mirror of
https://github.com/tiennm99/litellm.git
synced 2026-07-10 23:06:52 +00:00
Add "Get Code" Feature (#11629)
* added get code * modify template * fixed code template * put the codesnippets in a different file * minor
This commit is contained in:
tanjiro
@@ -19,12 +19,12 @@ import {
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Text,
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SelectItem,
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TextInput,
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Button,
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Button as TremorButton,
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Divider,
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} from "@tremor/react";
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import { v4 as uuidv4 } from 'uuid';
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import { message, Select, Spin, Typography, Tooltip, Input, Upload } from "antd";
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import { message, Select, Spin, Typography, Tooltip, Input, Upload, Modal, Button } from "antd";
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import { makeOpenAIChatCompletionRequest } from "./chat_ui/llm_calls/chat_completion";
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import { makeOpenAIImageGenerationRequest } from "./chat_ui/llm_calls/image_generation";
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import { makeOpenAIImageEditsRequest } from "./chat_ui/llm_calls/image_edits";
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@@ -39,6 +39,7 @@ import TagSelector from "./tag_management/TagSelector";
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import VectorStoreSelector from "./vector_store_management/VectorStoreSelector";
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import GuardrailSelector from "./guardrails/GuardrailSelector";
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import { determineEndpointType } from "./chat_ui/EndpointUtils";
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import { generateCodeSnippet } from "./chat_ui/CodeSnippets";
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import { MessageType } from "./chat_ui/types";
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import ReasoningContent from "./chat_ui/ReasoningContent";
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import ResponseMetrics, { TokenUsage } from "./chat_ui/ResponseMetrics";
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@@ -56,7 +57,8 @@ import {
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InfoCircleOutlined,
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SafetyOutlined,
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UploadOutlined,
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PictureOutlined
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PictureOutlined,
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CodeOutlined
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} from "@ant-design/icons";
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const { TextArea } = Input;
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@@ -98,9 +100,31 @@ const ChatUI: React.FC<ChatUIProps> = ({
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const [messageTraceId, setMessageTraceId] = useState<string | null>(null);
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const [uploadedImage, setUploadedImage] = useState<File | null>(null);
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const [imagePreviewUrl, setImagePreviewUrl] = useState<string | null>(null);
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const [isGetCodeModalVisible, setIsGetCodeModalVisible] = useState(false);
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const [generatedCode, setGeneratedCode] = useState("");
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const [selectedSdk, setSelectedSdk] = useState<'openai' | 'azure'>('openai');
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const chatEndRef = useRef<HTMLDivElement>(null);
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useEffect(() => {
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if (isGetCodeModalVisible) {
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const code = generateCodeSnippet({
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apiKeySource,
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accessToken,
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apiKey,
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inputMessage,
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chatHistory,
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selectedTags,
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selectedVectorStores,
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selectedGuardrails,
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endpointType,
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selectedModel,
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selectedSdk,
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});
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setGeneratedCode(code);
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}
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}, [isGetCodeModalVisible, selectedSdk, apiKeySource, accessToken, apiKey, inputMessage, chatHistory, selectedTags, selectedVectorStores, selectedGuardrails, endpointType, selectedModel]);
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useEffect(() => {
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let userApiKey = apiKeySource === 'session' ? accessToken : apiKey;
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if (!userApiKey || !token || !userRole || !userID) {
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@@ -472,10 +496,10 @@ const ChatUI: React.FC<ChatUIProps> = ({
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return (
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<div className="w-full h-screen p-4 bg-white">
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<Card className="w-full rounded-xl shadow-md overflow-hidden">
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<div className="flex h-[80vh] w-full">
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<div className="flex h-[80vh] w-full gap-4">
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{/* Left Sidebar with Controls */}
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<div className="w-1/4 p-4 border-r border-gray-200 bg-gray-50">
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<div className="mb-6">
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<div className="w-1/4 p-4 bg-gray-50">
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<Title className="text-xl font-semibold mb-6 mt-2">Configurations</Title>
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<div className="space-y-6">
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<div>
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<Text className="font-medium block mb-2 text-gray-700 flex items-center">
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@@ -607,19 +631,30 @@ const ChatUI: React.FC<ChatUIProps> = ({
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/>
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</div>
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<Button
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onClick={clearChatHistory}
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className="w-full bg-gray-100 hover:bg-gray-200 text-gray-700 border-gray-300 mt-4"
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icon={ClearOutlined}
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>
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Clear Chat
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</Button>
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<div className="space-y-2 mt-6">
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<TremorButton
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onClick={clearChatHistory}
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className="w-full bg-gray-100 hover:bg-gray-200 text-gray-700 border-gray-300"
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icon={ClearOutlined}
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>
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Clear Chat
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</TremorButton>
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</div>
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</div>
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</div>
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</div>
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{/* Main Chat Area */}
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<div className="w-3/4 flex flex-col bg-white">
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<div className="p-4 border-b border-gray-200 flex justify-between items-center">
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<Title className="text-xl font-semibold mb-0">Test Key</Title>
