Export jinja files as TS module at build time (#1296)

This PR should fix
huggingface-internal/moon-landing#13013. It
partially removes the structure introduced in
#1255.

Current problem is that inference snippets are generated in the
front-end. Since front-end cannot access file-system and therefore the
jinja files, we have to find a workaround. This PR adds a build step
which exports all jinja files into a single TS module. I've updated the
`package.json` file so that now the snippets code should be available in
any environment (both node and browser).

cc @coyotte508 who suggested such a solution.

Tested it in `@tasks-gen` and "it works"

---
For the record, the exported file (not committed in this PR) looks like
this:

_# packages/inference/src/snippets/templates.exported.ts_
```ts
// Generated file - do not edit directly
export const templates: Record<string, Record<string, Record<string, string>>> = {
  "js": {
    "fetch": {
      "basic": "async function query(data) {\n\tconst response = await fetch(\n\t\t\"{{ fullUrl }}\",\n\t\t{\n\t\t\theaders: {\n\t\t\t\tAuthorization: \"{{ authorizationHeader }}\",\n\t\t\t\t\"Content-Type\": \"application/json\",\n\t\t\t},\n\t\t\tmethod: \"POST\",\n\t\t\tbody: JSON.stringify(data),\n\t\t}\n\t);\n\tconst result = await response.json();\n\treturn result;\n}\n\nquery({ inputs: {{ providerInputs.asObj.inputs }} }).then((response) => {\n    console.log(JSON.stringify(response));\n});",
      "basicAudio": "async function query(data) {\n\tconst response = await fetch(\n\t\t\"{{ fullUrl }}\",\n\t\t{\n\t\t\theaders: {\n\t\t\t\tAuthorization: \"{{ authorizationHeader }}\",\n\t\t\t\t\"Content-Type\": \"audio/flac\"\n\t\t\t},\n\t\t\tmethod: \"POST\",\n\t\t\tbody: JSON.stringify(data),\n\t\t}\n\t);\n\tconst result = await response.json();\n\treturn result;\n}\n\nquery({ inputs: {{ providerInputs.asObj.inputs }} }).then((response) => {\n    console.log(JSON.stringify(response));\n});",
      "basicImage": "async function query(data) {\n\tconst response = await fetch(\n\t\t\"{{ fullUrl }}\",\n\t\t{\n\t\t\theaders: {\n\t\t\t\tAuthorization: \"{{ authorizationHeader }}\",\n\t\t\t\t\"Content-Type\": \"image/jpeg\"\n\t\t\t},\n\t\t\tmethod: \"POST\",\n\t\t\tbody: JSON.stringify(data),\n\t\t}\n\t);\n\tconst result = await response.json();\n\treturn result;\n}\n\nquery({ inputs: {{ providerInputs.asObj.inputs }} }).then((response) => {\n    console.log(JSON.stringify(response));\n});",
      "textToAudio": "{% if model.library_name == \"transformers\" %}\nasync function query(data) {\n\tconst response = await fetch(\n\t\t\"{{ fullUrl }}\",\n\t\t{\n\t\t\theaders: {\n\t\t\t\tAuthorization: \"{{ authorizationHeader }}\",\n\t\t\t\t\"Content-Type\": \"application/json\",\n\t\t\t},\n\t\t\tmethod: \"POST\",\n\t\t\tbody: JSON.stringify(data),\n\t\t}\n\t);\n\tconst result = await response.blob();\n    return result;\n}\n\nquery({ inputs: {{ providerInputs.asObj.inputs }} }).then((response) => {\n    // Returns a byte object of the Audio wavform. Use it directly!\n});\n{% else %}\nasync function query(data) {\n\tconst response = await fetch(\n\t\t\"{{ fullUrl }}\",\n\t\t{\n\t\t\theaders: {\n\t\t\t\tAuthorization: \"{{ authorizationHeader }}\",\n\t\t\t\t\"Content-Type\": \"application/json\",\n\t\t\t},\n\t\t\tmethod: \"POST\",\n\t\t\tbody: JSON.stringify(data),\n\t\t}\n\t);\n    const result = await response.json();\n    return result;\n}\n\nquery({ inputs: {{ providerInputs.asObj.inputs }} }).then((response) => {\n    console.log(JSON.stringify(response));\n});\n{% endif %} ",
      "textToImage": "async function query(data) {\n\tconst response = await fetch(\n\t\t\"{{ fullUrl }}\",\n\t\t{\n\t\t\theaders: {\n\t\t\t\tAuthorization: \"{{ authorizationHeader }}\",\n\t\t\t\t\"Content-Type\": \"application/json\",\n\t\t\t},\n\t\t\tmethod: \"POST\",\n\t\t\tbody: JSON.stringify(data),\n\t\t}\n\t);\n\tconst result = await response.blob();\n\treturn result;\n}\n\nquery({ inputs: {{ providerInputs.asObj.inputs }} }).then((response) => {\n    // Use image\n});",
