, args=(leaderboard_component,), daemon=True).start()
+
     demo.launch()