feat: 支持真实CSV数据训练与模型推理闭环

- 扩展TrainConfig，新增csv_path和target_col字段以支持外部数据
- 实现CSV数据集加载功能，动态解析特征名并适配训练流程
- 修改ReporterAgent以显示特征名而非索引，提升报告可读性
- 重构Web UI，增加数据上传、目标列配置及模型推理界面
- 更新README，强调完整闭环（训练+推理）和数据接入能力

README.md

@@ -5,23 +5,32 @@ sdk: docker
 app_port: 7860
 ---
 
-# 多 Agent 训练与调试小实验（闭环）
+# 多 Agent 训练与调试小实验（完整闭环版）
 
-一个“模型训练 + 调试 + 多 agent 协作”的小而美项目：用纯 Python（标准库）实现二分类 Logistic Regression 的训练流水线，并用多 agent 编排完成闭环： 
+一个“模型训练 + 调试 + 多 agent 协作 + 推理验证”的小而美项目：用纯 Python（标准库）实现二分类 Logistic Regression 的训练流水线，并用多 agent 编排完成闭环。
+**新增升级**：支持用户上传真实的 CSV 数据集，并在训练完成后进行实际的模型推理！
 
-数据生成 → 训练 → 评估 → 自动诊断（debug）→ 自动修复（调参）→ 复训 → 生成报告与可复现产物
+数据生成/读取 → 训练 → 评估 → 自动诊断（debug）→ 自动修复（调参）→ 复训 → 生成报告与可复现产物 → **模型推理**
 
 ## 你能看到的技术点
 
 - 多 agent 协作框架（消息总线 + 共享产物仓库 + 编排器）
+- 真实数据接入闭环（支持 CSV 动态解析）
 - 可复现训练（seed 固化、配置落盘、指标 JSONL 记录）
 - 训练稳定性调试（NaN/发散/过拟合/欠拟合的规则诊断与自动修复策略）
-- 训练产物闭环（runs/ 下保留：config、transcript、metrics、report）
+- 训练产物闭环（runs/ 下保留：config、transcript、metrics、report、模型参数）
+- **在线推理闭环（一键将最新模型拉起，进行数据预测）**
 
