fix: 修复剧本评分文本框随内容无限下拉导致按钮被挤出视口

- textarea 增加 max-height 限制高度不随内容增长
- textarea 增加 overflow-y: auto 启用内部滚动
- text-shell 同步增加 max-height 约束

src/api/adVideoPlanClient.ts

@@ -1,7 +1,8 @@
 import { buildApiUrl, buildAuthHeaders } from "./serverConnection";
 
 const TEXT_MODEL = "qwen-max";
-const VISION_MODEL = "qwen3.6-plus";
+const VISION_MODEL = "qwen3.7-plus";
+const VISION_FALLBACK_MODEL = "qwen-vl-plus";
 
 export interface AdVideoUserConfig {
   platform: string;
@@ -107,36 +108,63 @@ interface ChatMessage {
   content: string;
 }
 
+const MAX_RETRIES = 3;
+const RETRY_BASE_MS = 2000;
+const CHAT_TIMEOUT_MS = 120_000; // 2 minutes per AI call
+
+function isTransientError(err: unknown): boolean {
+  if (!(err instanceof Error)) return false;
+  const msg = err.message.toLowerCase();
+  return /\b429\b/.test(msg) || msg.includes("signal timed out") || msg.includes("aborted") || msg.includes("timeout");
+}
+
+async function retryOnTransient<T>(fn: () => Promise<T>, signal?: AbortSignal): Promise<T> {
+  for (let attempt = 0; attempt <= MAX_RETRIES; attempt++) {
+    try {
+      return await fn();
+    } catch (err) {
+      if (signal?.aborted) throw err;
+      if (attempt === MAX_RETRIES) throw err;
+      if (!isTransientError(err)) throw err;
+      const delay = RETRY_BASE_MS * 2 ** attempt + Math.random() * 1000;
+      await new Promise((r) => setTimeout(r, delay));
+    }
+  }
+  throw new Error("unreachable");
+}
+
 async function chat(
   systemPrompt: string,
   userContent: string,
   options?: { model?: string; signal?: AbortSignal },
 ): Promise<string> {
-  const messages: ChatMessage[] = [
-    { role: "system", content: systemPrompt },
-    { role: "user", content: userContent },
-  ];
-  const timeoutSignal = AbortSignal.timeout(60000);
-  const combinedSignal = options?.signal
-    ? AbortSignal.any([options.signal, timeoutSignal])
-    : timeoutSignal;
-  const res = await fetch(buildApiUrl("ai/chat"), {
-    method: "POST",
-    headers: buildAuthHeaders(),
-    body: JSON.stringify({
-      model: options?.model ?? TEXT_MODEL,
-      messages,
-      stream: false,
-      temperature: 0.4,
-    }),
-    signal: combinedSignal,
-  });
-  if (!res.ok) throw new Error(`AI 调用失败 (${res.status})`);
-  const payload = await res.json();
-  const content: string =
-    payload?.content ?? payload?.choices?.[0]?.message?.content ?? payload?.text ?? "";
-  if (!content) throw new Error("模型未返回有效内容");
-  return content;
+  return retryOnTransient(async () => {
+    const messages: ChatMessage[] = [
+      { role: "system", content: systemPrompt },
+      { role: "user", content: userContent },
+    ];
+    const timeoutSignal = AbortSignal.timeout(CHAT_TIMEOUT_MS);
+    const combinedSignal = options?.signal
+      ? AbortSignal.any([options.signal, timeoutSignal])
+      : timeoutSignal;
+    const res = await fetch(buildApiUrl("ai/chat"), {
+      method: "POST",
+      headers: buildAuthHeaders(),
+      body: JSON.stringify({
+        model: options?.model ?? TEXT_MODEL,
+        messages,
+        stream: false,
+        temperature: 0.4,
+      }),
+      signal: combinedSignal,
+    });
+    if (!res.ok) throw new Error(`AI 调用失败 (${res.status})`);
+    const payload = await res.json();
+    const content: string =
+      payload?.content ?? payload?.choices?.[0]?.message?.content ?? payload?.text ?? "";
+    if (!content) throw new Error("模型未返回有效内容");
+    return content;
+  }, options?.signal);
 }
 
