Update app.py

app.py

@@ -1,82 +1,113 @@
-import gradio as gr
-import json
-import os
-
-DATA_FILE = "./test_data.json"
-SAVE_FILE = "./annotations.jsonl"
-
-with open(DATA_FILE, "r", encoding="utf-8") as f:
-    data = json.load(f)
-
-if not os.path.exists(SAVE_FILE):
-    with open(SAVE_FILE, "w", encoding="utf-8") as f:
-        pass
-
-def annotate(index, score, comment, annotator):
-    entry = data[index]
-    record = {
-        "index": index,
-        "annotator": annotator,
-        "source": entry["source"],
-        "hypothesis": entry["hypothesis"],
-        "score": score,
-        "comment": comment
-    }
-
-    save_path = f"./annotations_{annotator}.jsonl"
-    with open(save_path, "a", encoding="utf-8") as f:
-        f.write(json.dumps(record, ensure_ascii=False) + "\n")
-
-    completed = index + 1
-    if completed >= len(data):
-        return (
-            "🎉 All samples annotated!",
-            index,
-            f"✅ Completed {completed}/{len(data)} samples.",
-            gr.update(interactive=False),
-            gr.update(interactive=False),
-            gr.update(interactive=False),
-            gr.update(interactive=False)
-        )
-
-    next_index = index + 1
-    progress = f"{completed}/{len(data)} annotated by {annotator}"
-    return (
-        "✅ Saved",
-        next_index,
-        progress,
-        gr.update(interactive=True),
-        gr.update(interactive=True),
-        gr.update(interactive=True),
-        gr.update(interactive=True)
-    )
-
-
-def load_sample(i):
-    entry = data[i]
-    return entry["source"], entry["hypothesis"]
-
-with gr.Blocks() as demo:
-    gr.Markdown("## Direct Assessment Annotation")
-
-    with gr.Row():
-        annotator = gr.Textbox(label="Annotator ID", placeholder="Enter your name or ID", value="annotator_1")
-        progress = gr.Textbox(label="Progress", interactive=False)
-
-    idx = gr.Number(value=0, visible=False)
-    source = gr.Textbox(label="Source Sentence", interactive=False)
-    hyp = gr.Textbox(label="Machine Translation", interactive=False)
-    score = gr.Slider(0, 100, step=1, label="Translation Quality Score")
-    comment = gr.Textbox(lines=2, placeholder="Optional comment...", label="Comment")
-    output = gr.Textbox(label="Status", interactive=False)
-    next_button = gr.Button("Submit and Next")
-
-    next_button.click(
-        fn=annotate,
-        inputs=[idx, score, comment, annotator],
-        outputs=[output, idx, progress, score, comment, next_button, annotator]
-    )
-    idx.change(fn=load_sample, inputs=idx, outputs=[source, hyp])
-    demo.load(fn=load_sample, inputs=[idx], outputs=[source, hyp])
-
-demo.launch()
+import gradio as gr
+import json
+import os
+import tempfile
+
+DATA_FILE = "./test_data.json"
+
+# 本地或 Render 环境下的保存目录
+SAVE_DIR = "./annotations"
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+# 读取样本数据
+with open(DATA_FILE, "r", encoding="utf-8") as f:
+    data = json.load(f)
+
+# 用于记录当前用户的所有打分（浏览器内存）
+user_annotations = []
+
+# 保存标注记录
+def annotate(index, score, comment, annotator):
+    entry = data[index]
+    record = {
+        "index": index,
+        "annotator": annotator,
+        "source": entry["source"],
+        "hypothesis": entry["hypothesis"],
+        "score": score,
+        "comment": comment,
+    }
+
+    # 1. 保存到用户 session 记录
+    user_annotations.append(record)
+
+    # 2. 仍然保存到服务器端（可选）
+    # save_path = os.path.join(SAVE_DIR, f"annotations_{annotator}.jsonl")
+    # with open(save_path, "a", encoding="utf-8") as f:
+    #     f.write(json.dumps(record, ensure_ascii=False) + "\n")
+
+    completed = index + 1
+    if completed >= len(data):
+        return (
+            "🎉 All samples annotated!",
+            index,
+            f"✅ Completed {completed}/{len(data)} samples.",
+            gr.update(interactive=False),
+            gr.update(interactive=False),
+            gr.update(interactive=False),
+            gr.update(interactive=False),
+            gr.update(visible=True)  # 显示导出按钮
+        )
+
+    next_index = index + 1
+    progress = f"{completed}/{len(data)} annotated by {annotator}"
+    return (
+        "✅ Saved",
+        next_index,
+        progress,
+        gr.update(interactive=True),
+        gr.update(interactive=True),
+        gr.update(interactive=True),
+        gr.update(interactive=True),
+        gr.update(visible=False)
+    )
+
+# 加载样本
+def load_sample(i):
+    entry = data[i]
+    return entry["source"], entry["hypothesis"]
+
+# 导出打分结果为 JSON 文件
+def export_results():
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".json", mode="w", encoding="utf-8")
+    json.dump(user_annotations, tmp, ensure_ascii=False, indent=2)
+    tmp.close()
+    return tmp.name
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Direct Assessment Annotation")
+
+    with gr.Row():
+        annotator = gr.Textbox(
+            label="Annotator ID",
+            placeholder="Enter your name or ID",
+            value="annotator_1",
+        )
+        progress = gr.Textbox(label="Progress", interactive=False)
+
+    idx = gr.Number(value=0, visible=False)
+    source = gr.Textbox(label="Source Sentence", interactive=False)
+    hyp = gr.Textbox(label="Machine Translation", interactive=False)
+    score = gr.Slider(0, 100, step=1, label="Translation Quality Score")
+    comment = gr.Textbox(lines=2, placeholder="Optional comment...", label="Comment")
+    output = gr.Textbox(label="Status", interactive=False)
+    next_button = gr.Button("Submit and Next")
+
+    # 新增：导出按钮和文件下载组件
+    export_button = gr.Button("📥 Export My Results")
+    export_file = gr.File(label="Download your results", visible=False)
+
+    # 原打分按钮逻辑
+    next_button.click(
+        fn=annotate,
+        inputs=[idx, score, comment, annotator],
+        outputs=[output, idx, progress, score, comment, next_button, annotator, export_file],
+    )
+
+    # 新增导出逻辑
+    export_button.click(fn=export_results, outputs=export_file)
+
+    idx.change(fn=load_sample, inputs=idx, outputs=[source, hyp])
+    demo.load(fn=load_sample, inputs=[idx], outputs=[source, hyp])
+
+demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))