 ## 快速开始
 
 在本目录执行：
 
+```bash
+python3 app.py
+```
+
+或者使用命令行模式：
+
 ```bash
 python3 -m multi_agent_lab --scenario unstable
 ```
@@ -40,19 +49,20 @@ python3 -m multi_agent_lab --scenario stable
 python3 -m unittest discover -s tests -q
 ```
 
-## Hugging Face Spaces（Docker）
+## Web UI 说明
 
-Docker Space 启动后会在 7860 端口提供一个简单的 Web UI：
-- 一键跑 stable/unstable 场景（unstable 会先故意不稳定，再自动修复并复训）
-- 展示最新一次运行的 report.md
+执行 `python3 app.py` 启动后会在 7860 端口提供一个强大的 Web UI：
+- **模型训练与多 Agent 调试**：可一键跑 stable/unstable 场景，或者上传你自己的 `.csv` 数据集进行真实场景的模型训练。
+- **模型推理测试**：使用最新跑完的模型，输入逗号分隔的特征，即可预测出类别和概率。
 
 ## 目录结构
 
 - multi_agent_lab/：核心代码
   - agents/：不同角色 agent（数据、训练、评估、调试、报告）
-  - ml/：最小可用的训练实现（logreg + SGD + 指标）
+  - ml/：最小可用的训练与数据处理实现（logreg + SGD + CSV解析）
 - runs/：每次运行自动生成（可忽略、可清理）
 - tests/：最小冒烟测试
+- app.py: 启动包含训练与推理双向闭环的 Web UI
 
 ## 产物说明（runs/<run_id>/）
 
@@ -60,15 +70,5 @@ Docker Space 启动后会在 7860 端口提供一个简单的 Web UI：
 - transcript.jsonl：多 agent 消息流水（协作过程可回放）
 - metrics.jsonl：每个 epoch 的训练/验证指标
 - report.md / report.json：可读报告 + 结构化摘要
+- model.attemptX.json：模型权重与偏置，用于下游推理
 
-## 参数
-
-```bash
-python3 -m multi_agent_lab --help
-```
-
-常用：
-- --scenario：stable | unstable（默认 stable）
-- --seed：随机种子
-- --epochs：训练轮数
-- --lr：学习率（unstable 场景会覆盖为一个更容易发散的值，用于演示调试闭环）

app.py

@@ -10,20 +10,18 @@ except RuntimeError:
     asyncio.set_event_loop(asyncio.new_event_loop())
 
 import gradio as gr
+import math
 
 from multi_agent_lab.config import TrainConfig
 from multi_agent_lab.orchestrator import Orchestrator
 
-
 LOCK = threading.Lock()
 
-
 def _runs_dir() -> Path:
     d = Path("./data/runs")
     d.mkdir(parents=True, exist_ok=True)
     return d
 
-
 def _latest_run_dir() -> Path | None:
     base = _runs_dir()
     if not base.exists():
@@ -31,15 +29,19 @@ def _latest_run_dir() -> Path | None:
     runs = sorted([p for p in base.iterdir() if p.is_dir()], key=lambda p: p.name)
     return runs[-1] if runs else None
 
-
-def run_pipeline(scenario: str, seed: int, epochs: int, lr: float) -> tuple[str, str]:
+def run_pipeline(scenario: str, seed: int, epochs: int, lr: float, csv_file, target_col: str) -> tuple[str, str]:
     with LOCK:
-        cfg = TrainConfig(seed=seed, epochs=epochs, lr=lr)
+        csv_path = str(csv_file) if csv_file else None
+        target_col_val = target_col.strip() if target_col.strip() else "target"
+        
+        cfg = TrainConfig(seed=seed, epochs=epochs, lr=lr, csv_path=csv_path, target_col=target_col_val)
         orch = Orchestrator(base_runs_dir=_runs_dir())
-        store, summary = orch.run(cfg=cfg, scenario=scenario)
-        report = store.path("report.md").read_text(encoding="utf-8")
-        return str(store.run_dir), report
-
+        try:
+            store, summary = orch.run(cfg=cfg, scenario=scenario)
+            report = store.path("report.md").read_text(encoding="utf-8")
+            return str(store.run_dir), report
+        except Exception as e:
+            return "Error", f"**运行失败**：{str(e)}"
 
 def load_latest_report() -> tuple[str, str]:
     p = _latest_run_dir()
@@ -50,32 +52,74 @@ def load_latest_report() -> tuple[str, str]:
         return str(p), "未找到 report.md（可能运行未完成）。"
     return str(p), rp.read_text(encoding="utf-8")
 
+def run_inference(features_text: str) -> str:
+    p = _latest_run_dir()
+    if p is None:
+        return "暂无模型，请先完成一次训练。"
+        
+    try:
+        report_json = json.loads((p / "report.json").read_text(encoding="utf-8"))
+        attempts = report_json.get("attempts", 1)
+        model_json = json.loads((p / f"model.attempt{attempts}.json").read_text(encoding="utf-8"))
+    except Exception as e:
+        return f"无法加载模型数据：{str(e)}"
+        
+    w = model_json.get("w", [])
+    b = model_json.get("b", 0.0)
+    
+    try:
+        features = [float(x.strip()) for x in features_text.split(",") if x.strip()]
+    except ValueError:
+        return "输入格式错误，请确保输入的是用逗号分隔的数字。"
+        
+    if len(features) != len(w):
+        return f"特征数量不匹配：模型需要 {len(w)} 个特征，但输入了 {len(features)} 个。"
+        