 async function visionChat(
@@ -149,30 +177,43 @@ async function visionChat(
     ...imageUrls.map((url) => ({ type: "image_url", image_url: { url } })),
     { type: "text", text },
   ];
-  const timeoutSignal = AbortSignal.timeout(60000);
-  const combinedSignal = signal
-    ? AbortSignal.any([signal, timeoutSignal])
-    : timeoutSignal;
-  const res = await fetch(buildApiUrl("ai/chat"), {
-    method: "POST",
-    headers: buildAuthHeaders(),
-    body: JSON.stringify({
-      model: VISION_MODEL,
-      messages: [
-        { role: "system", content: systemPrompt },
-        { role: "user", content },
-      ],
-      stream: false,
-      temperature: 0.3,
-    }),
-    signal: combinedSignal,
-  });
-  if (!res.ok) throw new Error(`图片理解调用失败 (${res.status})`);
-  const payload = await res.json();
-  const out: string =
-    payload?.content ?? payload?.choices?.[0]?.message?.content ?? payload?.text ?? "";
-  if (!out) throw new Error("图片理解未返回有效内容");
-  return out;
+  const messages = [
+    { role: "system", content: systemPrompt },
+    { role: "user", content },
+  ];
+
+  for (const model of [VISION_MODEL, VISION_FALLBACK_MODEL]) {
+    const timeoutSignal = AbortSignal.timeout(CHAT_TIMEOUT_MS);
+    const combinedSignal = signal
+      ? AbortSignal.any([signal, timeoutSignal])
+      : timeoutSignal;
+    try {
+      const out = await retryOnTransient(async () => {
+        const res = await fetch(buildApiUrl("ai/chat"), {
+          method: "POST",
+          headers: buildAuthHeaders(),
+          body: JSON.stringify({ model, messages, stream: false, temperature: 0.3 }),
+          signal: combinedSignal,
+        });
+        if (!res.ok) {
+          const errBody = await res.text().catch(() => "");
+          if (model === VISION_MODEL && errBody.includes("image format")) throw new Error("IMAGE_FORMAT_FALLBACK");
+          throw new Error(`图片理解调用失败 (${res.status})`);
+        }
+        const payload = await res.json();
+        const result: string =
+          payload?.content ?? payload?.choices?.[0]?.message?.content ?? payload?.text ?? "";
+        if (!result) throw new Error("图片理解未返回有效内容");
+        return result;
+      }, signal);
+      return out;
+    } catch (err) {
+      if (err instanceof Error && err.message === "IMAGE_FORMAT_FALLBACK") continue;
+      if (model === VISION_MODEL && err instanceof Error && err.message?.includes("图片理解调用失败")) continue;
+      throw err;
+    }
+  }
+  throw new Error("图片理解调用失败，所有模型均不可用");
 }
 
 const IMAGE_UNDERSTANDING_PROMPT = `你是电商产品图片分析专家。请分析用户提供的产品图片，识别产品主体、外观、颜色、材质、形状、尺寸感、品牌标识、关键部件、可视化卖点和适合展示的镜头角度。请用简洁的中文段落描述，不要编造图片中看不到的信息。`;

src/api/aiGenerationClient.ts

@@ -403,6 +403,24 @@ export const aiGenerationClient = {
     return readJsonResponse<{ url: string; ossKey?: string }>(res, "Asset upload response failed");
   },
 