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<TremorButton
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onClick={() => setIsGetCodeModalVisible(true)}
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className="bg-gray-100 hover:bg-gray-200 text-gray-700 border-gray-300"
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icon={CodeOutlined}
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>
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Get Code
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</TremorButton>
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</div>
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<div className="flex-1 overflow-auto p-4 pb-0">
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{chatHistory.length === 0 && (
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<div className="h-full flex flex-col items-center justify-center text-gray-400">
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@@ -781,15 +816,15 @@ const ChatUI: React.FC<ChatUIProps> = ({
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style={{ resize: 'none', paddingRight: '10px', paddingLeft: '10px' }}
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/>
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{isLoading ? (
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<Button
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<TremorButton
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onClick={handleCancelRequest}
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className="ml-2 bg-red-50 hover:bg-red-100 text-red-600 border-red-200"
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icon={DeleteOutlined}
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>
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Cancel
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</Button>
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</TremorButton>
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) : (
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<Button
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<TremorButton
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onClick={handleSendMessage}
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className="ml-2 text-white"
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icon={
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@@ -807,13 +842,56 @@ const ChatUI: React.FC<ChatUIProps> = ({
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: endpointType === EndpointType.IMAGE_EDITS
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? "Edit"
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: "Generate"}
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</Button>
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</TremorButton>
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)}
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</div>
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</div>
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</div>
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</div>
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</Card>
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<Modal
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title="Generated Code"
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visible={isGetCodeModalVisible}
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onCancel={() => setIsGetCodeModalVisible(false)}
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footer={null}
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width={800}
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>
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<div className="flex justify-between items-end my-4">
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<div>
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<Text className="font-medium block mb-1 text-gray-700">SDK Type</Text>
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<Select
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value={selectedSdk}
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onChange={(value) => setSelectedSdk(value as 'openai' | 'azure')}
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style={{ width: 150 }}
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options={[
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{ value: 'openai', label: 'OpenAI SDK' },
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{ value: 'azure', label: 'Azure SDK' },
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]}
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/>
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</div>
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<Button
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onClick={() => {
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navigator.clipboard.writeText(generatedCode);
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message.success("Copied to clipboard!");
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}}
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>
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Copy to Clipboard
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</Button>
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</div>
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<SyntaxHighlighter
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language="python"
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style={coy as any}
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wrapLines={true}
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wrapLongLines={true}
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className="rounded-md"
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customStyle={{
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maxHeight: '60vh',
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overflowY: 'auto',
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}}
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>
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{generatedCode}
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</SyntaxHighlighter>
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</Modal>
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</div>
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);
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};
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@@ -0,0 +1,432 @@
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import { MessageType } from "./types";
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import { EndpointType } from "./mode_endpoint_mapping";
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interface CodeGenMetadata {
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tags?: string[];
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vector_stores?: string[];
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guardrails?: string[];
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}
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interface GenerateCodeParams {
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apiKeySource: 'session' | 'custom';
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accessToken: string | null;
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apiKey: string;
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inputMessage: string;
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chatHistory: MessageType[];
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selectedTags: string[];
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selectedVectorStores: string[];
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selectedGuardrails: string[];
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endpointType: string;
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selectedModel: string | undefined;
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selectedSdk: 'openai' | 'azure';
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}
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export const generateCodeSnippet = (params: GenerateCodeParams): string => {
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const {
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apiKeySource,
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accessToken,
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apiKey,
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inputMessage,
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chatHistory,
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selectedTags,
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selectedVectorStores,
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selectedGuardrails,
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endpointType,
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selectedModel,
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selectedSdk,
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} = params;
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const effectiveApiKey = apiKeySource === 'session' ? accessToken : apiKey;