      "zeroShotClassification": "async function query(data) {\n    const response = await fetch(\n\t\t\"{{ fullUrl }}\",\n        {\n            headers: {\n\t\t\t\tAuthorization: \"{{ authorizationHeader }}\",\n                \"Content-Type\": \"application/json\",\n            },\n            method: \"POST\",\n            body: JSON.stringify(data),\n        }\n    );\n    const result = await response.json();\n    return result;\n}\n\nquery({\n    inputs: {{ providerInputs.asObj.inputs }},\n    parameters: { candidate_labels: [\"refund\", \"legal\", \"faq\"] }\n}).then((response) => {\n    console.log(JSON.stringify(response));\n});"
    },
    "huggingface.js": {
      "basic": "import { InferenceClient } from \"@huggingface/inference\";\n\nconst client = new InferenceClient(\"{{ accessToken }}\");\n\nconst output = await client.{{ methodName }}({\n\tmodel: \"{{ model.id }}\",\n\tinputs: {{ inputs.asObj.inputs }},\n\tprovider: \"{{ provider }}\",\n});\n\nconsole.log(output);",
      "basicAudio": "import { InferenceClient } from \"@huggingface/inference\";\n\nconst client = new InferenceClient(\"{{ accessToken }}\");\n\nconst data = fs.readFileSync({{inputs.asObj.inputs}});\n\nconst output = await client.{{ methodName }}({\n\tdata,\n\tmodel: \"{{ model.id }}\",\n\tprovider: \"{{ provider }}\",\n});\n\nconsole.log(output);",
      "basicImage": "import { InferenceClient } from \"@huggingface/inference\";\n\nconst client = new InferenceClient(\"{{ accessToken }}\");\n\nconst data = fs.readFileSync({{inputs.asObj.inputs}});\n\nconst output = await client.{{ methodName }}({\n\tdata,\n\tmodel: \"{{ model.id }}\",\n\tprovider: \"{{ provider }}\",\n});\n\nconsole.log(output);",
      "conversational": "import { InferenceClient } from \"@huggingface/inference\";\n\nconst client = new InferenceClient(\"{{ accessToken }}\");\n\nconst chatCompletion = await client.chatCompletion({\n    provider: \"{{ provider }}\",\n    model: \"{{ model.id }}\",\n{{ inputs.asTsString }}\n});\n\nconsole.log(chatCompletion.choices[0].message);",
      "conversationalStream": "import { InferenceClient } from \"@huggingface/inference\";\n\nconst client = new InferenceClient(\"{{ accessToken }}\");\n\nlet out = \"\";\n\nconst stream = await client.chatCompletionStream({\n    provider: \"{{ provider }}\",\n    model: \"{{ model.id }}\",\n{{ inputs.asTsString }}\n});\n\nfor await (const chunk of stream) {\n\tif (chunk.choices && chunk.choices.length > 0) {\n\t\tconst newContent = chunk.choices[0].delta.content;\n\t\tout += newContent;\n\t\tconsole.log(newContent);\n\t}  \n}",
      "textToImage": "import { InferenceClient } from \"@huggingface/inference\";\n\nconst client = new InferenceClient(\"{{ accessToken }}\");\n\nconst image = await client.textToImage({\n    provider: \"{{ provider }}\",\n    model: \"{{ model.id }}\",\n\tinputs: {{ inputs.asObj.inputs }},\n\tparameters: { num_inference_steps: 5 },\n});\n/// Use the generated image (it's a Blob)",
      "textToVideo": "import { InferenceClient } from \"@huggingface/inference\";\n\nconst client = new InferenceClient(\"{{ accessToken }}\");\n\nconst image = await client.textToVideo({\n    provider: \"{{ provider }}\",\n    model: \"{{ model.id }}\",\n\tinputs: {{ inputs.asObj.inputs }},\n});\n// Use the generated video (it's a Blob)"
    },
    "openai": {
      "conversational": "import { OpenAI } from \"openai\";\n\nconst client = new OpenAI({\n\tbaseURL: \"{{ baseUrl }}\",\n\tapiKey: \"{{ accessToken }}\",\n});\n\nconst chatCompletion = await client.chat.completions.create({\n\tmodel: \"{{ providerModelId }}\",\n{{ inputs.asTsString }}\n});\n\nconsole.log(chatCompletion.choices[0].message);",
      "conversationalStream": "import { OpenAI } from \"openai\";\n\nconst client = new OpenAI({\n\tbaseURL: \"{{ baseUrl }}\",\n\tapiKey: \"{{ accessToken }}\",\n});\n\nlet out = \"\";\n\nconst stream = await client.chat.completions.create({\n    provider: \"{{ provider }}\",\n    model: \"{{ model.id }}\",\n{{ inputs.asTsString }}\n});\n\nfor await (const chunk of stream) {\n\tif (chunk.choices && chunk.choices.length > 0) {\n\t\tconst newContent = chunk.choices[0].delta.content;\n\t\tout += newContent;\n\t\tconsole.log(newContent);\n\t}  \n}"
    }
  },
  "python": {
    "fal_client": {
      "textToImage": "{% if provider == \"fal-ai\" %}\nimport fal_client\n\nresult = fal_client.subscribe(\n    \"{{ providerModelId }}\",\n    arguments={\n        \"prompt\": {{ inputs.asObj.inputs }},\n    },\n)\nprint(result)\n{% endif %} "
    },
    "huggingface_hub": {
      "basic": "result = client.{{ methodName }}(\n    inputs={{ inputs.asObj.inputs }},\n    model=\"{{ model.id }}\",\n)",