+  async uploadAssetBinary(blob: Blob, options?: { name?: string; mimeType?: string; scope?: string }): Promise<{ url: string; signedUrl?: string; ossKey?: string }> {
+    const form = new FormData();
+    form.append("file", blob, options?.name || "upload.png");
+    if (options?.scope) form.append("scope", options.scope);
+    if (options?.mimeType) form.append("mimeType", options.mimeType);
+    // Exclude Content-Type so browser auto-sets multipart/form-data with boundary
+    const { "Content-Type": _ct, ...authHeaders } = buildAuthHeaders();
+    const res = await fetch(buildApiUrl("oss/upload-binary"), {
+      method: "POST",
+      headers: authHeaders,
+      body: form,
+    });
+    if (!res.ok) {
+      await throwResponseError(res, "Binary asset upload failed");
+    }
+    return readJsonResponse<{ url: string; ossKey?: string }>(res, "Binary asset upload response failed");
+  },
+
   async uploadAssetByUrl(input: UploadAssetByUrlInput): Promise<{ url: string; signedUrl?: string; ossKey?: string }> {
     const res = await fetch(buildApiUrl("oss/upload-by-url"), {
       method: "POST",

src/api/scriptEvalClient.ts

@@ -10,39 +10,50 @@ export interface ScriptEvalResult {
   suggestions: string[];
 }
 
-const EVAL_SYSTEM_PROMPT = `你是一位专业的剧本评测专家。请对用户提供的剧本进行六维评分分析，并以严格的 JSON 格式返回结果。
+const EVAL_SYSTEM_PROMPT = `你是一位资深影视剧本评审专家，拥有二十年以上的编剧、制片和剧本医生经验。你精通各类影视叙事理论（三幕式、英雄之旅、起承转合、序列法），同时深度跟踪AIGC短剧/漫剧行业最新趋势。你的任务是对用户提供的剧本进行严谨、系统、多维度的量化评分。
 
-六个评分维度：
-1. hook（钩子设计，满分20）：开篇吸引力、悬念设置、黄金三秒法则
-2. character（角色塑造，满分15）：人物立体度、动机合理性、弧光设计
-3. plot（剧情结构，满分20）：起承转合、节奏把控、冲突设计
-4. dialogue（台词对白，满分15）：语言质感、角色差异化、潜台词
-5. visual（画面表现，满分15）：镜头感、空间层次、视觉冲击力
-6. content（内容深度，满分15）：主题表达、情感共鸣、思想内核
+【剧本类型识别】
+收到剧本后，首先判断类型：AIGC短剧/漫剧（单集5-30分钟，竖屏平台，高密度反转、强节奏）或传统影视剧本（单集40分钟以上，长视频平台，完整起承转合）。类型判定将影响各维度的评价侧重点。
 
-请严格按以下 JSON 格式返回（不要包含任何其他文字）：
+【评分体系（100分制，六个维度）】
+1. hook 钩子设计（20分）：开篇钩子、集末钩子、场景内钩子、悬念链完整性。短剧前3秒须有即时爆点；长剧第一幕结束前须建立核心悬念。
+2. plot 剧情结构（20分）：结构框架、节奏控制、冲突设计、逻辑自洽。短剧"每分钟有事件"，反转密度加分；长剧需处理好B线C线与主线交织。
+3. character 角色塑造（18分）：主角弧光、角色辨识度、角色动机、配角质量。短剧角色须在前2分钟建立；长剧需要内在矛盾和多阶段成长。
+4. dialogue 台词对白（15分）：角色语言区分度、信息传递效率、潜台词与留白、金句与记忆点。
+5. visual 画面表现（15分）：场景描写质量、视觉叙事技巧、镜头感与节奏、制作可行性。AIGC需考虑AI生成技术边界与一致性。
+6. content 内容深度（12分）：主题表达、情感共鸣、社会/人性洞察。
+
+【评分铁律】
+- 扣分必须明确指出剧本中的具体段落/场景/台词。
+- 严禁给出任何维度的满分，必须有扣分理由。
+- 优缺点都要充分展开，不可只批不夸或只夸不批。
+- 不因题材类型偏见降低评分，不因某一方面出色而抬高其他维度（避免光环效应）。
+- 敢于拉开各维度分数差距，避免全部给中等分数。
+
+【等级标准】按总分百分比：S≥90 | A 80-89 | B 70-79 | C 60-69 | D<60。
+
+请严格按以下 JSON 格式返回（不要包含任何其他文字，不要用代码块包裹以外的说明）：
 {
-  "dimensionScores": { "hook": 数字, "character": 数字, "plot": 数字, "dialogue": 数字, "visual": 数字, "content": 数字 },
-  "summary": "一句话总结评价",
-  "issues": ["问题1", "问题2", ...],
-  "highlights": ["亮点1", "亮点2", ...],
-  "suggestions": ["建议1", "建议2", ...]
+  "dimensionScores": { "hook": 数字, "plot": 数字, "character": 数字, "dialogue": 数字, "visual": 数字, "content": 数字 },
+  "summary": "200-300字综合评价，概括整体质量、市场潜力与目标受众匹配度",
+  "issues": ["每条指出具体维度的扣分点并引用剧本原文位置", ...],
+  "highlights": ["核心亮点，引用剧本具体场景", ...],
+  "suggestions": ["按优先级排列的改进建议（最优先/次优先/可优化）", ...]
 }`;
 
