更新app.py，删除metadata.csv，添加test.py

app.py

@@ -7,115 +7,330 @@ import gradio as gr
 os.environ["GRADIO_SSR_MODE"] = "False"    # 关掉 SSR
 
 # 确保 data 目录及子目录存在
-os.makedirs("data/images", exist_ok=True)
+os.makedirs("data/images/task0/orig_imgs", exist_ok=True)
+os.makedirs("data/images/task0/processed_imgs", exist_ok=True)
+os.makedirs("data/images/task1/orig_imgs", exist_ok=True)
+os.makedirs("data/images/task1/processed_imgs", exist_ok=True)
+os.makedirs("data/images/task2/orig_imgs", exist_ok=True)
+os.makedirs("data/images/task2/processed_imgs", exist_ok=True)
+os.makedirs("data/images/task3/orig_imgs", exist_ok=True)
+os.makedirs("data/images/task3/processed_imgs", exist_ok=True)
+os.makedirs("data/images/task4/orig_imgs", exist_ok=True)
+os.makedirs("data/images/task4/processed_imgs", exist_ok=True)
+os.makedirs("data/images/task5/orig_imgs", exist_ok=True)
+os.makedirs("data/images/task5/processed_imgs", exist_ok=True)
+os.makedirs("data/images/task6/orig_imgs", exist_ok=True)
+os.makedirs("data/images/task6/processed_imgs", exist_ok=True)
 
-# metadata 文件：保存每次 run 的原图、prompt、agent、结果路径
-METADATA_FILE = "data/metadata.csv"
-if not os.path.exists(METADATA_FILE):
-    with open(METADATA_FILE, "w", newline="", encoding="utf-8") as f:
+# 在文件开头添加必要的目录创建
+os.makedirs("data/evaluations", exist_ok=True)
+os.makedirs("data/metadatas", exist_ok=True)
+
+meta0 = "data/metadatas/meta0.csv"
+meta1 = "data/metadatas/meta1.csv"
+meta2 = "data/metadatas/meta2.csv"
+meta3 = "data/metadatas/meta3.csv"
+meta4 = "data/metadatas/meta4.csv"
+meta5 = "data/metadatas/meta5.csv"
+meta6 = "data/metadatas/meta6.csv"
+
+if not os.path.exists(meta0):
+    with open(meta0, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=[
+            "id","original_path","prompt",
+            "agent1","img1_path","agent2","img2_path"
+        ])
+        writer.writeheader()
+        
+if not os.path.exists(meta1):
+    with open(meta1, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=[
+            "id","original_path","prompt",
+            "agent1","img1_path","agent2","img2_path"
+        ])
+        writer.writeheader()
+
+if not os.path.exists(meta2):
+    with open(meta2, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=[
+            "id","original_path","prompt",
+            "agent1","img1_path","agent2","img2_path"
+        ])
+        writer.writeheader()
+
+if not os.path.exists(meta3):
+    with open(meta3, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=[
+            "id","original_path","prompt",
+            "agent1","img1_path","agent2","img2_path"
+        ])
+        writer.writeheader()
+
+if not os.path.exists(meta4):
+    with open(meta4, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=[
+            "id","original_path","prompt",
+            "agent1","img1_path","agent2","img2_path"
+        ])
+        writer.writeheader()
+
+if not os.path.exists(meta5):
+    with open(meta5, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=[
+            "id","original_path","prompt",
+            "agent1","img1_path","agent2","img2_path"
+        ])
+        writer.writeheader()
+
+if not os.path.exists(meta6):
+    with open(meta6, "w", newline="", encoding="utf-8") as f:
         writer = csv.DictWriter(f, fieldnames=[
             "id","original_path","prompt",
             "agent1","img1_path","agent2","img2_path"
         ])
         writer.writeheader()
 
