.

gradio_ui.ok.py

@@ -0,0 +1,214 @@
+import time
+import os
+import glob
+import threading
+import subprocess
+import gradio as gr
+
+# 全局变量：当前子进程
+current_process = None
+
+
+# ⬇️⬇️⬇️ 运行 EvalScope 并（可选）启动可视化服务 ⬇️⬇️⬇️
+def run_eval(inputs, native, other, output_choices, api_url, api_token):
+    """
+    1. 调用 `evalscope perf …` 跑基准测试
+    2. 若用户勾选 “Evaluation Report”，测试完成后后台启动
+       `evalscope app` Web 可视化服务，并在文本框追加访问链接
+    """
+    global current_process
+
+    timestamp = time.strftime("%Y%m%d-%H%M%S")
+    command = [
+        "evalscope", "perf",
+        "--url", api_url.strip(),
+        "--api", "openai",
+        "--model", timestamp,       # 以时间戳当模型名，避免冲突
+        "--dataset", "openqa",
+        "--max-tokens", "1024",
+        "--min-tokens", "1024",
+        "--parallel", "1",
+        "--max-prompt-length", "15360",
+        "--number", "100",
+        "--api-key", api_token.strip(),
+    ]
+
+    full_output = f"[Eval Started @ {timestamp}]\n"
+    yield full_output, True, gr.update(value="Stop Evaluation")
+
+    try:
+        current_process = subprocess.Popen(
+            command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
+            text=True, bufsize=1
+        )
+
+        # 实时流式输出
+        for line in current_process.stdout:
+            full_output += line
+            yield full_output, True, gr.update(value="Stop Evaluation")
+
+        current_process.stdout.close()
+        current_process.wait()
+
+    except Exception as e:
+        full_output += f"[Error] {e}\n"
+        yield full_output, False, gr.update(value="Run Evaluation")
+
+    finally:
+        current_process = None
+
+    full_output += "[Eval Finished]\n"
+
+    # ========== 可视化报告 ==========
+    if "Evaluation Report" in output_choices:
+        vis_port = 7861
+        outputs_root = "./outputs"
+        # ⬇️ EvalScope perf 会在 outputs_root 下生成 timestamp 目录
+        #    这里额外取最新目录备用（目前 UI 只需要根目录）
+        try:
+            latest_output = max(
+                glob.glob(os.path.join(outputs_root, "*")),
+                key=os.path.getmtime
+            )
+        except ValueError:
+            latest_output = outputs_root  # 保险：若 outputs 还不存在
+
+        vis_cmd = [
+            "evalscope", "app",
+            "--outputs", outputs_root,
+            "--server-name", "0.0.0.0",
+            "--server-port", str(vis_port),
+        ]
+
+        # 后台线程启动，不阻塞 UI
+        threading.Thread(
+            target=subprocess.Popen,
+            args=(vis_cmd,),
+            kwargs={"stdout": subprocess.DEVNULL,
+                    "stderr": subprocess.STDOUT},
+            daemon=True
+        ).start()
+
+        full_output += f"[Visualization 👉] http://localhost:{vis_port}\n"
+
+    yield full_output, False, gr.update(value="Run Evaluation")
+
+
+# ⬇️⬇️⬇️ 停止按钮逻辑 ⬇️⬇️⬇️
+def stop_eval():
+    global current_process
+    if current_process and current_process.poll() is None:
+        current_process.terminate()
+        current_process = None
+        return "[Stopped by user]\n"
+    return "[No active process]\n"
+
+
+# ⬇️⬇️⬇️ Run/Stop 控制器（必须是 generator） ⬇️⬇️⬇️
+def toggle_run(inputs, native, other, output_choices,
+               api_url, api_token, is_running):
+    if not is_running:
+        # 开始跑
+        yield from run_eval(inputs, native, other,
+                            output_choices, api_url, api_token)
+    else:
+        # 用户点 Stop
+        msg = stop_eval()
+        yield msg, False, gr.update(value="Run Evaluation")
+
+
+# ⬇️⬇️⬇️ 互斥逻辑：同组保留最后一个选项 ⬇️⬇️⬇️
+def enforce_input_exclusive_and_toggle_fields(selected):
+    group1 = {"API Models", "Local Models"}
+    group2 = {"Benchmarks", "Custom Datasets"}
+
+    def keep_only_one(group):
+        filtered = [item for item in selected if item in group]
+        return filtered[-1:]
+
+    final_selection = set(selected)
+    final_selection -= group1
+    final_selection |= set(keep_only_one(group1))
+
+    final_selection -= group2
+    final_selection |= set(keep_only_one(group2))
+
+    show_api_fields = "API Models" in final_selection
+    return (
+        gr.update(value=list(final_selection)),
+        gr.Row.update(visible=show_api_fields)
+    )
+
+
+# ------------- 构建 Gradio UI -------------
+with gr.Blocks(title="EvalScope 全功能界面") as demo:
+    is_running = gr.State(value=False)
+
+    with gr.Group():
+        with gr.Row():
+            input_choices = gr.CheckboxGroup(
+                label="选择输入源",
+                choices=["API Models", "Local Models",
+                         "Benchmarks", "Custom Datasets"],
+                interactive=True
+            )
+
+    with gr.Row(visible=False) as api_fields:
+        api_url_input = gr.Textbox(
+            label="API 地址",
+            placeholder="https://api.example.com/v1/chat"
+        )
+        api_token_input = gr.Textbox(
+            label="Token 密钥",
+            type="password",
+            placeholder="sk-xxx"
+        )
+
+    with gr.Row():
+        with gr.Column():
+            native_choices = gr.CheckboxGroup(
+                label="启用本地模块",
+                choices=["Model Adapter", "Data Adapter",
+                         "Evaluator", "Perf Monitor"]
+            )
+        with gr.Column():
+            other_choices = gr.CheckboxGroup(
+                label="启用外部后端",
+                choices=["OpenCompass", "VLMEvalKit",
+                         "RAGAS", "MTEB/CMTEB"]
+            )
+
+    with gr.Row():
+        output_choices = gr.CheckboxGroup(
+            label="输出形式",
+            choices=["Evaluation Report", "Gradio", "WandB", "Swanlab"]
+        )
+
+    run_button = gr.Button("Run Evaluation")
+    output_text = gr.TextArea(
+        label="执行结果",
+        lines=20,
+        interactive=False,
+        show_copy_button=True
+    )
+
+    # 绑定输入互斥
+    input_choices.change(
+        fn=enforce_input_exclusive_and_toggle_fields,
+        inputs=input_choices,
+        outputs=[input_choices, api_fields]
+    )
+
+    # 绑定 Run/Stop
+    run_button.click(
+        fn=toggle_run,
+        inputs=[
+            input_choices, native_choices, other_choices,
+            output_choices, api_url_input, api_token_input, is_running
+        ],
+        outputs=[output_text, is_running, run_button],
+        show_progress=True
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7900)