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const apiBase = window.location.origin;
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// Always get the input message early on, regardless of what happens later
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const userPrompt = inputMessage || "Your prompt here"; // Fallback if inputMessage is empty
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// Safely escape the prompt to prevent issues with quotes
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const safePrompt = userPrompt.replace(/\\/g, '\\\\').replace(/"/g, '\\"').replace(/\n/g, '\\n');
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const messages = chatHistory
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.filter(msg => !msg.isImage)
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.map(({ role, content }) => ({ role, content }));
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const metadata: CodeGenMetadata = {};
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if (selectedTags.length > 0) metadata.tags = selectedTags;
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if (selectedVectorStores.length > 0) metadata.vector_stores = selectedVectorStores;
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if (selectedGuardrails.length > 0) metadata.guardrails = selectedGuardrails;
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const modelNameForCode = selectedModel || 'your-model-name';
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const clientInitialization = selectedSdk === 'azure'
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? `import openai
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client = openai.AzureOpenAI(
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api_key="${effectiveApiKey || 'YOUR_LITELLM_API_KEY'}",
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azure_endpoint="${apiBase}",
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api_version="2024-02-01"
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)`
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: `import openai
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client = openai.OpenAI(
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api_key="${effectiveApiKey || 'YOUR_LITELLM_API_KEY'}",
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base_url="${apiBase}"
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)`;
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let endpointSpecificCode;
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switch (endpointType) {
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case EndpointType.CHAT: {
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const metadataIsNotEmpty = Object.keys(metadata).length > 0;
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let extraBodyCode = '';
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if (metadataIsNotEmpty) {
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const extraBodyObject = { metadata };
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const extraBodyString = JSON.stringify(extraBodyObject, null, 2);
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const indentedExtraBodyString = extraBodyString.split('\n').map(line => ' '.repeat(4) + line).join('\n').trim();
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extraBodyCode = `,\n extra_body=${indentedExtraBodyString}`;
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}
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endpointSpecificCode = `
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# request sent to model set on litellm proxy, \`litellm --model\`
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response = client.chat.completions.create(
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model="${modelNameForCode}",
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messages = ${JSON.stringify(messages, null, 4)}${extraBodyCode}
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)
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print(response)
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`;
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break;
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}
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case EndpointType.RESPONSES: {
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const metadataIsNotEmpty = Object.keys(metadata).length > 0;
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let extraBodyCode = '';
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if (metadataIsNotEmpty) {
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const extraBodyObject = { metadata };
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const extraBodyString = JSON.stringify(extraBodyObject, null, 2);
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const indentedExtraBodyString = extraBodyString.split('\n').map(line => ' '.repeat(4) + line).join('\n').trim();
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extraBodyCode = `,\n extra_body=${indentedExtraBodyString}`;
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}
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endpointSpecificCode = `
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# request sent to model set on litellm proxy, \`litellm --model\`
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response = client.responses.create(
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model="${modelNameForCode}",
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messages = ${JSON.stringify(messages, null, 4)}${extraBodyCode}
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)
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print(response)
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`;
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break;
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}
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case EndpointType.IMAGE:
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if (selectedSdk === 'azure') {
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endpointSpecificCode = `
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# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
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# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
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# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
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import os
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import requests
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import json
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import time
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from PIL import Image
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result = client.images.generate(
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model="${modelNameForCode}",
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prompt="${inputMessage}",
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n=1
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)
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json_response = json.loads(result.model_dump_json())
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# Set the directory for the stored image
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image_dir = os.path.join(os.curdir, 'images')
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# If the directory doesn't exist, create it
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if not os.path.isdir(image_dir):
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os.mkdir(image_dir)
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# Initialize the image path
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image_filename = f"generated_image_{int(time.time())}.png"
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image_path = os.path.join(image_dir, image_filename)
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try:
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# Retrieve the generated image
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if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
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image_url = json_response["data"][0]["url"]
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generated_image = requests.get(image_url).content
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with open(image_path, "wb") as image_file:
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image_file.write(generated_image)
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print(f"Image saved to {image_path}")
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# Display the image
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image = Image.open(image_path)
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image.show()
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else:
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print("Could not find image URL in response.")
