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@@ -9228,3 +9228,5 @@ images/images/texture/true/t2i_r1_run_03956_00000.png filter=lfs diff=lfs merge=
 images/images/texture/true/t2i_r1_run_04173_00009.png filter=lfs diff=lfs merge=lfs -text
 images/images/texture/true/t2i_r1_run_04271_00005.png filter=lfs diff=lfs merge=lfs -text
 images/images/texture/true/t2i_r1_run_04307_00003.png filter=lfs diff=lfs merge=lfs -text
+reflectionbench_demo/detailed_cases_qwen235b.json filter=lfs diff=lfs merge=lfs -text
+reflectionbench_demo/detailed_cases_qwen.json filter=lfs diff=lfs merge=lfs -text

reflectionbench_demo/app.py

@@ -0,0 +1,349 @@
+#!/usr/bin/env python3
+"""
+ReflectionBench Case 展示 Gradio 应用 (HuggingFace Spaces 版本 v2)
+
+启动时自动解压 images.zip 和 edited_images.zip
+"""
+
+import gradio as gr
+import json
+import zipfile
+from pathlib import Path
+from PIL import Image
+import numpy as np
+from typing import Dict, List, Optional
+
+# ============================================================================
+# 初始化：解压图像文件
+# ============================================================================
+
+CURRENT_DIR = Path(__file__).parent
+
+def extract_if_needed():
+    """如果 zip 文件存在且未解压，则解压"""
+    for zip_name in ["images.zip", "edited_images.zip"]:
+        zip_path = CURRENT_DIR / zip_name
+        extract_dir = CURRENT_DIR / zip_name.replace(".zip", "")
+
+        if zip_path.exists() and not extract_dir.exists():
+            print(f"Extracting {zip_name}...")
+            with zipfile.ZipFile(zip_path, 'r') as zf:
+                zf.extractall(CURRENT_DIR)
+            print(f"  Done: {extract_dir}")
+
+# 启动时解压
+extract_if_needed()
+
+# ============================================================================
+# 配置
+# ============================================================================
+
+EVAL_MODELS = {
+    "qwen": "qwen_eval",
+    "qwen235b": "qwen235b_eval",
+}
+
+BASELINE_MODELS = [
+    "qwen_ft_v1_step900",
+    "qwen_ft_v3_step730",
+    "qwen_ft_v4_step765",
+    "qwen_ft_v5_step920",
+    "qwen_ft_v6_step910",
+    "qwen_ft_v7_step445",
+    "qwen_ft_v8_step365",
+    "qwen_ft_v8_step795",
+]
+
+COMPARISON_MODELS = [
+    "qwen",
+    "qwen3vl",
+    "qwen3vl_thinking",
+    "bagel",
+    "omnigen2",
+    "omniverifier",
+    "unicot",
+    "sld",
+    "reflect_dit",
+    "reflectionflow_qwen8b",
+    "thinkgen",
+    "reasonedit",
+]
+
+EDITOR_CONFIG = {
+    "qwen_ft_v1_step900": "qwen_image_2511",
+    "qwen_ft_v3_step730": "qwen_image_2511",
+    "qwen_ft_v4_step765": "qwen_image_2511",
+    "qwen_ft_v5_step920": "qwen_image_2511",
+    "qwen_ft_v6_step910": "qwen_image_2511",
+    "qwen_ft_v7_step445": "qwen_image_2511",
+    "qwen_ft_v8_step365": "qwen_image_2511",
+    "qwen_ft_v8_step795": "qwen_image_2511",
+    "qwen": "qwen_image_2511",
+    "qwen3vl": "qwen_image_2511",
+    "qwen3vl_thinking": "qwen_image_2511",
+    "omniverifier": "qwen_image_2511",
+    "sld": "qwen_image_2511",
+    "bagel": "bagel",
+    "omnigen2": "omnigen2",
+    "unicot": "unicot",
+    "reflect_dit": "reflect_dit",
+    "reflectionflow_qwen8b": "reflectionflow",
+    "thinkgen": "thinkgen",
+    "reasonedit": "reasonedit",
+}
+
+CASE_TYPE_NAMES = {
+    "type1_answer_wrong": "Type 1: Answer 错误",
+    "type2_explanation_wrong": "Type 2: Explanation 错误→编辑失败",
+    "type3_edit_better": "Type 3: Edit Prompt 更优",
+}
+
+
+# ============================================================================
+# 数据加载
+# ============================================================================
+
+def load_cases(eval_model_key: str) -> Dict:
+    filename = f"detailed_cases_{eval_model_key}.json"
+    filepath = CURRENT_DIR / filename
+    if filepath.exists():
+        with open(filepath, "r", encoding="utf-8") as f:
+            return json.load(f)
+    return {}
+
+_cases_cache = {}
+