gradio_ui.py

@@ -5,35 +5,44 @@ import threading
 import subprocess
 import gradio as gr
 
-# 全局变量：当前子进程
+# ---------------- 全局进程句柄 ----------------
 current_process = None
 
 
-# ⬇️⬇️⬇️ 运行 EvalScope 并（可选）启动可视化服务 ⬇️⬇️⬇️
+# ---------------- 核心运行函数 ----------------
-def run_eval(inputs, native, other, output_choices, api_url, api_token):
+def run_eval(
+    inputs, native, other, output_choices,
+    api_url, api_token,
+    api_provider, dataset,
+    max_tokens, min_tokens, parallel_reqs,
+    max_prompt_len, num_requests,
+    model_override
+):
     """
-    1. 调用 `evalscope perf …` 跑基准测试
+    1. 动态拼装 evalscope perf 命令
-    2. 若用户勾选 “Evaluation Report”，测试完成后后台启动
+    2. 流式打印日志
-       `evalscope app` Web 可视化服务，并在文本框追加访问链接
+    3. （可选）启动可视化报告
     """
     global current_process
 
     timestamp = time.strftime("%Y%m%d-%H%M%S")
+    model_name = model_override.strip() or timestamp
+
     command = [
         "evalscope", "perf",
         "--url", api_url.strip(),
-        "--api", "openai",
+        "--api", api_provider,
-        "--model", timestamp,       # 以时间戳当模型名，避免冲突
+        "--model", model_name,
-        "--dataset", "openqa",
+        "--dataset", dataset,
-        "--max-tokens", "1024",
+        "--max-tokens", str(int(max_tokens)),
-        "--min-tokens", "1024",
+        "--min-tokens", str(int(min_tokens)),
-        "--parallel", "1",
+        "--parallel", str(int(parallel_reqs)),
-        "--max-prompt-length", "15360",
+        "--max-prompt-length", str(int(max_prompt_len)),
-        "--number", "100",
+        "--number", str(int(num_requests)),
         "--api-key", api_token.strip(),
     ]
 
