Document python text to image snippets (#1016)

Supersedes #994.

This PR adds an `huggingface_hub` snippet for `text-to-image` inference
in Python. I added a test as done in
#1003.

Once this one is approved and merged, I'll move on with all other tasks
that the `InferenceClient` supports.

---------

Co-authored-by: Mishig <[email protected]>

Author: Wauplin

packages/tasks/src/snippets/python.spec.ts

@@ -104,4 +104,41 @@ stream = client.chat.completions.create(
 for chunk in stream:
     print(chunk.choices[0].delta.content, end="")`);
 	});
+
+	it("text-to-image", async () => {
+		const model: ModelDataMinimal = {
+			id: "black-forest-labs/FLUX.1-schnell",
+			pipeline_tag: "text-to-image",
+			tags: [],
+			inference: "",
+		};
+		const snippets = getPythonInferenceSnippet(model, "api_token") as InferenceSnippet[];
+
+		expect(snippets.length).toEqual(2);
+
+		expect(snippets[0].client).toEqual("huggingface_hub");
+		expect(snippets[0].content).toEqual(`from huggingface_hub import InferenceClient
+client = InferenceClient("black-forest-labs/FLUX.1-schnell", token="api_token")
+
+# output is a PIL.Image object
+image = client.text_to_image("Astronaut riding a horse")`);
+
+		expect(snippets[1].client).toEqual("requests");
+		expect(snippets[1].content).toEqual(`import requests
+
+API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
+headers = {"Authorization": "Bearer api_token"}
+
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.content
+image_bytes = query({
+	"inputs": "Astronaut riding a horse",
+})
+
+# You can access the image with PIL.Image for example
+import io
+from PIL import Image
+image = Image.open(io.BytesIO(image_bytes))`);
+	});
 });

packages/tasks/src/snippets/python.ts

@@ -4,6 +4,11 @@ import { stringifyGenerationConfig, stringifyMessages } from "./common.js";
 import { getModelInputSnippet } from "./inputs.js";
 import type { InferenceSnippet, ModelDataMinimal } from "./types.js";
 
+const snippetImportInferenceClient = (model: ModelDataMinimal, accessToken: string): string =>
+	`from huggingface_hub import InferenceClient
+client = InferenceClient("${model.id}", token="${accessToken || "{API_TOKEN}"}")
+`;
+
 export const snippetConversational = (
 	model: ModelDataMinimal,
 	accessToken: string,
@@ -161,18 +166,28 @@ export const snippetFile = (model: ModelDataMinimal): InferenceSnippet => ({
 output = query(${getModelInputSnippet(model)})`,
 });
 
-export const snippetTextToImage = (model: ModelDataMinimal): InferenceSnippet => ({
-	content: `def query(payload):
+export const snippetTextToImage = (model: ModelDataMinimal, accessToken: string): InferenceSnippet[] => [
+	{
+		client: "huggingface_hub",
+		content: `${snippetImportInferenceClient(model, accessToken)}
+# output is a PIL.Image object
+image = client.text_to_image(${getModelInputSnippet(model)})`,
+	},
+	{
+		client: "requests",
+		content: `def query(payload):
 	response = requests.post(API_URL, headers=headers, json=payload)
 	return response.content
 image_bytes = query({
 	"inputs": ${getModelInputSnippet(model)},
 })
+
 # You can access the image with PIL.Image for example
 import io
 from PIL import Image
 image = Image.open(io.BytesIO(image_bytes))`,
-});
+	},
+];
 
 export const snippetTabular = (model: ModelDataMinimal): InferenceSnippet => ({
 	content: `def query(payload):
@@ -288,12 +303,14 @@ export function getPythonInferenceSnippet(
 		return snippets.map((snippet) => {
 			return {
 				...snippet,
-				content: `import requests
+				content: snippet.content.includes("requests")
+					? `import requests
 
 API_URL = "https://api-inference.huggingface.co/models/${model.id}"
 headers = {"Authorization": ${accessToken ? `"Bearer ${accessToken}"` : `f"Bearer {API_TOKEN}"`}}
 
-${snippet.content}`,
+${snippet.content}`
+					: snippet.content,
 			};
 		});
 	}