-# evaluations 文件：保存 judge 提交的评分
-EVAL_FILE = "data/evaluations.csv"
-if not os.path.exists(EVAL_FILE):
-    with open(EVAL_FILE, "w", newline="", encoding="utf-8") as f:
+eval0 = "data/evaluations/eval0.csv"
+eval1 = "data/evaluations/eval1.csv"
+eval2 = "data/evaluations/eval2.csv"
+eval3 = "data/evaluations/eval3.csv"
+eval4 = "data/evaluations/eval4.csv"
+eval5 = "data/evaluations/eval5.csv"
+eval6 = "data/evaluations/eval6.csv"
+
+
+if not os.path.exists(eval0):
+    with open(eval0, "w", newline="", encoding="utf-8") as f:
         writer = csv.DictWriter(f, fieldnames=[
-            "record_id","timestamp","task",
+            "timestamp", "record_id",
             "a1_follow","a1_creativity","a1_finesse",
             "a2_follow","a2_creativity","a2_finesse"
         ])
         writer.writeheader()
 
+if not os.path.exists(eval1):
+    with open(eval1, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=[
+            "timestamp", "record_id",
+            "a1_follow","a1_creativity","a1_finesse",
+            "a2_follow","a2_creativity","a2_finesse"
+        ])
+        writer.writeheader()
 