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print("Full response:", json_response)
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except Exception as e:
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print(f"An error occurred: {e}")
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print("Full response:", json_response)
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`;
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} else {
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endpointSpecificCode = `
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import base64
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import os
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import time
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import json
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from PIL import Image
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import requests
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# Helper function to encode images to base64
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def encode_image(image_path):
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode('utf-8')
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# Helper function to create a file (simplified for this example)
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def create_file(image_path):
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# In a real implementation, this would upload the file to OpenAI
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# For this example, we'll just return a placeholder ID
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return f"file_{os.path.basename(image_path).replace('.', '_')}"
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# The prompt entered by the user
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prompt = "${safePrompt}"
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# Encode images to base64
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base64_image1 = encode_image("body-lotion.png")
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base64_image2 = encode_image("soap.png")
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# Create file IDs
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file_id1 = create_file("body-lotion.png")
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file_id2 = create_file("incense-kit.png")
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response = client.responses.create(
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model="${modelNameForCode}",
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input=[
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{
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"role": "user",
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"content": [
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{"type": "input_text", "text": prompt},
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{
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"type": "input_image",
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"image_url": f"data:image/jpeg;base64,{base64_image1}",
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},
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{
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"type": "input_image",
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"image_url": f"data:image/jpeg;base64,{base64_image2}",
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},
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{
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"type": "input_image",
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"file_id": file_id1,
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},
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{
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"type": "input_image",
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"file_id": file_id2,
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}
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],
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}
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],
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tools=[{"type": "image_generation"}],
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)
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# Process the response
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image_generation_calls = [
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output
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for output in response.output
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if output.type == "image_generation_call"
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]
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image_data = [output.result for output in image_generation_calls]
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if image_data:
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image_base64 = image_data[0]
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image_filename = f"edited_image_{int(time.time())}.png"
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with open(image_filename, "wb") as f:
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f.write(base64.b64decode(image_base64))
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print(f"Image saved to {image_filename}")
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else:
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# If no image is generated, there might be a text response with an explanation
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text_response = [output.text for output in response.output if hasattr(output, 'text')]
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if text_response:
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print("No image generated. Model response:")
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print("\\n".join(text_response))
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else:
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print("No image data found in response.")
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print("Full response for debugging:")
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print(response)
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`;
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}
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break;
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case EndpointType.IMAGE_EDITS:
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if (selectedSdk === 'azure') {
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endpointSpecificCode = `
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import base64
|
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import os
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import time
|
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import json
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from PIL import Image
|
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import requests
|
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|
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# Helper function to encode images to base64
|
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def encode_image(image_path):
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with open(image_path, "rb") as image_file:
|
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return base64.b64encode(image_file.read()).decode('utf-8')
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# The prompt entered by the user
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prompt = "${safePrompt}"
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# Encode images to base64
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base64_image1 = encode_image("body-lotion.png")
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base64_image2 = encode_image("soap.png")
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# Create file IDs
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file_id1 = create_file("body-lotion.png")
|
||||
file_id2 = create_file("incense-kit.png")
|
||||
|
||||
response = client.responses.create(
|
||||
model="${modelNameForCode}",
|
||||
input=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "input_text", "text": prompt},
|
||||
{
|
||||
"type": "input_image",
|
||||
"image_url": f"data:image/jpeg;base64,{base64_image1}",
|
||||
},
|
||||
{
|
||||
"type": "input_image",
|
||||
"image_url": f"data:image/jpeg;base64,{base64_image2}",
|
||||
},
|
||||
{
|
||||
"type": "input_image",
|
||||
"file_id": file_id1,
|
||||
},
|
||||
{
|
||||
"type": "input_image",
|
||||
"file_id": file_id2,
|
||||
}
|
||||
],
|
||||
}
|
||||
],
|
||||
tools=[{"type": "image_generation"}],
|
||||
)
|
||||
|
||||
# Process the response
|
||||
image_generation_calls = [
|
||||
output
|
||||
for output in response.output
|
||||
if output.type == "image_generation_call"
|
||||
]
|
||||
|
||||
image_data = [output.result for output in image_generation_calls]
|
||||
|
||||
if image_data:
|
||||
image_base64 = image_data[0]
|
||||
image_filename = f"edited_image_{int(time.time())}.png"
|
||||
with open(image_filename, "wb") as f:
|
||||
f.write(base64.b64decode(image_base64))
|
||||
print(f"Image saved to {image_filename}")
|
||||
else:
|
||||
# If no image is generated, there might be a text response with an explanation
|
||||
text_response = [output.text for output in response.output if hasattr(output, 'text')]
|
||||
if text_response:
|
||||
print("No image generated. Model response:")
|
||||
print("\\n".join(text_response))
|
||||
else:
|
||||
print("No image data found in response.")