+    # Logistic regression inference
+    margin = sum(x * weight for x, weight in zip(features, w)) + b
+    prob = 1.0 / (1.0 + math.exp(-margin))
+    pred = 1 if prob >= 0.5 else 0
+    
+    return f"**预测类别**：{pred}\n\n**正类概率**：{prob:.4f}\n**Margin**：{margin:.4f}"
 
 with gr.Blocks(title="多 Agent 训练与调试实验室") as demo:
-    gr.Markdown("# 多 Agent 训练与调试实验室\n\n一键演示：数据→训练→评估→自动诊断→自动修复→复训→报告。")
-
-    with gr.Row():
-        scenario = gr.Dropdown(choices=["stable", "unstable"], value="unstable", label="场景")
-        seed = gr.Number(value=42, precision=0, label="seed")
-        epochs = gr.Number(value=20, precision=0, label="epochs")
-        lr = gr.Number(value=0.2, label="lr（stable 场景有效；unstable 会先覆盖成易发散值）")
-
-    with gr.Row():
-        btn_run = gr.Button("开始运行")
-        btn_load = gr.Button("加载最新报告")
-
-    out_run_dir = gr.Textbox(label="run_dir", interactive=False)
-    out_report = gr.Markdown(label="report.md")
-
-    btn_run.click(
-        fn=run_pipeline,
-        inputs=[scenario, seed, epochs, lr],
-        outputs=[out_run_dir, out_report],
-    )
-    btn_load.click(fn=load_latest_report, inputs=[], outputs=[out_run_dir, out_report])
-
-    demo.load(fn=load_latest_report, inputs=[], outputs=[out_run_dir, out_report])
-
+    gr.Markdown("# 多 Agent 训练与调试实验室\n\n一键演示：数据→训练→评估→自动诊断→自动修复→复训→报告。\n\n**新增功能**：支持上传真实 CSV 数据进行训练，并提供推理闭环！")
+
+    with gr.Tabs():
+        with gr.Tab("1. 模型训练与多 Agent 调试"):
+            with gr.Row():
+                with gr.Column():
+                    scenario = gr.Dropdown(choices=["stable", "unstable"], value="unstable", label="场景")
+                    seed = gr.Number(value=42, precision=0, label="seed")
+                    epochs = gr.Number(value=20, precision=0, label="epochs")
+                    lr = gr.Number(value=0.2, label="lr（stable 场景有效；unstable 会先覆盖成易发散值）")
+                with gr.Column():
+                    csv_file = gr.File(label="上传 CSV 数据集 (可选，不传则使用内置合成数据)", file_types=[".csv"], type="filepath")
+                    target_col = gr.Textbox(value="target", label="目标列名 (例如：target)")
+
+            with gr.Row():
+                btn_run = gr.Button("开始运行", variant="primary")
+                btn_load = gr.Button("加载最新报告")
+
+            out_run_dir = gr.Textbox(label="run_dir", interactive=False)
+            out_report = gr.Markdown(label="report.md")
+
+            btn_run.click(
+                fn=run_pipeline,
+                inputs=[scenario, seed, epochs, lr, csv_file, target_col],
+                outputs=[out_run_dir, out_report],
+            )
+            btn_load.click(fn=load_latest_report, inputs=[], outputs=[out_run_dir, out_report])
+            demo.load(fn=load_latest_report, inputs=[], outputs=[out_run_dir, out_report])
+            
+        with gr.Tab("2. 模型推理测试"):
+            gr.Markdown("### 使用最新训练的模型进行推理")
+            gr.Markdown("请输入逗号分隔的特征值。例如：`1.5, -0.2, 3.1, 0.8, -1.1`")
+            feat_input = gr.Textbox(label="特征输入 (逗号分隔)", lines=3)
+            btn_infer = gr.Button("执行推理", variant="primary")
+            infer_result = gr.Markdown(label="推理结果")
+            
+            btn_infer.click(fn=run_inference, inputs=[feat_input], outputs=[infer_result])
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)

multi_agent_lab/agents/data_agent.py

@@ -4,7 +4,7 @@ from dataclasses import asdict
 
 from .base import BaseAgent
 from ..config import TrainConfig
-from ..ml.dataset import Dataset, make_synthetic_binary_classification
+from ..ml.dataset import Dataset, make_synthetic_binary_classification, load_csv_dataset
 
 
 class DataAgent(BaseAgent):
@@ -12,21 +12,37 @@ class DataAgent(BaseAgent):
 
     def run(self, *, cfg: TrainConfig) -> Dataset:
         self.emit(receiver="MessageBus", kind="stage", payload={"stage": "data", "cfg": cfg.to_dict()})
-        ds = make_synthetic_binary_classification(
-            seed=cfg.seed,
-            n_samples=cfg.n_samples,
-            n_features=cfg.n_features,
-            train_ratio=cfg.train_ratio,
-        )