+def get_cases(eval_model_key: str) -> Dict:
+    if eval_model_key not in _cases_cache:
+        _cases_cache[eval_model_key] = load_cases(eval_model_key)
+    return _cases_cache[eval_model_key]
+
+
+# ============================================================================
+# 图片处理
+# ============================================================================
+
+def load_image_256(path: Path) -> Optional[np.ndarray]:
+    if path and path.exists():
+        try:
+            img = Image.open(path)
+            img.thumbnail((256, 256), Image.Resampling.LANCZOS)
+            return np.array(img)
+        except Exception:
+            pass
+    return None
+
+
+def get_bad_image_path(bad_image_rel: str) -> Path:
+    return CURRENT_DIR / "images" / bad_image_rel
+
+
+def get_edited_image_path(bad_image_rel: str, verifier: str, editor: str) -> Path:
+    bad_image_path_obj = Path(bad_image_rel)
+    filename = f"{editor}_{verifier}_{bad_image_path_obj.stem}{bad_image_path_obj.suffix}"
+    return CURRENT_DIR / "edited_images" / filename
+
+
+# ============================================================================
+# Gradio 回调函数
+# ============================================================================
+
+def get_case_list(eval_model_key: str, baseline: str, comparison: str, case_type: str) -> List[str]:
+    cases_data = get_cases(eval_model_key)
+    if not cases_data:
+        return []
+    cases = cases_data.get(baseline, {}).get(comparison, {}).get(case_type, [])
+    return [f"idx={c['idx']} | {c['category']} | {c['original_prompt'][:40]}..." for c in cases]
+
+
+def update_case_list(eval_model_key: str, baseline: str, comparison: str, case_type: str):
+    cases = get_case_list(eval_model_key, baseline, comparison, case_type)
+    return gr.update(choices=cases, value=cases[0] if cases else None)
+
+
+def get_comparison_choices(eval_model_key: str, baseline: str) -> List[str]:
+    cases_data = get_cases(eval_model_key)
+    if not cases_data or baseline not in cases_data:
+        return COMPARISON_MODELS
+    available = []
+    for comp in COMPARISON_MODELS:
+        comp_data = cases_data.get(baseline, {}).get(comp, {})
+        total = sum(len(comp_data.get(t, [])) for t in CASE_TYPE_NAMES.keys())
+        if total > 0:
+            available.append(comp)
+    return available if available else COMPARISON_MODELS
+
+
+def update_comparison_choices(eval_model_key: str, baseline: str):
+    choices = get_comparison_choices(eval_model_key, baseline)
+    return gr.update(choices=choices, value=choices[0] if choices else None)
+
+
+def show_case(eval_model_key: str, baseline: str, comparison: str, case_type: str, case_idx_str: str):
+    empty_result = ("请选择 case", "", "", "", "", "", None, None, None)
+
+    if not case_idx_str:
+        return empty_result
+
+    cases_data = get_cases(eval_model_key)
+    if not cases_data:
+        return ("数据未加载", "", "", "", "", "", None, None, None)
+
+    try:
+        idx = int(case_idx_str.split("|")[0].replace("idx=", "").strip())
+    except (ValueError, IndexError):
+        return ("无法解析 idx", "", "", "", "", "", None, None, None)
+
+    cases = cases_data.get(baseline, {}).get(comparison, {}).get(case_type, [])
+    case = next((c for c in cases if c["idx"] == idx), None)
+
+    if not case:
+        return ("未找到 case", "", "", "", "", "", None, None, None)
+
+    bad_img = load_image_256(get_bad_image_path(case["bad_image"]))
+    baseline_editor = case.get("baseline_editor", EDITOR_CONFIG.get(baseline, "qwen_image_2511"))
+    comparison_editor = case.get("comparison_editor", EDITOR_CONFIG.get(comparison, "qwen_image_2511"))
+
+    baseline_img = load_image_256(get_edited_image_path(case["bad_image"], baseline, baseline_editor))
+    comparison_img = load_image_256(get_edited_image_path(case["bad_image"], comparison, comparison_editor))