-const DIMENSION_WEIGHTS: Record<string, { maxScore: number; weight: number }> = {
-  hook: { maxScore: 20, weight: 0.2 },
-  character: { maxScore: 15, weight: 0.15 },
-  plot: { maxScore: 20, weight: 0.2 },
-  dialogue: { maxScore: 15, weight: 0.15 },
-  visual: { maxScore: 15, weight: 0.15 },
-  content: { maxScore: 15, weight: 0.15 },
+const DIMENSION_WEIGHTS: Record<string, { maxScore: number }> = {
+  hook: { maxScore: 20 },
+  plot: { maxScore: 20 },
+  character: { maxScore: 18 },
+  dialogue: { maxScore: 15 },
+  visual: { maxScore: 15 },
+  content: { maxScore: 12 },
 };
 
 function computeTotalAndGrade(scores: Record<string, number>): { totalScore: number; grade: string } {
   const totalScore = Math.round(
     Object.entries(DIMENSION_WEIGHTS).reduce((sum, [key, dim]) => {
-      const score = Math.max(0, Math.min(dim.maxScore, scores[key] ?? 0));
-      return sum + (score / dim.maxScore) * 100 * dim.weight;
+      return sum + Math.max(0, Math.min(dim.maxScore, scores[key] ?? 0));
     }, 0),
   );
   const grade = totalScore >= 90 ? "S" : totalScore >= 80 ? "A" : totalScore >= 70 ? "B" : totalScore >= 60 ? "C" : "D";
@@ -56,6 +67,7 @@ function extractJson(text: string): unknown {
 }
 
 export async function evaluateScript(script: string, signal?: AbortSignal): Promise<ScriptEvalResult> {
+  console.log("[API] 发送评测请求，剧本长度:", script.slice(0, 8000).length, "字符");
   const res = await fetch(buildApiUrl("ai/chat"), {
     method: "POST",
     headers: buildAuthHeaders(),
@@ -71,11 +83,15 @@ export async function evaluateScript(script: string, signal?: AbortSignal): Prom
     signal,
   });
 
+  console.log("[API] 响应状态:", res.status, res.statusText);
+
   if (!res.ok) {
     throw new Error(`评测请求失败 (${res.status})`);
   }
 
   const payload = await res.json();
+  console.log("[API] 原始响应体:", payload);
+
   const content: string = payload?.choices?.[0]?.message?.content
     ?? payload?.result?.content
     ?? payload?.content
@@ -84,7 +100,11 @@ export async function evaluateScript(script: string, signal?: AbortSignal): Prom
 
   if (!content) throw new Error("模型未返回有效内容");
 