-# —— 2. Agent 处理 & 保存到库 —— 
-def run_agent_on_image(original_img: Image.Image, prompt: str, agent_name: str) -> Image.Image:
-    """
-    TODO: 这里替换为你自己调用 HuggingFace API 或本地模型的逻辑
-    """
-    return original_img
-
-def save_to_library(orig_img, prompt, a1, a2, img1, img2):
-    """把这一组 original+prompt+两个 agent 的结果存到本地 data/ 文件夹，并在 metadata.csv 记录"""
-    rec_id = uuid.uuid4().hex
-    # 保存原图
-    orig_path = f"data/images/{rec_id}_orig.png"
-    orig_img.save(orig_path)
-    # 保存两张结果图（文件名中空格替换为下划线）
-    img1_path = f"data/images/{rec_id}_{a1.replace(' ','_')}.png"
-    img2_path = f"data/images/{rec_id}_{a2.replace(' ','_')}.png"
-    img1.save(img1_path)
-    img2.save(img2_path)
-    # 追加到 metadata.csv
-    with open(METADATA_FILE, "a", newline="", encoding="utf-8") as f:
+if not os.path.exists(eval2):
+    with open(eval2, "w", newline="", encoding="utf-8") as f:
         writer = csv.DictWriter(f, fieldnames=[
-            "id","original_path","prompt","agent1","img1_path","agent2","img2_path"
+            "timestamp", "record_id",
+            "a1_follow","a1_creativity","a1_finesse",
+            "a2_follow","a2_creativity","a2_finesse"
         ])
-        writer.writerow({
-            "id": rec_id,
-            "original_path": orig_path,
-            "prompt": prompt,
-            "agent1": a1,
-            "img1_path": img1_path,
-            "agent2": a2,
-            "img2_path": img2_path
-        })
-
-def generate_and_store(orig_img, prompt, a1, a2):
-    """处理＋保存＋返回两张结果图给 Gradio 显示"""
-    out1 = run_agent_on_image(orig_img, prompt, a1)
-    out2 = run_agent_on_image(orig_img, prompt, a2)
-    save_to_library(orig_img, prompt, a1, a2, out1, out2)
-    return out1, out2
-
-
-# —— 3. 从库中随机抽取 —— 
-def load_random_record():
-    """从 metadata.csv 随机选一条，返回 record_id、原图、prompt、两张处理图的路径"""
-    with open(METADATA_FILE, "r", encoding="utf-8") as f:
-        rows = list(csv.DictReader(f))
-    if not rows:
-        # 库空时提示
-        return "", None, "No records in library", None, None
-    rec = random.choice(rows)
-    return (
-        rec["id"],
-        rec["original_path"],
-        rec["prompt"],
-        rec["img1_path"],
-        rec["img2_path"]
-    )
-
-
-# —— 4. 保存评测结果 —— 
-def save_evaluation(record_id, task, 
-                    a1_follow, a1_creativity, a1_finesse,
-                    a2_follow, a2_creativity, a2_finesse):
-    """把打分连同 record_id 和 task 存到 evaluations.csv"""
-    with open(EVAL_FILE, "a", newline="", encoding="utf-8") as f:
+        writer.writeheader()
+
+if not os.path.exists(eval3):
+    with open(eval3, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=[
+            "timestamp", "record_id",
+            "a1_follow","a1_creativity","a1_finesse",
+            "a2_follow","a2_creativity","a2_finesse"
+        ])
+        writer.writeheader()
+
+if not os.path.exists(eval4):
+    with open(eval4, "w", newline="", encoding="utf-8") as f:
         writer = csv.DictWriter(f, fieldnames=[
-            "record_id","timestamp","task",
+            "timestamp", "record_id",
             "a1_follow","a1_creativity","a1_finesse",
             "a2_follow","a2_creativity","a2_finesse"
         ])
-        writer.writerow({
-            "record_id": record_id,
-            "timestamp": datetime.now().isoformat(),
-            "task": task,
-            "a1_follow": a1_follow,
-            "a1_creativity": a1_creativity,
-            "a1_finesse": a1_finesse,
-            "a2_follow": a2_follow,
-            "a2_creativity": a2_creativity,
-            "a2_finesse": a2_finesse
-        })
-    return "✅ Evaluation submitted!"
-
-
-# —— 5. Gradio UI —— 
+        writer.writeheader()
+
+if not os.path.exists(eval5):
+    with open(eval5, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=[
+            "timestamp", "record_id",
+            "a1_follow","a1_creativity","a1_finesse",
+            "a2_follow","a2_creativity","a2_finesse"
+        ])
+        writer.writeheader()
+
+if not os.path.exists(eval6):
+    with open(eval6, "w", newline="", encoding="utf-8") as f:
+        writer = csv.DictWriter(f, fieldnames=[
+            "timestamp", "record_id",
+            "a1_follow","a1_creativity","a1_finesse",
+            "a2_follow","a2_creativity","a2_finesse"
+        ])
+        writer.writeheader()
+        
+        
+def run_agent_on_image(original_img: Image.Image, prompt: str, agent_name: str) -> Image.Image:
+    if original_img is None:
+        raise ValueError("Input image cannot be None")
+    if not prompt or prompt.strip() == "":
+        raise ValueError("Prompt cannot be empty")
+    return original_img # TODO: implement actual agent processing
+
+def save_to_library(task_id, orig_img, prompt, a1, a2, img1, img2):
+    try:
+        if any(img is None for img in [orig_img, img1, img2]):
+            raise ValueError("All images must be valid")
+        if not prompt or prompt.strip() == "":
+            raise ValueError("Prompt cannot be empty")
+            
+        orig_id = uuid.uuid4().hex
+        
+        orig_path = f"data/images/task{task_id}/orig_imgs/{orig_id}.png"
+        img1_path = f"data/images/task{task_id}/processed_imgs/{orig_id}_a1.png"
+        img2_path = f"data/images/task{task_id}/processed_imgs/{orig_id}_a2.png"
+        
+        # 使用 try-except 处理图片保存
+        try:
+            orig_img.save(orig_path)
+            img1.save(img1_path)
+            img2.save(img2_path)
+        except Exception as e:
+            raise IOError(f"Failed to save images: {str(e)}")
+        
+        # 使用 try-except 处理 CSV 写入
