mirror of
https://github.com/cfngc4594/monaco-editor-lsp-next.git
synced 2025-07-04 01:10:53 +00:00
The window is basically complete, but there are some issues need to fix.
1. The window won't scroll when complete. 2. The AI needs the context to generate new inputs, I don't know how to write.
This commit is contained in:
Dioxide
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79d56204ce
commit
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150
src/app/actions/ai-testcase.ts
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150
src/app/actions/ai-testcase.ts
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@ -0,0 +1,150 @@
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"use server";
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import {AITestCaseInput, AITestCaseOutput, AITestCaseOutputSchema} from "@/types/ai-testcase";
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import { deepseek } from "@/lib/ai";
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import { CoreMessage, generateText } from "ai";
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import prisma from "@/lib/prisma";
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/**
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*
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* @param input
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* @returns
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*/
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export const generateAITestcase = async (
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input: AITestCaseInput
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): Promise<AITestCaseOutput> => {
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const model = deepseek("deepseek-chat");
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let problemDetails = "";
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if (input.problemId) {
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try {
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// 尝试获取英文描述
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const problemLocalizationEn = await prisma.problemLocalization.findUnique({
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where: {
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problemId_locale_type: {
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problemId: input.problemId,
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locale: "en",
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type: "DESCRIPTION",
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},
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},
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include: {
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problem: true,
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},
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});
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if (problemLocalizationEn) {
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problemDetails = `
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Problem Requirements:
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-------------------
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Description: ${problemLocalizationEn.content}
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`;
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} else {
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// 回退到中文描述
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const problemLocalizationZh = await prisma.problemLocalization.findUnique({
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where: {
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problemId_locale_type: {
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problemId: input.problemId,
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locale: "zh",
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type: "DESCRIPTION",
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},
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},
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include: {
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problem: true,
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},
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});
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if (problemLocalizationZh) {
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problemDetails = `
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Problem Requirements:
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-------------------
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Description: ${problemLocalizationZh.content}
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`;
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console.warn(`Fallback to Chinese description for problemId: ${input.problemId}`);
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} else {
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problemDetails = "Problem description not found in any language.";
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console.warn(`No description found for problemId: ${input.problemId}`);
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}
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}
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} catch (error) {
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console.error("Failed to fetch problem details:", error);
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problemDetails = "Error fetching problem description.";
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}
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}
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// 构建AI提示词
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const prompt = `
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Analyze the problem statement to get the expected input structure, constraints, and output logic. Generate **novel, randomized** inputs/outputs that strictly adhere to the problem's requirements. Focus on:
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Your entire response/output is going to consist of a single JSON object {}, and you will NOT wrap it within JSON Markdown markers.
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1. **Input Data Structure**: Identify required formats (e.g., arrays, integers, strings).
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2. **Input Constraints**: Determine valid ranges (e.g., array length: 2–100, integers: -1000 to 1000) and edge cases.
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3. **Output Logic**: Ensure outputs correctly reflect problem-specific operations.
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4. **Randomization**:
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Vary input magnitudes (mix min/max/-edge values with mid-range randomness)
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Use diverse data distributions (e.g., sorted/unsorted, negative/positive values)
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Avoid patterns from existing examples
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Your entire response/output is going to consist of a single JSON object {}, and you will NOT wrap it within JSON Markdown markers.
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Here is the problem description:
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${problemDetails}
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Respond **ONLY** with this JSON structure.
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***Do not wrap the json codes in JSON markers*** :
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{
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"expectedOutput": "Randomized output (e.g., [-5, 100] instead of [1, 2])",
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"inputs": [
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{
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"name": "Parameter 1",
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"value": <RANDOMIZED_DATA> // Use string to wrap actual JSON types (arrays/numbers/strings)
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},
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... // Add parameters as needed
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]
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}
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`;
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// 发送请求给OpenAI
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const messages: CoreMessage[] = [{ role: "user", content: prompt }];
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let text;
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try {
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const response = await generateText({
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model: model,
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messages: messages,
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});
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text = response.text;
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} catch (error) {
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console.error("Error generating text with OpenAI:", error);
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throw new Error("Failed to generate response from OpenAI");
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}
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// 解析LLM响应
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let llmResponseJson;
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try {
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llmResponseJson = JSON.parse(text)
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} catch (error) {
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console.error("Failed to parse LLM response as JSON:", error);
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console.error("LLM raw output:", text);
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throw new Error("Invalid JSON response from LLM");
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}
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// 验证响应格式
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const validationResult = AITestCaseOutputSchema.safeParse(llmResponseJson);
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if (!validationResult.success) {
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console.error("Zod validation failed:", validationResult.error.format());