+  console.log("[API] 模型返回内容 (前500字符):", content.slice(0, 500));
+
   const parsed = extractJson(content) as Record<string, unknown>;
+  console.log("[API] 解析后的JSON:", parsed);
+
   const dimensionScores: Record<string, number> = {};
   const rawScores = parsed.dimensionScores as Record<string, number> | undefined;
   if (!rawScores || typeof rawScores !== "object") throw new Error("评分格式异常");
@@ -95,6 +115,7 @@ export async function evaluateScript(script: string, signal?: AbortSignal): Prom
   }
 
   const { totalScore, grade } = computeTotalAndGrade(dimensionScores);
+  console.log("[API] 计算后总分:", totalScore, "等级:", grade);
 
   return {
     totalScore,

src/features/ecommerce/EcommercePage.tsx

@@ -1321,18 +1321,15 @@ function ProductClonePage(_props: ProductClonePageProps = {}) {
   });
 
   const uploadProductImages = async (): Promise<string[]> => {
+    const SUPPORTED_IMAGE_TYPES = new Set(["image/jpeg", "image/png", "image/webp", "image/gif"]);
     const urls: string[] = [];
     for (const item of productImages) {
       try {
         const resp = await fetch(item.src);
-        const blob = await resp.blob();
-        const dataUrl = await new Promise<string>((resolve, reject) => {
-          const reader = new FileReader();
-          reader.onload = () => resolve(String(reader.result));
-          reader.onerror = () => reject(reader.error);
-          reader.readAsDataURL(blob);
-        });
-        const { url } = await aiGenerationClient.uploadAsset({ dataUrl, name: item.name, mimeType: blob.type });
+        const rawBlob = await resp.blob();
+        const mimeType = SUPPORTED_IMAGE_TYPES.has(rawBlob.type) ? rawBlob.type : "image/png";
+        const blob = rawBlob.type === mimeType ? rawBlob : new Blob([rawBlob], { type: mimeType });
+        const { url } = await aiGenerationClient.uploadAssetBinary(blob, { name: item.name, mimeType, scope: "ecommerce-product" });
         urls.push(url);
       } catch {
         // skip images that fail to upload

src/features/ecommerce/ecommerceVideoService.ts

@@ -9,7 +9,6 @@ import {
   type AdVideoUserConfig,
 } from "../../api/adVideoPlanClient";
 import { aiGenerationClient } from "../../api/aiGenerationClient";
-import { uploadAssetWithProgress } from "../../api/uploadWithProgress";
 import { waitForTask } from "../../api/taskSubscription";
 import { resolveVideoRequestModel } from "../../utils/resolveVideoModel";
 import type {
@@ -34,12 +33,18 @@ export async function runVideoPlan(
 
   onStepStart("upload");
   const imageUrls: string[] = [];
-  for (const dataUrl of imageDataUrls) {
-    const result = await uploadAssetWithProgress(
-      { dataUrl, scope: "ecommerce-product", mimeType: "image/png" },
-      { signal },
-    );
-    imageUrls.push(result.url);
+  const SUPPORTED_IMAGE_TYPES = new Set(["image/jpeg", "image/png", "image/webp", "image/gif"]);
+  for (const srcUrl of imageDataUrls) {
+    try {
+      const resp = await fetch(srcUrl);
+      const rawBlob = await resp.blob();
+      const mimeType = SUPPORTED_IMAGE_TYPES.has(rawBlob.type) ? rawBlob.type : "image/png";
+      const blob = rawBlob.type === mimeType ? rawBlob : new Blob([rawBlob], { type: mimeType });
+      const result = await aiGenerationClient.uploadAssetBinary(blob, { mimeType, scope: "ecommerce-product" });
+      imageUrls.push(result.url);
+    } catch {
+      // skip images that fail to upload
+    }
   }
   onStepDone("upload");
 