+
+    info_col = f"""### Case 详情
+- **评估模型**: {eval_model_key.upper()}
+- **类型**: {CASE_TYPE_NAMES.get(case_type, case_type)}
+- **idx**: {case["idx"]} | **类别**: {case["category"]}
+- **Prompt**: *{case["original_prompt"]}*"""
+
+    gt_col = f"""### Ground Truth
+- **Answer**: `{case["gt_answer"]}`
+- **Explanation**: {case.get("gt_explanation", "N/A")}"""
+
+    baseline_col = f"""### {baseline} (基准)
+- **Answer**: `{case["baseline_answer"]}` {"✓" if case["baseline_answer_correct"] else "✗"} | **Exp评估**: {"✓" if case["baseline_explanation_correct"] else "✗"}
+- **Explanation**: {case["baseline_explanation"]}
+- **Edit指令**: {case["baseline_edit_prompt"]}
+- **I_Score**: **{case["baseline_i_score"]:.3f}** | Edited Acc: **{case["baseline_edited_acc"]:.3f}**"""
+
+    comp_col = f"""### {comparison} (对比)
+- **Answer**: `{case["comparison_answer"]}` {"✓" if case["comparison_answer_correct"] else "✗"} | **Exp评估**: {"✓" if case["comparison_explanation_correct"] else "✗"}
+- **Explanation**: {case["comparison_explanation"] if case["comparison_explanation"] else "—"}
+- **Edit指令**: {case["comparison_edit_prompt"] if case["comparison_edit_prompt"] else "—"}
+- **I_Score**: **{case["comparison_i_score"]:.3f}** | Edited Acc: **{case["comparison_edited_acc"]:.3f}**"""
+
+    i_diff = case["baseline_i_score"] - case["comparison_i_score"]
+    ans_adv = "✓" if case["baseline_answer_correct"] and not case["comparison_answer_correct"] else "-"
+    exp_adv = "✓" if case["baseline_explanation_correct"] and not case["comparison_explanation_correct"] else "-"
+    summary = f"""**对比总结**: Answer({ans_adv}) | Explanation({exp_adv}) | I_Score: {baseline} **{case["baseline_i_score"]:.3f}** vs {comparison} {case["comparison_i_score"]:.3f} = **+{i_diff:.3f}** | Edited Acc: **{case["baseline_edited_acc"]:.3f}** vs {case["comparison_edited_acc"]:.3f}"""
+
+    img_labels = f"原图 → {baseline} 编辑 ({baseline_editor}) → {comparison} 编辑 ({comparison_editor})"
+
+    return (info_col, gt_col, baseline_col, comp_col, summary, img_labels, bad_img, baseline_img, comparison_img)
+
+
+def get_statistics(eval_model_key: str) -> str:
+    cases_data = get_cases(eval_model_key)
+    if not cases_data:
+        return "数据未加载"
+
+    lines = [f"### 统计摘要 ({eval_model_key.upper()})\n"]
+
+    for baseline in BASELINE_MODELS:
+        if baseline not in cases_data:
+            continue
+        lines.append(f"\n**{baseline}**:\n")
+        lines.append("| 对比模型 | Type1 | Type2 | Type3 | 总计 |")
+        lines.append("|----------|-------|-------|-------|------|")
+
+        for comparison in COMPARISON_MODELS:
+            comp_data = cases_data.get(baseline, {}).get(comparison, {})
+            t1 = len(comp_data.get("type1_answer_wrong", []))
+            t2 = len(comp_data.get("type2_explanation_wrong", []))
+            t3 = len(comp_data.get("type3_edit_better", []))
+            total = t1 + t2 + t3
+            if total > 0:
+                lines.append(f"| {comparison} | {t1} | {t2} | {t3} | {total} |")
+
+    return "\n".join(lines)
+
+
+# ============================================================================
+# Gradio 界面
+# ============================================================================
+
+def create_app():
+    with gr.Blocks(title="ReflectionBench Case Viewer", theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# ReflectionBench Case 展示")
+        gr.Markdown("对比 Reflector (基准模型) 与其他 baseline 模型的表现差异")
+
+        with gr.Accordion("统计摘要", open=False):
+            stats_md = gr.Markdown()
+
+        with gr.Row():
+            eval_model = gr.Radio(choices=list(EVAL_MODELS.keys()), label="评估模型", value="qwen", scale=1)
+            baseline = gr.Dropdown(choices=BASELINE_MODELS, label="基准模型 (Reflector)", value=BASELINE_MODELS[0], scale=1)
+            comparison = gr.Dropdown(choices=COMPARISON_MODELS, label="对比模型", value=COMPARISON_MODELS[0], scale=1)