+        try:
+            with open(f"data/metadatas/meta{task_id}.csv", "a", newline="", encoding="utf-8") as f:
+                writer = csv.DictWriter(f, fieldnames=[
+                    "id","original_path", "prompt",
+                    "agent1","img1_path","agent2","img2_path"
+                ])
+                writer.writerow({
+                    "id": orig_id,
+                    "original_path": orig_path,
+                    "prompt": prompt,
+                    "agent1": a1,
+                    "img1_path": img1_path,
+                    "agent2": a2,
+                    "img2_path": img2_path
+                })
+        except Exception as e:
+            # 如果写入CSV失败，清理已保存的图片
+            for path in [orig_path, img1_path, img2_path]:
+                if os.path.exists(path):
+                    os.remove(path)
+            raise IOError(f"Failed to write metadata: {str(e)}")
+            
+    except Exception as e:
+        raise Exception(f"Error in save_to_library: {str(e)}")
+
+def generate_and_store(task_id, orig_img, prompt, a1, a2):
+    try:
+        if orig_img is None:
+            return None, None
+        if not prompt or prompt.strip() == "":
+            return None, None
+        if a1 == a2:
+            return None, None  # 不允许选择相同的Agent
+            
+        out1 = run_agent_on_image(orig_img, prompt, a1)
+        out2 = run_agent_on_image(orig_img, prompt, a2)
+        save_to_library(task_id, orig_img, prompt, a1, a2, out1, out2)
+        return out1, out2
+    except Exception as e:
+        print(f"Error in generate_and_store: {str(e)}")
+        return None, None
+
+
+def load_random_record(task_id):
+    try:
+        # 检查文件是否存在
+        meta_file = f"data/metadatas/meta{task_id}.csv"
+        if not os.path.exists(meta_file):
+            return "", None, "Metadata file not found", None, None
+            
+        # 读取所有记录
+        with open(meta_file, "r", encoding="utf-8") as f:
+            all_records = list(csv.DictReader(f))
+        
+        if not all_records:
+            return "", None, "No records in library", None, None
+        
+        # 读取最近5分钟内的评测记录
+        recent_evaluated_ids = set()
+        current_time = datetime.now()
+        
+        eval_file = f"data/evaluations/eval{task_id}.csv"
+        if os.path.exists(eval_file):
+            try:
+                with open(eval_file, "r", encoding="utf-8") as f:
+                    eval_records = list(csv.DictReader(f))
+                    
+                for record in eval_records:
+                    try:
+                        eval_time = datetime.fromisoformat(record["timestamp"])
+                        time_diff = (current_time - eval_time).total_seconds() / 60  
+                        
+                        if time_diff <= 5: 
+                            recent_evaluated_ids.add(record["record_id"])
+                    except ValueError:
+                        # 跳过无效的时间戳
+                        continue
+            except Exception as e:
+                print(f"Error reading evaluation file: {str(e)}")
+        
+        available_records = [r for r in all_records if r["id"] not in recent_evaluated_ids]
+        
+        if not available_records:
+            return "", None, "All available records have been recently evaluated", None, None
+
+        rec = random.choice(available_records)
+        
+        # 验证图片文件是否存在
+        for path in [rec["original_path"], rec["img1_path"], rec["img2_path"]]:
+            if not os.path.exists(path):
+                return "", None, f"Image file not found: {path}", None, None
+                
+        return (
+            rec["id"],
+            rec["original_path"],
+            rec["prompt"],
+            rec["img1_path"],
+            rec["img2_path"]
+        )
+    except Exception as e:
+        return "", None, f"Error loading record: {str(e)}", None, None
+
+
+def save_evaluation(task_id, record_id,
+                    a1_follow, a1_creativity, a1_finesse,
+                    a2_follow, a2_creativity, a2_finesse):
+    try:
+        # 验证输入
+        if not record_id:
+            return "❌ Invalid record ID", *load_random_record(task_id)
+            
+        # 验证评分
+        scores = [a1_follow, a1_creativity, a1_finesse,
+                 a2_follow, a2_creativity, a2_finesse]
+        if any(score is None for score in scores):
+            return "❌ Please complete all evaluations", *load_random_record(task_id)
+            
+        with open(f"data/evaluations/eval{task_id}.csv", "a", newline="", encoding="utf-8") as f:
+            writer = csv.DictWriter(f, fieldnames=[
+                "timestamp", "record_id",
+                "a1_follow","a1_creativity","a1_finesse",
+                "a2_follow","a2_creativity","a2_finesse"
+            ])
+            writer.writerow({
+                "timestamp": datetime.now().isoformat(),
+                "record_id": record_id,
+                "a1_follow": a1_follow,
+                "a1_creativity": a1_creativity,
+                "a1_finesse": a1_finesse,
+                "a2_follow": a2_follow,
+                "a2_creativity": a2_creativity,
+                "a2_finesse": a2_finesse
+            })
+        
+        return "✅ Evaluation submitted!", *load_random_record(task_id)
+    except Exception as e:
+        return f"❌ Error saving evaluation: {str(e)}", *load_random_record(task_id)
+
+
 MODEL_CHOICES = ["Model A", "Model B", "Model C"]
 TASK_CHOICES = [
     "Image Restoration",
@@ -157,53 +372,59 @@ with gr.Blocks() as demo:
 