      "basicAudio": "output = client.{{ methodName }}({{ inputs.asObj.inputs }}, model=\"{{ model.id }}\")",
      "basicImage": "output = client.{{ methodName }}({{ inputs.asObj.inputs }}, model=\"{{ model.id }}\")",
      "conversational": "completion = client.chat.completions.create(\n    model=\"{{ model.id }}\",\n{{ inputs.asPythonString }}\n)\n\nprint(completion.choices[0].message) ",
      "conversationalStream": "stream = client.chat.completions.create(\n    model=\"{{ model.id }}\",\n{{ inputs.asPythonString }}\n    stream=True,\n)\n\nfor chunk in stream:\n    print(chunk.choices[0].delta.content, end=\"\") ",
      "documentQuestionAnswering": "output = client.document_question_answering(\n    \"{{ inputs.asObj.image }}\",\n    question=\"{{ inputs.asObj.question }}\",\n    model=\"{{ model.id }}\",\n) ",
      "imageToImage": "# output is a PIL.Image object\nimage = client.image_to_image(\n    \"{{ inputs.asObj.inputs }}\",\n    prompt=\"{{ inputs.asObj.parameters.prompt }}\",\n    model=\"{{ model.id }}\",\n) ",
      "importInferenceClient": "from huggingface_hub import InferenceClient\n\nclient = InferenceClient(\n    provider=\"{{ provider }}\",\n    api_key=\"{{ accessToken }}\",\n)",
      "textToImage": "# output is a PIL.Image object\nimage = client.text_to_image(\n    {{ inputs.asObj.inputs }},\n    model=\"{{ model.id }}\",\n) ",
      "textToVideo": "video = client.text_to_video(\n    {{ inputs.asObj.inputs }},\n    model=\"{{ model.id }}\",\n) "
    },
    "openai": {
      "conversational": "from openai import OpenAI\n\nclient = OpenAI(\n    base_url=\"{{ baseUrl }}\",\n    api_key=\"{{ accessToken }}\"\n)\n\ncompletion = client.chat.completions.create(\n    model=\"{{ providerModelId }}\",\n{{ inputs.asPythonString }}\n)\n\nprint(completion.choices[0].message) ",
      "conversationalStream": "from openai import OpenAI\n\nclient = OpenAI(\n    base_url=\"{{ baseUrl }}\",\n    api_key=\"{{ accessToken }}\"\n)\n\nstream = client.chat.completions.create(\n    model=\"{{ providerModelId }}\",\n{{ inputs.asPythonString }}\n    stream=True,\n)\n\nfor chunk in stream:\n    print(chunk.choices[0].delta.content, end=\"\")"
    },
    "requests": {
      "basic": "def query(payload):\n    response = requests.post(API_URL, headers=headers, json=payload)\n    return response.json()\n\noutput = query({\n    \"inputs\": {{ providerInputs.asObj.inputs }},\n}) ",
      "basicAudio": "def query(filename):\n    with open(filename, \"rb\") as f:\n        data = f.read()\n    response = requests.post(API_URL, headers={\"Content-Type\": \"audio/flac\", **headers}, data=data)\n    return response.json()\n\noutput = query({{ providerInputs.asObj.inputs }})",
      "basicImage": "def query(filename):\n    with open(filename, \"rb\") as f:\n        data = f.read()\n    response = requests.post(API_URL, headers={\"Content-Type\": \"image/jpeg\", **headers}, data=data)\n    return response.json()\n\noutput = query({{ providerInputs.asObj.inputs }})",
      "conversational": "def query(payload):\n    response = requests.post(API_URL, headers=headers, json=payload)\n    return response.json()\n\nresponse = query({\n{{ providerInputs.asJsonString }}\n})\n\nprint(response[\"choices\"][0][\"message\"])",
      "conversationalStream": "def query(payload):\n    response = requests.post(API_URL, headers=headers, json=payload, stream=True)\n    for line in response.iter_lines():\n        if not line.startswith(b\"data:\"):\n            continue\n        if line.strip() == b\"data: [DONE]\":\n            return\n        yield json.loads(line.decode(\"utf-8\").lstrip(\"data:\").rstrip(\"/n\"))\n\nchunks = query({\n{{ providerInputs.asJsonString }},\n    \"stream\": True,\n})\n\nfor chunk in chunks:\n    print(chunk[\"choices\"][0][\"delta\"][\"content\"], end=\"\")",
      "documentQuestionAnswering": "def query(payload):\n    with open(payload[\"image\"], \"rb\") as f:\n        img = f.read()\n        payload[\"image\"] = base64.b64encode(img).decode(\"utf-8\")\n    response = requests.post(API_URL, headers=headers, json=payload)\n    return response.json()\n\noutput = query({\n    \"inputs\": {\n        \"image\": \"{{ inputs.asObj.image }}\",\n        \"question\": \"{{ inputs.asObj.question }}\",\n    },\n}) ",
