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viewer.py

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+import base64
+import io
+from PIL import Image
+import gradio as gr
+import pandas as pd
+
+def decode_image(base64_str):
+    try:
+        img_data = base64.b64decode(base64_str)
+        img = Image.open(io.BytesIO(img_data))
+        return img
+    except:
+        return None
+
+def display_sample(df, idx):
+    sample = df.iloc[idx]
+    
+    title = sample['title']
+    description = sample['description']
+    label = "广告" if sample['label'] == 1 else "非广告"
+    date = sample['date']
+    comments = sample['comments'] if 'comments' in sample else []
+    
+    images = []
+    for img_b64 in sample['images']:
+        img = decode_image(img_b64)
+        if img:
+            images.append(img)
+    
+    return title, description, label, date, comments, images
+
+def create_demo(df):
+    with gr.Blocks() as demo:
+        gr.Markdown("# RedNote Covert Advertisement Detection Dataset Viewer")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                idx_slider = gr.Slider(minimum=0, maximum=len(df)-1, step=1, value=0, label="Sample Index")
+                label_filter = gr.Radio(["全部", "仅广告", "仅非广告"], value="全部", label="筛选")
+                
+                def update_slider(choice):
+                    if choice == "仅广告":
+                        ad_indices = df[df['label'] == 1].index.tolist()
+                        return gr.Slider(minimum=0, maximum=len(ad_indices)-1, step=1, value=0)
+                    elif choice == "仅非广告":
+                        non_ad_indices = df[df['label'] == 0].index.tolist()
+                        return gr.Slider(minimum=0, maximum=len(non_ad_indices)-1, step=1, value=0)
+                    else:
+                        return gr.Slider(minimum=0, maximum=len(df)-1, step=1, value=0)
+                
+                label_filter.change(update_slider, inputs=[label_filter], outputs=[idx_slider])
+            
+            with gr.Column(scale=3):
+                title_text = gr.Textbox(label="标题")
+                desc_text = gr.Textbox(label="描述", lines=5)
+                label_text = gr.Textbox(label="标签")
+                date_text = gr.Textbox(label="日期")
+                comments_text = gr.Textbox(label="评论", lines=5)
+                image_gallery = gr.Gallery(label="图片", columns=3, height=400)
+        
+        def get_filtered_index(idx, filter_choice):
+            if filter_choice == "仅广告":
+                ad_indices = df[df['label'] == 1].index.tolist()
+                return ad_indices[idx]
+            elif filter_choice == "仅非广告":
+                non_ad_indices = df[df['label'] == 0].index.tolist()
+                return non_ad_indices[idx]
+            else:
+                return idx
+        
+        def update_display(idx, filter_choice):
+            real_idx = get_filtered_index(idx, filter_choice)
+            return display_sample(df, real_idx)
+        
+        idx_slider.change(
+            update_display, 
+            inputs=[idx_slider, label_filter], 
+            outputs=[title_text, desc_text, label_text, date_text, comments_text, image_gallery]
+        )
+        
+        # 初始显示第一个样本
+        title, desc, label, date, comments, images = display_sample(df, 0)
+        title_text.value = title
+        desc_text.value = desc
+        label_text.value = label
+        date_text.value = date
+        comments_text.value = "\n".join(comments) if comments else ""
+        image_gallery.value = images
+    
+    return demo
+
+# 加载数据集
+def load_dataset():
+    try:
+        train_df = pd.read_parquet("train.parquet")
+        val_df = pd.read_parquet("validation.parquet")
+        test_df = pd.read_parquet("test.parquet")
+        return pd.concat([train_df, val_df, test_df])
+    except:
+        # 如果加载失败，返回一个示例数据框
+        return pd.DataFrame({
+            'title': ['示例标题'],
+            'description': ['这是一个示例描述'],
+            'label': [0],
+            'date': ['01-01'],
+            'comments': [['评论1', '评论2']],
+            'images': [['']]  # 空图片
+        })
+
+# 创建演示
+df = load_dataset()
+demo = create_demo(df)