-    full_output = f"[Eval Started @ {timestamp}]\n"
+    full_output = f"[Eval Started @ {timestamp}]\nCmd: {' '.join(command)}\n"
     yield full_output, True, gr.update(value="Stop Evaluation")
 
     try:
@@ -42,7 +51,6 @@ def run_eval(inputs, native, other, output_choices, api_url, api_token):
             text=True, bufsize=1
         )
 
-        # 实时流式输出
         for line in current_process.stdout:
             full_output += line
             yield full_output, True, gr.update(value="Stop Evaluation")
@@ -59,19 +67,17 @@ def run_eval(inputs, native, other, output_choices, api_url, api_token):
 
     full_output += "[Eval Finished]\n"
 
-    # ========== 可视化报告 ==========
+    # ---------- 可视化报告 ----------
     if "Evaluation Report" in output_choices:
         vis_port = 7861
         outputs_root = "./outputs"
-        # ⬇️ EvalScope perf 会在 outputs_root 下生成 timestamp 目录
-        #    这里额外取最新目录备用（目前 UI 只需要根目录）
         try:
             latest_output = max(
                 glob.glob(os.path.join(outputs_root, "*")),
                 key=os.path.getmtime
             )
         except ValueError:
-            latest_output = outputs_root  # 保险：若 outputs 还不存在
+            latest_output = outputs_root
 
         vis_cmd = [
             "evalscope", "app",
@@ -79,8 +85,6 @@ def run_eval(inputs, native, other, output_choices, api_url, api_token):
             "--server-name", "0.0.0.0",
             "--server-port", str(vis_port),
         ]
-
-        # 后台线程启动，不阻塞 UI
         threading.Thread(
             target=subprocess.Popen,
             args=(vis_cmd,),
@@ -94,7 +98,7 @@ def run_eval(inputs, native, other, output_choices, api_url, api_token):
     yield full_output, False, gr.update(value="Run Evaluation")
 
 
-# ⬇️⬇️⬇️ 停止按钮逻辑 ⬇️⬇️⬇️
+# ---------------- 停止函数 ----------------
 def stop_eval():
     global current_process
     if current_process and current_process.poll() is None:
@@ -104,20 +108,31 @@ def stop_eval():
     return "[No active process]\n"
 
 
-# ⬇️⬇️⬇️ Run/Stop 控制器（必须是 generator） ⬇️⬇️⬇️
+# ---------------- Run/Stop 控制器 ----------------
-def toggle_run(inputs, native, other, output_choices,
+def toggle_run(
-               api_url, api_token, is_running):
+    inputs, native, other, output_choices,
+    api_url, api_token,
+    api_provider, dataset,
+    max_tokens, min_tokens, parallel_reqs,
+    max_prompt_len, num_requests,
+    model_override,
+    is_running
+):
     if not is_running:
-        # 开始跑
+        yield from run_eval(
-        yield from run_eval(inputs, native, other,
+            inputs, native, other, output_choices,
-                            output_choices, api_url, api_token)
+            api_url, api_token,
+            api_provider, dataset,
+            max_tokens, min_tokens, parallel_reqs,
+            max_prompt_len, num_requests,
+            model_override
+        )
     else:
-        # 用户点 Stop
         msg = stop_eval()
         yield msg, False, gr.update(value="Run Evaluation")
 
 
-# ⬇️⬇️⬇️ 互斥逻辑：同组保留最后一个选项 ⬇️⬇️⬇️
+# ---------------- 互斥逻辑 ----------------
 def enforce_input_exclusive_and_toggle_fields(selected):
     group1 = {"API Models", "Local Models"}
     group2 = {"Benchmarks", "Custom Datasets"}
@@ -140,10 +155,11 @@ def enforce_input_exclusive_and_toggle_fields(selected):
     )
 