      "imageToImage": "def query(payload):\n    with open(payload[\"inputs\"], \"rb\") as f:\n        img = f.read()\n        payload[\"inputs\"] = base64.b64encode(img).decode(\"utf-8\")\n    response = requests.post(API_URL, headers=headers, json=payload)\n    return response.content\n\nimage_bytes = query({\n{{ providerInputs.asJsonString }}\n})\n\n# You can access the image with PIL.Image for example\nimport io\nfrom PIL import Image\nimage = Image.open(io.BytesIO(image_bytes)) ",
      "importRequests": "{% if importBase64 %}\nimport base64\n{% endif %}\n{% if importJson %}\nimport json\n{% endif %}\nimport requests\n\nAPI_URL = \"{{ fullUrl }}\"\nheaders = {\"Authorization\": \"{{ authorizationHeader }}\"}",
      "tabular": "def query(payload):\n    response = requests.post(API_URL, headers=headers, json=payload)\n    return response.content\n\nresponse = query({\n    \"inputs\": {\n        \"data\": {{ providerInputs.asObj.inputs }}\n    },\n}) ",
      "textToAudio": "{% if model.library_name == \"transformers\" %}\ndef query(payload):\n    response = requests.post(API_URL, headers=headers, json=payload)\n    return response.content\n\naudio_bytes = query({\n    \"inputs\": {{ providerInputs.asObj.inputs }},\n})\n# You can access the audio with IPython.display for example\nfrom IPython.display import Audio\nAudio(audio_bytes)\n{% else %}\ndef query(payload):\n    response = requests.post(API_URL, headers=headers, json=payload)\n    return response.json()\n\naudio, sampling_rate = query({\n    \"inputs\": {{ providerInputs.asObj.inputs }},\n})\n# You can access the audio with IPython.display for example\nfrom IPython.display import Audio\nAudio(audio, rate=sampling_rate)\n{% endif %} ",
      "textToImage": "{% if provider == \"hf-inference\" %}\ndef query(payload):\n    response = requests.post(API_URL, headers=headers, json=payload)\n    return response.content\n\nimage_bytes = query({\n    \"inputs\": {{ providerInputs.asObj.inputs }},\n})\n\n# You can access the image with PIL.Image for example\nimport io\nfrom PIL import Image\nimage = Image.open(io.BytesIO(image_bytes))\n{% endif %}",
      "zeroShotClassification": "def query(payload):\n    response = requests.post(API_URL, headers=headers, json=payload)\n    return response.json()\n\noutput = query({\n    \"inputs\": {{ providerInputs.asObj.inputs }},\n    \"parameters\": {\"candidate_labels\": [\"refund\", \"legal\", \"faq\"]},\n}) ",
      "zeroShotImageClassification": "def query(data):\n    with open(data[\"image_path\"], \"rb\") as f:\n        img = f.read()\n    payload={\n        \"parameters\": data[\"parameters\"],\n        \"inputs\": base64.b64encode(img).decode(\"utf-8\")\n    }\n    response = requests.post(API_URL, headers=headers, json=payload)\n    return response.json()\n\noutput = query({\n    \"image_path\": {{ providerInputs.asObj.inputs }},\n    \"parameters\": {\"candidate_labels\": [\"cat\", \"dog\", \"llama\"]},\n}) "
    }
  },
  "sh": {
    "curl": {
      "basic": "curl {{ fullUrl }} \\\n    -X POST \\\n    -H 'Authorization: {{ authorizationHeader }}' \\\n    -H 'Content-Type: application/json' \\\n    -d '{\n{{ providerInputs.asCurlString }}\n    }'",
      "basicAudio": "curl {{ fullUrl }} \\\n    -X POST \\\n    -H 'Authorization: {{ authorizationHeader }}' \\\n    -H 'Content-Type: audio/flac' \\\n    --data-binary @{{ providerInputs.asObj.inputs }}",
      "basicImage": "curl {{ fullUrl }} \\\n    -X POST \\\n    -H 'Authorization: {{ authorizationHeader }}' \\\n    -H 'Content-Type: image/jpeg' \\\n    --data-binary @{{ providerInputs.asObj.inputs }}",
      "conversational": "curl {{ fullUrl }} \\\n    -H 'Authorization: {{ authorizationHeader }}' \\\n    -H 'Content-Type: application/json' \\\n    -d '{\n{{ providerInputs.asCurlString }},\n        \"stream\": false\n    }'",
      "conversationalStream": "curl {{ fullUrl }} \\\n    -H 'Authorization: {{ authorizationHeader }}' \\\n    -H 'Content-Type: application/json' \\\n    -d '{\n{{ providerInputs.asCurlString }},\n        \"stream\": true\n    }'",
      "zeroShotClassification": "curl {{ fullUrl }} \\\n    -X POST \\\n    -d '{\"inputs\": {{ providerInputs.asObj.inputs }}, \"parameters\": {\"candidate_labels\": [\"refund\", \"legal\", \"faq\"]}}' \\\n    -H 'Content-Type: application/json' \\\n    -H 'Authorization: {{ authorizationHeader }}'"
    }
  }
} as const;