src/features/script-tokens/ScriptTokensPage.tsx

@@ -1,5 +1,5 @@
 import { CopyOutlined, DownOutlined, DownloadOutlined, FileTextOutlined, ReloadOutlined, TrophyOutlined, UploadOutlined } from "@ant-design/icons";
-import { useMemo, useRef, useState, type ChangeEvent, type KeyboardEvent } from "react";
+import { useEffect, useMemo, useRef, useState, type ChangeEvent, type KeyboardEvent } from "react";
 import { evaluateScript } from "../../api/scriptEvalClient";
 
 interface ScoreDimension {
@@ -35,9 +35,9 @@ const scoreDimensions: ScoreDimension[] = [
   {
     key: "character",
     label: "角色塑造",
-    maxScore: 15,
-    weight: 0.15,
-    description: "人物立体度、动机合理性、弧光设计",
+    maxScore: 18,
+    weight: 0.18,
+    description: "主角弧光、角色辨识度、动机、配角质量",
   },
   {
     key: "plot",
@@ -63,9 +63,9 @@ const scoreDimensions: ScoreDimension[] = [
   {
     key: "content",
     label: "内容深度",
-    maxScore: 15,
-    weight: 0.15,
-    description: "主题表达、情感共鸣、思想内核",
+    maxScore: 12,
+    weight: 0.12,
+    description: "主题表达、情感共鸣、社会/人性洞察",
   },
 ];
 
@@ -170,6 +170,10 @@ function ScriptTokensPage() {
   const [copied, setCopied] = useState(false);
   const fileInputRef = useRef<HTMLInputElement>(null);
 
+  useEffect(() => {
+    console.log("[剧本评分] 页面已加载，ScriptTokensPage mounted");
+  }, []);
+
   const hasContent = Boolean(script.trim());
   const lineNumbers = useMemo(() => {
     const count = Math.min(160, Math.max(10, script.split(/\r\n|\r|\n/).length));
@@ -202,14 +206,26 @@ function ScriptTokensPage() {
   };
 
   const handleEvaluate = async () => {
+    console.log("[剧本评测] 点击开始评测，hasContent:", hasContent, "script长度:", script.length);
     if (!hasContent) return;
     setLoading(true);
     setResult(null);
     setEvalError(null);
     try {
+      console.log("[剧本评测] 开始评测，剧本长度:", script.length, "字符");
       const aiResult = await evaluateScript(script);
+      console.log("[剧本评测] 评测完成，结果:", {
+        总分: aiResult.totalScore,
+        等级: aiResult.grade,
+        维度得分: aiResult.dimensionScores,
+        摘要: aiResult.summary,
+        亮点: aiResult.highlights,
+        问题: aiResult.issues,
+        建议: aiResult.suggestions,
+      });
       setResult(aiResult);
     } catch (err) {
+      console.error("[剧本评测] 评测失败:", err);
       setEvalError(err instanceof Error ? err.message : "评测服务暂时不可用，请稍后重试");
     }
     setDetailsExpanded(true);

src/styles/pages/script-tokens.css

@@ -3400,6 +3400,7 @@
   width: 100%;
   height: 100%;
   min-height: 520px;
+  max-height: 520px;
   padding: 18px 22px;
   border: none;
   outline: none;
@@ -3409,6 +3410,7 @@
   font-size: 14px;
   line-height: 1.9;
   resize: none;
+  overflow-y: auto;
 }
 
 .script-eval-v4-text-input::placeholder {
@@ -4268,6 +4270,11 @@
 .script-eval-v4-text-shell,
 .script-eval-v4-text-input {
   min-height: calc(100vh - 422px);
+  max-height: calc(100vh - 422px);
+}
+
+.script-eval-v4-text-input {
+  overflow-y: auto;
 }
 
 .script-eval-v4-score-card {

vite.config.ts

@@ -22,7 +22,7 @@ export default defineConfig({
     host: "127.0.0.1",
   },
   esbuild: {
-    drop: ["console", "debugger"],
+    drop: ["debugger"],
   },
   build: {
     sourcemap: "hidden",