98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
import argparse
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import json
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import os
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import sys
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import pandas as pd
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import streamlit as st
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from agentic.data_analysis_env import extract_final_answer, score_agentic_trajectory
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from dataset.agentic_dataset import load_agentic_jsonl
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DEFAULT_DATA = "dataset/agentic_data/dev.jsonl"
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def trajectory_to_text(sample):
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chunks = []
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for item in sample.get("expert_trajectory", []):
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if item.get("role") == "assistant" and item.get("tool_call"):
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chunks.append("<tool_call>\n" + json.dumps(item["tool_call"], ensure_ascii=False, indent=2) + "\n</tool_call>")
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elif item.get("role") == "assistant":
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chunks.append(str(item.get("content", "")))
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return "\n---\n".join(chunks)
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def parse_turns(text):
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if "\n---\n" in text:
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return [part.strip() for part in text.split("\n---\n") if part.strip()]
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return [text.strip()] if text.strip() else []
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def parse_args():
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parser = argparse.ArgumentParser(description="Agentic DataAnalysis Streamlit demo")
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parser.add_argument("--data", default=DEFAULT_DATA)
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parser.add_argument("--repo_root", default=".")
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return parser.parse_args()
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def main():
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args = parse_args()
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st.set_page_config(page_title="Agentic DataAnalysis", layout="wide")
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st.title("Agentic DataAnalysis")
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rows = load_agentic_jsonl(args.data) if os.path.exists(args.data) else []
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if not rows:
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st.error(f"数据不存在或为空：{args.data}")
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return
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sample_ids = [row.get("id", str(i)) for i, row in enumerate(rows)]
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selected = st.sidebar.selectbox("样本", sample_ids)
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sample = rows[sample_ids.index(selected)]
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st.subheader("问题")
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st.write(sample.get("question", ""))
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left, right = st.columns([1.1, 1.0])
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with left:
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st.subheader("数据表")
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st.dataframe(pd.DataFrame(sample.get("tables", [])), use_container_width=True)
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st.subheader("指标文档")
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st.dataframe(pd.DataFrame(sample.get("documents", [])), use_container_width=True)
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with right:
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st.subheader("标准答案")
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st.write(sample.get("answer", ""))
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st.json(sample.get("checks", {}))
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st.subheader("期望工具")
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st.write(", ".join(sample.get("expected_tools", [])))
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st.subheader("轨迹与奖励")
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default_text = trajectory_to_text(sample)
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trajectory_text = st.text_area("轨迹文本（用 --- 分隔多个 assistant turn 可手动评测模型输出）", value=default_text, height=360)
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turn_outputs = [
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part
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for part in parse_turns(trajectory_text)
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if "<tool_response>" not in part
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]
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reward, parts = score_agentic_trajectory(turn_outputs, sample, repo_root=args.repo_root)
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c1, c2, c3, c4, c5 = st.columns(5)
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c1.metric("Reward", f"{reward:.3f}")
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c2.metric("Success", f"{parts['task_success']:.3f}")
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c3.metric("Tool F1", f"{parts['tool_selection_f1']:.3f}")
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c4.metric("Invalid", f"{parts['invalid_action_rate']:.3f}")
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c5.metric("Turns", f"{parts['avg_turns']:.1f}")
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st.subheader("Reward 分解")
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st.dataframe(pd.DataFrame([parts]).T.rename(columns={0: "score"}), use_container_width=True)
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st.subheader("Final Answer")
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st.write(extract_final_answer(turn_outputs))
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if __name__ == "__main__":
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main()
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