```

Author: Wauplin

packages/inference/.gitignore

@@ -0,0 +1 @@
+src/snippets/templates.exported.ts

packages/inference/package.json

@@ -26,27 +26,21 @@
 	},
 	"files": [
 		"dist",
-		"src"
+		"src",
+		"!src/snippets/templates/**/*.jinja"
 	],
 	"source": "src/index.ts",
 	"types": "./dist/src/index.d.ts",
 	"main": "./dist/index.cjs",
 	"module": "./dist/index.js",
 	"exports": {
-		".": {
-			"types": "./dist/src/index.d.ts",
-			"require": "./dist/index.cjs",
-			"import": "./dist/index.js"
-		}
-	},
-	"browser": {
-		"./src/snippets/index.js": false,
-		"./dist/index.js": "./dist/browser/index.js",
-		"./dist/index.mjs": "./dist/browser/index.mjs"
+		"types": "./dist/src/index.d.ts",
+		"require": "./dist/index.cjs",
+		"import": "./dist/index.js"
 	},
 	"type": "module",
 	"scripts": {
-		"build": "tsup src/index.ts --format cjs,esm --clean && tsc --emitDeclarationOnly --declaration",
+		"build": "pnpm run export-templates && tsup src/index.ts --format cjs,esm --clean && tsc --emitDeclarationOnly --declaration",
 		"dts": "tsx scripts/generate-dts.ts && tsc --noEmit dist/index.d.ts",
 		"lint": "eslint --quiet --fix --ext .cjs,.ts .",
 		"lint:check": "eslint --ext .cjs,.ts .",
@@ -57,7 +51,8 @@
 		"test": "vitest run --config vitest.config.mts",
 		"test:browser": "vitest run --browser.name=chrome --browser.headless --config vitest.config.mts",
 		"check": "tsc",
-		"dev": "tsup src/index.ts --format cjs,esm --watch"
+		"dev": "pnpm run export-templates && tsup src/index.ts --format cjs,esm --watch",
+		"export-templates": "tsx scripts/export-templates.ts"
 	},
 	"dependencies": {
 		"@huggingface/tasks": "workspace:^",