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throw new Error("Response validation failed");
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}
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console.log("LLM response:", llmResponseJson);
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return validationResult.data;
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};
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@ -1,11 +1,12 @@
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"use client";
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import { useState, useEffect } from "react";
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import { Label } from "@/components/ui/label";
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import { Input } from "@/components/ui/input";
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import { Button } from "@/components/ui/button";
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import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
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import { getProblemData } from "@/app/actions/getProblem";
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import {useState, useEffect} from "react";
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import {generateAITestcase} from "@/app/actions/ai-testcase";
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import {Label} from "@/components/ui/label";
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import {Input} from "@/components/ui/input";
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import {Button} from "@/components/ui/button";
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import {Card, CardContent, CardHeader, CardTitle} from "@/components/ui/card";
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import {getProblemData} from "@/app/actions/getProblem";
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export default function EditTestcasePanel({
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problemId,
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@ -37,6 +38,7 @@ export default function EditTestcasePanel({
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setTestcases([]);
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}
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}
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fetchData();
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}, [problemId]);
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@ -46,11 +48,36 @@ export default function EditTestcasePanel({
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{
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id: `new-${Date.now()}`,
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expectedOutput: "",
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inputs: [{ name: "input1", value: "" }],
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inputs: [{name: "input1", value: ""}],
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},
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]);
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};
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const [isGenerating, setIsGenerating] = useState(false);
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const handleAITestcase = async () => {
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setIsGenerating(true);
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try {
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const AIOutputParsed = await generateAITestcase({problemId: problemId});
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setTestcases([
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...testcases,
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{
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id: `new-${Date.now()}`,
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expectedOutput: AIOutputParsed.expectedOutput,
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inputs: AIOutputParsed.inputs
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}
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])
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window.scrollTo({
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top: document.body.scrollHeight,
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behavior: 'smooth',
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});
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} catch (error) {
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console.error(error)
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} finally {
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setIsGenerating(false);
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}
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}
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const handleRemoveTestcase = (index: number) => {
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const newTestcases = [...testcases];
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newTestcases.splice(index, 1);
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@ -93,9 +120,41 @@ export default function EditTestcasePanel({
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<Card className="w-full">
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<CardHeader>
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<CardTitle>测试用例</CardTitle>
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<div className="flex items-center space-x-1"> {/* space-x-1 让按钮更接近 */}
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<Button type="button" onClick={handleAddTestcase}>
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添加测试用例
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</Button>
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<Button
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type="button"
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className="flex items-center gap-1"
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onClick={handleAITestcase}
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disabled={isGenerating}
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style={{
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opacity: isGenerating ? 0.7 : 1,
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cursor: isGenerating ? 'not-allowed' : 'pointer'
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}}
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>
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<svg
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data-testid="geist-icon"
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height="16"
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strokeLinejoin="round"
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style={{color: isGenerating ? '#888' : "currentColor"}}
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viewBox="0 0 16 16"
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width="16"
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>
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<path
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d="M2.5 0.5V0H3.5V0.5C3.5 1.60457 4.39543 2.5 5.5 2.5H6V3V3.5H5.5C4.39543 3.5 3.5 4.39543 3.5 5.5V6H3H2.5V5.5C2.5 4.39543 1.60457 3.5 0.5 3.5H0V3V2.5H0.5C1.60457 2.5 2.5 1.60457 2.5 0.5Z"
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fill="currentColor"></path>
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<path
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d="M14.5 4.5V5H13.5V4.5C13.5 3.94772 13.0523 3.5 12.5 3.5H12V3V2.5H12.5C13.0523 2.5 13.5 2.05228 13.5 1.5V1H14H14.5V1.5C14.5 2.05228 14.9477 2.5 15.5 2.5H16V3V3.5H15.5C14.9477 3.5 14.5 3.94772 14.5 4.5Z"
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fill="currentColor"></path>
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<path
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d="M8.40706 4.92939L8.5 4H9.5L9.59294 4.92939C9.82973 7.29734 11.7027 9.17027 14.0706 9.40706L15 9.5V10.5L14.0706 10.5929C11.7027 10.8297 9.82973 12.7027 9.59294 15.0706L9.5 16H8.5L8.40706 15.0706C8.17027 12.7027 6.29734 10.8297 3.92939 10.5929L3 10.5V9.5L3.92939 9.40706C6.29734 9.17027 8.17027 7.29734 8.40706 4.92939Z"
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fill="currentColor"></path>
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</svg>
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{isGenerating ? '生成中...' : '使用AI生成测试用例'}
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</Button>
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</div>
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</CardHeader>
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<CardContent>
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<div className="space-y-6">
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@ -176,5 +235,6 @@ export default function EditTestcasePanel({
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</div>
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</CardContent>
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</Card>
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);
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}
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21
src/types/ai-testcase.ts
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21
src/types/ai-testcase.ts
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import {z} from "zod";
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export const AITestCaseInputSchema = z.object({
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problemId: z.string(),
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})
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export type AITestCaseInput = z.infer<typeof AITestCaseInputSchema>
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const input = z.object({
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name: z.string(),
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value: z.string()
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})
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export const AITestCaseOutputSchema = z.object({
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expectedOutput: z.string(),
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inputs: z.array(input)
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})
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export type AITestCaseOutput = z.infer<typeof AITestCaseOutputSchema>
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