Update app.py

app.py

@@ -16,40 +16,48 @@ def extract_text(file_path):
             doc = fitz.open(file_path)
             text = "".join([page.get_text() for page in doc])
             doc.close()
-            return text[:20000] # 截取前2万字，防止超长导致API崩溃
+            return text[:20000] # 截取前2万字，防止超长
         return f"[检测到附件: {os.path.basename(file_path)}]"
     except Exception as e:
         return f"[解析失败: {str(e)}]"
 
 @spaces.GPU(duration=60)
 def chat_fn(message, history):
-    # 构造对话历史
-    messages = []
+    # --- 关键修复：清洗历史记录，确保只有纯字符串 ---
+    clean_messages = []
     for turn in history:
-        messages.append({"role": turn["role"], "content": turn["content"]})
+        # 强制将历史记录内容转化为纯字符串，剔除任何嵌套的对象/文件
+        content = turn.get("content", "")
+        if not isinstance(content, str):
+            content = str(content)
+        clean_messages.append({"role": turn["role"], "content": content})
     
-    # 提取用户输入和文件
+    # 提取当前用户输入
     prompt_text = message.get("text", "")
     files = message.get("files", [])
     
-    # 处理文件逻辑
+    # 处理新上传的文件逻辑
     if files and len(files) > 0:
-        # 兼容处理 Gradio 6.0 的文件对象格式
         f = files[0]
         file_path = f.get("path") if isinstance(f, dict) else f
         context = extract_text(file_path)
         if context:
-            prompt_text = f"【参考文档】:\n{context}\n\n【用户指令】: {prompt_text}"
+            # 只有在第一轮或新传文件时注入上下文
+            prompt_text = f"【参考文档内容】:\n{context}\n\n【当前指令】: {prompt_text}"
         
-    messages.append({"role": "user", "content": prompt_text})
+    clean_messages.append({"role": "user", "content": prompt_text})
 
     response = ""
     try:
-        # 增加流式输出的安全性校验
-        stream = client.chat_completion(messages, max_tokens=4096, stream=True, temperature=0.6)
+        # 调用 API
+        stream = client.chat_completion(
+            clean_messages, 
+            max_tokens=4096, 
+            stream=True, 
+            temperature=0.6
+        )
         
         for msg in stream:
-            # 这里的判断是修复 'list index out of range' 的关键
             if hasattr(msg, 'choices') and len(msg.choices) > 0:
                 delta = msg.choices[0].delta
                 if hasattr(delta, 'content') and delta.content:
@@ -57,17 +65,15 @@ def chat_fn(message, history):
                     yield response
         
         if response:
-            response += "\n\n---\n> **OpenIPOS MegaNode | Kimi-K2.5 旗舰算力**"
+            response += "\n\n---\n> **OpenIPOS MegaNode | 旗舰算力输出**"
             yield response
             
     except Exception as e:
-        # 捕获所有异常并友好提示
-        yield f"⚠️ 算力调度波动: {str(e)}。由于 Kimi-K2.5 访问量巨大，请刷新重试。"
+        yield f"⚠️ 算力调度波动: {str(e)}。由于 Kimi-K2.5 访问量巨大，请尝试刷新。建议：翻译长文档时请分段进行。"
 
-# 3. UI 界面 (Logo 完美适配)
+# 3. UI 界面
 with gr.Blocks(fill_height=True) as demo:
     with gr.Row():
-        # 使用 HTML 确保 Logo 比例协调
         gr.HTML("""
             <div style="display: flex; align-items: center; gap: 20px; padding: 10px;">
                 <img src="https://s3.bmp.ovh/2026/03/30/rIAGKg0O.png" style="height: 60px; width: auto;">
@@ -83,5 +89,4 @@ with gr.Blocks(fill_height=True) as demo:
     gr.Markdown("---")
     gr.Markdown("© 2026 OpenIPOS Global Limited. 算力驱动：Hugging Face Inference Providers")
 
-# 启动配置
 demo.launch(theme=gr.themes.Soft())