packages/inference/scripts/export-templates.ts

@@ -0,0 +1,54 @@
+/* Script that export all jinja files from packages/inference/src/snippets/templates into a TS module.
+
+Templates are exported in packages/inference/src/snippets/templates.exported.ts.
+*/
+import { readFileSync, writeFileSync, readdirSync } from "node:fs";
+import path from "node:path";
+
+const TEMPLATES_DIR = path.join(process.cwd(), "src", "snippets", "templates");
+
+function exportTemplatesAsCode(): string {
+	/// language -> client -> templateName -> templateContent
+	const templates: Record<string, Record<string, Record<string, string>>> = {};
+
+	// Read all language directories
+	const languages = readdirSync(TEMPLATES_DIR);
+	for (const language of languages) {
+		const languagePath = path.join(TEMPLATES_DIR, language);
+		if (!readdirSync(languagePath).length) continue;
+
+		templates[language] = {};
+
+		// Read all client directories
+		const clients = readdirSync(languagePath);
+		for (const client of clients) {
+			const clientPath = path.join(languagePath, client);
+			if (!readdirSync(clientPath).length) continue;
+
+			templates[language][client] = {};
+
+			// Read all template files
+			const files = readdirSync(clientPath);
+			for (const file of files) {
+				if (!file.endsWith(".jinja")) continue;
+
+				const templatePath = path.join(clientPath, file);
+				const templateContent = readFileSync(templatePath, "utf8");
+				const templateName = path.basename(file, ".jinja");
+
+				templates[language][client][templateName] = templateContent;
+			}
+		}
+	}
+
+	const templatesJson = JSON.stringify(templates, null, 2);
+	return `// Generated file - do not edit directly
+export const templates: Record<string, Record<string, Record<string, string>>> = ${templatesJson} as const;
+`;
+}
+
+// Generate and write the templates file
+const output = exportTemplatesAsCode();
+const outputPath = path.join(process.cwd(), "src", "snippets", "templates.exported.ts");
+writeFileSync(outputPath, output);
+console.log("Templates exported successfully! 🚀");

packages/inference/src/snippets/getInferenceSnippets.ts

@@ -10,9 +10,7 @@ import {
 import type { InferenceProvider, InferenceTask, RequestArgs } from "../types";
 import { Template } from "@huggingface/jinja";
 import { makeRequestOptionsFromResolvedModel } from "../lib/makeRequestOptions";
-import fs from "fs";
-import path from "path";
-import { existsSync as pathExists } from "node:fs";
+import { templates } from "./templates.exported";
 
 const PYTHON_CLIENTS = ["huggingface_hub", "fal_client", "requests", "openai"] as const;
 const JS_CLIENTS = ["fetch", "huggingface.js", "openai"] as const;
@@ -44,34 +42,18 @@ interface TemplateParams {
 
 // Helpers to find + load templates
 
-const rootDirFinder = (): string => {
-	let currentPath =
-		typeof import.meta !== "undefined" && import.meta.url
-			? path.normalize(new URL(import.meta.url).pathname) /// for ESM
-			: __dirname; /// for CJS
-
-	while (currentPath !== "/") {
-		if (pathExists(path.join(currentPath, "package.json"))) {
-			return currentPath;
-		}
-
-		currentPath = path.normalize(path.join(currentPath, ".."));
-	}
-
-	return "/";
-};
-
-const templatePath = (language: InferenceSnippetLanguage, client: Client, templateName: string): string =>
-	path.join(rootDirFinder(), "src", "snippets", "templates", language, client, `${templateName}.jinja`);
 const hasTemplate = (language: InferenceSnippetLanguage, client: Client, templateName: string): boolean =>
-	pathExists(templatePath(language, client, templateName));
+	templates[language]?.[client]?.[templateName] !== undefined;
 
 const loadTemplate = (
 	language: InferenceSnippetLanguage,
 	client: Client,
 	templateName: string
 ): ((data: TemplateParams) => string) => {
-	const template = fs.readFileSync(templatePath(language, client, templateName), "utf8");
+	const template = templates[language]?.[client]?.[templateName];
+	if (!template) {
+		throw new Error(`Template not found: ${language}/${client}/${templateName}`);
+	}
 	return (data: TemplateParams) => new Template(template).render({ ...data });
 };