|
||||
print("Full response for debugging:")
|
||||
print(response)
|
||||
`;
|
||||
} else {
|
||||
endpointSpecificCode = `
|
||||
import base64
|
||||
import os
|
||||
import time
|
||||
|
||||
# Helper function to encode images to base64
|
||||
def encode_image(image_path):
|
||||
with open(image_path, "rb") as image_file:
|
||||
return base64.b64encode(image_file.read()).decode('utf-8')
|
||||
|
||||
# Helper function to create a file (simplified for this example)
|
||||
def create_file(image_path):
|
||||
# In a real implementation, this would upload the file to OpenAI
|
||||
# For this example, we'll just return a placeholder ID
|
||||
return f"file_{os.path.basename(image_path).replace('.', '_')}"
|
||||
|
||||
# The prompt entered by the user
|
||||
prompt = "${safePrompt}"
|
||||
|
||||
# Encode images to base64
|
||||
base64_image1 = encode_image("body-lotion.png")
|
||||
base64_image2 = encode_image("soap.png")
|
||||
|
||||
# Create file IDs
|
||||
file_id1 = create_file("body-lotion.png")
|
||||
file_id2 = create_file("incense-kit.png")
|
||||
|
||||
response = client.responses.create(
|
||||
model="${modelNameForCode}",
|
||||
input=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "input_text", "text": prompt},
|
||||
{
|
||||
"type": "input_image",
|
||||
"image_url": f"data:image/jpeg;base64,{base64_image1}",
|
||||
},
|
||||
{
|
||||
"type": "input_image",
|
||||
"image_url": f"data:image/jpeg;base64,{base64_image2}",
|
||||
},
|
||||
{
|
||||
"type": "input_image",
|
||||
"file_id": file_id1,
|
||||
},
|
||||
{
|
||||
"type": "input_image",
|
||||
"file_id": file_id2,
|
||||
}
|
||||
],
|
||||
}
|
||||
],
|
||||
tools=[{"type": "image_generation"}],
|
||||
)
|
||||
|
||||
# Process the response
|
||||
image_generation_calls = [
|
||||
output
|
||||
for output in response.output
|
||||
if output.type == "image_generation_call"
|
||||
]
|
||||
|
||||
image_data = [output.result for output in image_generation_calls]
|
||||
|
||||
if image_data:
|
||||
image_base64 = image_data[0]
|
||||
image_filename = f"edited_image_{int(time.time())}.png"
|
||||
with open(image_filename, "wb") as f:
|
||||
f.write(base64.b64decode(image_base64))
|
||||
print(f"Image saved to {image_filename}")
|
||||
else:
|
||||
# If no image is generated, there might be a text response with an explanation
|
||||
text_response = [output.text for output in response.output if hasattr(output, 'text')]
|
||||
if text_response:
|
||||
print("No image generated. Model response:")
|
||||
print("\\n".join(text_response))
|
||||
else:
|
||||
print("No image data found in response.")
|
||||
print("Full response for debugging:")
|
||||
print(response)
|
||||
`;
|
||||
}
|
||||
break;
|
||||
default:
|
||||
endpointSpecificCode = "\n# Code generation for this endpoint is not implemented yet.";
|
||||
}
|
||||
|
||||
const finalCode = `${clientInitialization}\n${endpointSpecificCode}`;
|
||||
|
||||
return finalCode;
|
||||
};
|
||||
Reference in New Issue
Block a user