+        
+        if cfg.csv_path:
+            ds = load_csv_dataset(
+                csv_path=cfg.csv_path,
+                target_col=cfg.target_col,
+                seed=cfg.seed,
+                train_ratio=cfg.train_ratio,
+            )
+            # Update config based on loaded data
+            n_features = len(ds.feature_names) if ds.feature_names else 0
+            cfg_dict = cfg.to_dict()
+            cfg_dict["n_features"] = n_features
+            cfg_dict["n_samples"] = len(ds.x_train) + len(ds.x_val)
+        else:
+            ds = make_synthetic_binary_classification(
+                seed=cfg.seed,
+                n_samples=cfg.n_samples,
+                n_features=cfg.n_features,
+                train_ratio=cfg.train_ratio,
+            )
+            n_features = cfg.n_features
 
         self.ctx.store.write_json(
             "dataset.json",
             {
                 "n_train": len(ds.x_train),
                 "n_val": len(ds.x_val),
-                "n_features": cfg.n_features,
+                "n_features": n_features,
                 "label_rate_train": sum(ds.y_train) / max(1, len(ds.y_train)),
                 "label_rate_val": sum(ds.y_val) / max(1, len(ds.y_val)),
+                "feature_names": ds.feature_names
             },
         )
         self.ctx.store.write_json("true_params.json", {"true_w": ds.true_w, "true_b": ds.true_b})
@@ -34,7 +50,7 @@ class DataAgent(BaseAgent):
         self.emit(
             receiver="Orchestrator",
             kind="data_ready",
-            payload={"n_train": len(ds.x_train), "n_val": len(ds.x_val), "n_features": cfg.n_features},
+            payload={"n_train": len(ds.x_train), "n_val": len(ds.x_val), "n_features": n_features},
         )
         return ds
 

multi_agent_lab/agents/reporter_agent.py

@@ -18,7 +18,13 @@ class ReporterAgent(BaseAgent):
         pairs = list(enumerate(w))
         pairs.sort(key=lambda x: abs(float(x[1])), reverse=True)
         top_k = max(1, int(cfg.report_top_k_features))
-        top = [{"feature": int(i), "weight": float(v)} for i, v in pairs[:top_k]]
+        
+        feature_names = getattr(dataset, "feature_names", None)
+        
+        top = []
+        for i, v in pairs[:top_k]:
+            fname = feature_names[i] if feature_names and i < len(feature_names) else str(i)
+            top.append({"feature": fname, "weight": float(v)})
 
         report_obj = {
             "run_dir": str(self.ctx.store.run_dir),

multi_agent_lab/config.py

@@ -16,6 +16,9 @@ class TrainConfig:
     grad_clip: Optional[float] = None
     loss_eps: float = 1e-12
     report_top_k_features: int = 8
+    
+    csv_path: Optional[str] = None
+    target_col: str = "target"
 
     def to_dict(self) -> Dict[str, Any]:
         return asdict(self)

multi_agent_lab/ml/dataset.py

@@ -1,9 +1,9 @@
 from __future__ import annotations
 
+import csv
 from dataclasses import dataclass
 from random import Random
-from typing import List, Tuple
-
+from typing import List, Tuple, Dict, Any
 
 @dataclass(frozen=True)
 class Dataset:
@@ -13,11 +13,62 @@ class Dataset:
     y_val: List[int]
     true_w: List[float]
     true_b: float
-
+    feature_names: List[str] = None
 
 def _dot(a: List[float], b: List[float]) -> float:
     return sum(x * y for x, y in zip(a, b))
 
+def load_csv_dataset(*, csv_path: str, target_col: str, seed: int, train_ratio: float) -> Dataset:
+    with open(csv_path, "r", encoding="utf-8") as f:
+        reader = csv.reader(f)
+        header = next(reader)
+        try:
+            target_idx = header.index(target_col)
+        except ValueError:
+            target_idx = -1 # default to last column if not found
+            target_col = header[-1]
+            
+        feature_names = [col for i, col in enumerate(header) if i != target_idx]
+        
+        xs: List[List[float]] = []
+        ys: List[int] = []
+        for row in reader:
+            if not row:
+                continue
+            # try parsing