         # ——— Tab 2: Human as Judge ———
         with gr.TabItem("Human as Judge"):
-            # 隐藏状态：保存本次抽到的 record_id
             record_id_state = gr.State("")
+            task_dropdown = gr.Dropdown(choices=TASK_CHOICES, label="Task Category", type="index")
 
-            task_dropdown   = gr.Dropdown(choices=TASK_CHOICES, label="Task Category")
-            judge_orig      = gr.Image(label="Original Image")
-            judge_prompt    = gr.Textbox(label="Prompt", interactive=False)
-            judge_out1      = gr.Image(label="Agent 1 Result")
-            judge_out2      = gr.Image(label="Agent 2 Result")
+            # 原图与 Prompt 并排
+            with gr.Row():
+                judge_orig   = gr.Image(label="Original Image")
+                judge_prompt = gr.Textbox(label="Prompt", interactive=False)
+
+            # 两张结果图并排
+            with gr.Row():
+                judge_out1   = gr.Image(label="Agent 1 Result")
+                judge_out2   = gr.Image(label="Agent 2 Result")
 
-            # 当用户选 Task（或切换到此页）时，随机抽 record
+            # 当选 Task 时加载随机样本
             task_dropdown.change(
                 fn=load_random_record,
-                inputs=[],
+                inputs=[task_dropdown],
                 outputs=[record_id_state, judge_orig, judge_prompt, judge_out1, judge_out2],
                 show_api=False
             )
 
-            gr.Markdown("### 请对两张处理图分别打分（0–5）")
             with gr.Row():
+                gr.Markdown("## Please Evaluate the Original Image from 3 Aspects").style(text_align="center")
                 with gr.Column():
-                    gr.Markdown("#### Agent 1 Evaluation")
                     a1_follow     = gr.Radio([0,1,2,3,4,5], label="Follow Prompt")
                     a1_creativity = gr.Radio([0,1,2,3,4,5], label="Creativity")
                     a1_finesse    = gr.Radio([0,1,2,3,4,5], label="Finesse/Detail")
                 with gr.Column():
-                    gr.Markdown("#### Agent 2 Evaluation")
                     a2_follow     = gr.Radio([0,1,2,3,4,5], label="Follow Prompt")
                     a2_creativity = gr.Radio([0,1,2,3,4,5], label="Creativity")
                     a2_finesse    = gr.Radio([0,1,2,3,4,5], label="Finesse/Detail")
 
             submit_btn    = gr.Button("Submit Evaluation")
             submit_status = gr.Textbox(label="Status", interactive=False)
-
+            
             submit_btn.click(
                 fn=save_evaluation,
                 inputs=[
-                    record_id_state, task_dropdown,
+                    task_dropdown, record_id_state,
                     a1_follow, a1_creativity, a1_finesse,
                     a2_follow, a2_creativity, a2_finesse
                 ],
-                outputs=[submit_status],
+                outputs=[
+                    submit_status,
+                    record_id_state, judge_orig, judge_prompt, judge_out1, judge_out2
+                ],
                 show_api=False
             )
+            
 
     demo.queue()
     demo.launch(
         share=False,
         show_api=False,
         ssr_mode=False
-    )
+    )

test.py

@@ -0,0 +1,4 @@
+import uuid
+
+rec_id = uuid.uuid4().hex
+print(rec_id)