 
-# ------------- 构建 Gradio UI -------------
+# ---------------- 构建 Gradio UI ----------------
 with gr.Blocks(title="EvalScope 全功能界面") as demo:
     is_running = gr.State(value=False)
 
+    # ===== 输入源 =====
     with gr.Group():
         with gr.Row():
             input_choices = gr.CheckboxGroup(
@@ -153,6 +169,7 @@ with gr.Blocks(title="EvalScope 全功能界面") as demo:
                 interactive=True
             )
 
+    # ===== API 地址 & Token =====
     with gr.Row(visible=False) as api_fields:
         api_url_input = gr.Textbox(
             label="API 地址",
@@ -164,6 +181,7 @@ with gr.Blocks(title="EvalScope 全功能界面") as demo:
             placeholder="sk-xxx"
         )
 
+    # ===== 本地/外部组件 =====
     with gr.Row():
         with gr.Column():
             native_choices = gr.CheckboxGroup(
@@ -178,12 +196,53 @@ with gr.Blocks(title="EvalScope 全功能界面") as demo:
                          "RAGAS", "MTEB/CMTEB"]
             )
 
+    # ===== 运行参数 =====
+    with gr.Accordion("运行参数（可选修改）", open=False):
         with gr.Row():
+            api_provider_dropdown = gr.Dropdown(
+                label="API Provider (--api)",
+                choices=["openai", "azure", "ollama", "gemini"],
+                value="openai"
+            )
+            dataset_dropdown = gr.Dropdown(
+                label="评测数据集 (--dataset)",
+                choices=["openqa", "gsm8k", "mmlu", "truthfulqa"],
+                value="openqa"
+            )
+        model_override_input = gr.Textbox(
+            label="自定义模型名 (--model)，留空则使用时间戳",
+            placeholder="e.g. my-llm-7b"
+        )
+        with gr.Row():
+            max_tokens_slider = gr.Slider(
+                label="Max Tokens (--max-tokens)",
+                minimum=256, maximum=8192, step=256, value=1024
+            )
+            min_tokens_slider = gr.Slider(
+                label="Min Tokens (--min-tokens)",
+                minimum=0, maximum=4096, step=64, value=1024
+            )
+        with gr.Row():
+            parallel_slider = gr.Slider(
+                label="并发请求数 (--parallel)",
+                minimum=1, maximum=16, step=1, value=1
+            )
+            num_req_slider = gr.Slider(
+                label="请求条数 (--number)",
+                minimum=1, maximum=1000, step=1, value=100
+            )
+        max_prompt_len_slider = gr.Slider(
+            label="最大 Prompt 长度 (--max-prompt-length)",
+            minimum=2048, maximum=32768, step=512, value=15360
+        )
+
+    # ===== 输出形式 =====
     output_choices = gr.CheckboxGroup(
         label="输出形式",
         choices=["Evaluation Report", "Gradio", "WandB", "Swanlab"]
     )
 
+    # ===== 控制按钮 & 日志 =====
     run_button = gr.Button("Run Evaluation")
     output_text = gr.TextArea(
         label="执行结果",
@@ -192,19 +251,24 @@ with gr.Blocks(title="EvalScope 全功能界面") as demo:
         show_copy_button=True
     )
 
-    # 绑定输入互斥
+    # ===== 绑定事件 =====
     input_choices.change(
         fn=enforce_input_exclusive_and_toggle_fields,
         inputs=input_choices,
         outputs=[input_choices, api_fields]
     )
 
-    # 绑定 Run/Stop
     run_button.click(
         fn=toggle_run,
         inputs=[
             input_choices, native_choices, other_choices,
-            output_choices, api_url_input, api_token_input, is_running
+            output_choices,
+            api_url_input, api_token_input,
+            api_provider_dropdown, dataset_dropdown,
+            max_tokens_slider, min_tokens_slider, parallel_slider,
+            max_prompt_len_slider, num_req_slider,
+            model_override_input,
+            is_running
         ],
         outputs=[output_text, is_running, run_button],
         show_progress=True