+            try:
+                y = int(float(row[target_idx]))
+                x = [float(val) for i, val in enumerate(row) if i != target_idx]
+                xs.append(x)
+                ys.append(y)
+            except ValueError:
+                continue # skip invalid rows
+                
+    n_samples = len(xs)
+    if n_samples == 0:
+        raise ValueError("No valid data found in CSV.")
+        
+    idx = list(range(n_samples))
+    r = Random(seed)
+    r.shuffle(idx)
+    cut = max(1, min(n_samples - 1, int(n_samples * train_ratio)))
+    train_idx = idx[:cut]
+    val_idx = idx[cut:]
+    
+    x_train = [xs[i] for i in train_idx]
+    y_train = [ys[i] for i in train_idx]
+    x_val = [xs[i] for i in val_idx]
+    y_val = [ys[i] for i in val_idx]
+    
+    return Dataset(
+        x_train=x_train,
+        y_train=y_train,
+        x_val=x_val,
+        y_val=y_val,
+        true_w=[],
+        true_b=0.0,
+        feature_names=feature_names
+    )
 
 def make_synthetic_binary_classification(
     *, seed: int, n_samples: int, n_features: int, train_ratio: float
@@ -46,6 +97,8 @@ def make_synthetic_binary_classification(
     y_train = [ys[i] for i in train_idx]
     x_val = [xs[i] for i in val_idx]
     y_val = [ys[i] for i in val_idx]
+    
+    feature_names = [f"feature_{i}" for i in range(n_features)]
 
     return Dataset(
         x_train=x_train,
@@ -54,5 +107,6 @@ def make_synthetic_binary_classification(
         y_val=y_val,
         true_w=true_w,
         true_b=true_b,
+        feature_names=feature_names
     )
 

sample_data.csv

@@ -0,0 +1,501 @@
+feature_0,feature_1,feature_2,feature_3,feature_4,target
+-2.3468363405322377,1.4867755355330141,0.36214665017389464,-1.7242796473619575,-1.3486100207272043,0
+-1.887519768477663,2.074467083235396,-1.6690419292832792,-0.2741587864200055,0.9209298338775862,0
+1.3803138964143644,0.4095448993103014,-2.120465089526448,-0.6696767271957258,-0.11034911626218555,0
+1.3141962575721207,-0.24073240673411203,-0.8477168460099764,1.4070880601866445,-2.120148676156491,1
+-0.2936424421827941,1.3634399245291309,0.48993937313499564,2.103194716799419,0.5121426548122502,1
+0.6944751642619672,0.0005039451722672428,0.744386496307233,1.1234823509683671,-0.31912364559912587,1
+1.6673653979063394,0.34168962308846923,-0.7665395180422613,-1.3494401812599384,0.9691786448138914,1
+0.6481768221723694,0.20362557976061,-0.670454763752551,-0.45531018809836604,-0.03110857588324539,0
+0.23407997414868098,-0.6613351128348073,0.6618487509250924,0.7643211745440954,0.25946807504936853,1
+0.12047786502704581,0.1631412912740077,0.2459217827886572,-1.557437726548158,-1.578144306068366,1
+-1.449993289553797,-0.9286645614696276,-1.416646579792145,-0.4259982668672846,0.4513221499088889,1
+0.7371400911961541,2.106378857483204,1.5159286452177367,-2.2172342899801296,-1.2339677598418564,0
+1.0668073779905354,-2.0649475521534697,0.07158112884147223,1.0764453773401423,0.8336857698293755,1
+-0.9114522515798462,-1.179524420147439,0.41496210524522964,-1.7939802719875688,1.3516116934438673,1
+-0.3386973028932461,0.8591917638025413,-0.36853382917908756,1.7040644249500434,-0.6146781237603604,0
+0.20536763745917688,0.7291671649423542,0.2707136730267819,1.7513104095455234,-1.8711796080402732,1
+1.5262036340110838,0.822468567099911,0.646471206858607,0.5445615937349866,1.0801056360751944,0
+-0.24780754133461147,1.4944086913822938,-0.6706630791658991,-0.5461528408468102,0.3775014538653653,1
+-0.46726065502164366,-0.15930929186479034,-0.9578455966358435,0.16293560017044378,0.26784200510273454,1
+1.8609452254609142,-0.2583446621042438,0.7455027553887055,0.6426432676845873,0.9535742702367482,0
+1.7235045335125103,1.3103805431897007,-0.5697013262309529,-0.5642679945468686,0.45271079406267034,1
+-0.773148667333396,0.004177329125527271,-2.185443708812737,-0.7062695975483689,0.0888927891549404,0
+-0.5254697146753308,-0.9702438166683606,1.3000299185785968,0.806396642845671,-0.3374348216195167,0
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