+
+        with gr.Row():
+            case_type = gr.Radio(
+                choices=[
+                    ("Type1: Answer错误", "type1_answer_wrong"),
+                    ("Type2: Exp错误→编辑失败", "type2_explanation_wrong"),
+                    ("Type3: Edit更优", "type3_edit_better"),
+                ],
+                label="Case类型", value="type1_answer_wrong", scale=2
+            )
+            case_dropdown = gr.Dropdown(choices=[], label="选择Case", scale=2)
+
+        with gr.Row():
+            info_md = gr.Markdown()
+            gt_md = gr.Markdown()
+
+        with gr.Row():
+            baseline_md = gr.Markdown()
+            comparison_md = gr.Markdown()
+
+        summary_md = gr.Markdown()
+        img_label = gr.Markdown()
+
+        with gr.Row():
+            bad_image = gr.Image(label="原图", scale=1, height=200)
+            baseline_edited = gr.Image(label="基准模型编辑后", scale=1, height=200)
+            comparison_edited = gr.Image(label="对比模型编辑后", scale=1, height=200)
+
+        outputs = [info_md, gt_md, baseline_md, comparison_md, summary_md, img_label, bad_image, baseline_edited, comparison_edited]
+
+        def on_eval_model_change(eval_key, base, comp, ctype):
+            stats = get_statistics(eval_key)
+            comp_update = update_comparison_choices(eval_key, base)
+            case_update = update_case_list(eval_key, base, comp, ctype)
+            return stats, comp_update, case_update
+
+        eval_model.change(fn=on_eval_model_change, inputs=[eval_model, baseline, comparison, case_type], outputs=[stats_md, comparison, case_dropdown])
+
+        def on_baseline_change(eval_key, base, comp, ctype):
+            comp_update = update_comparison_choices(eval_key, base)
+            case_update = update_case_list(eval_key, base, comp, ctype)
+            return comp_update, case_update
+
+        baseline.change(fn=on_baseline_change, inputs=[eval_model, baseline, comparison, case_type], outputs=[comparison, case_dropdown])
+        comparison.change(fn=update_case_list, inputs=[eval_model, baseline, comparison, case_type], outputs=[case_dropdown])
+        case_type.change(fn=update_case_list, inputs=[eval_model, baseline, comparison, case_type], outputs=[case_dropdown])
+        case_dropdown.change(fn=show_case, inputs=[eval_model, baseline, comparison, case_type, case_dropdown], outputs=outputs)
+
+        def on_load(eval_key, base, comp, ctype):
+            stats = get_statistics(eval_key)
+            cases = get_case_list(eval_key, base, comp, ctype)
+            case_val = cases[0] if cases else None
+            if case_val:
+                case_result = show_case(eval_key, base, comp, ctype, case_val)
+            else:
+                case_result = ("请选择 case", "", "", "", "", "", None, None, None)
+            return (stats, gr.update(choices=cases, value=case_val)) + case_result
+
+        demo.load(fn=on_load, inputs=[eval_model, baseline, comparison, case_type], outputs=[stats_md, case_dropdown] + outputs)
+
+    return demo
+
+
+if __name__ == "__main__":
+    demo = create_app()
+    demo.launch()

reflectionbench_demo/detailed_cases_qwen.json

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+size 31125455

reflectionbench_demo/detailed_cases_qwen235b.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a0ab8a89ba7b98768f607a9ca9ebe3d369284dd6f5cbe5c0e6dfabc752f8487c
+size 34145044

reflectionbench_demo/edited_images.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b48c25bc80055debf7b7310a7071cba2f2b18ac7fbc63115462b3823bbe07454
+size 8475442818

reflectionbench_demo/images.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:21502d845a90717f1d3f19cba06aaadd4272e51acb1dfd1d0120b3c62a8fd44f
+size 681831366

reflectionbench_demo/requirements.txt

@@ -0,0 +1,3 @@
+gradio>=4.0.0
+Pillow>=9.0.0
+numpy>=1.20.0