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It provides functionality for making predictions, streaming responses.","maintainers":[{"name":"aykes","email":"ratio123@yandex.ru"}],"readme":"# FlyMyAI SDK\r\n\r\nThis repository contains the FlyMyAI SDK, a TypeScript library for interacting with the FlyMyAI API. It provides functionality for making predictions, streaming responses.\r\n\r\n## Installation\r\n\r\nTo install the SDK, use npm or yarn:\r\n\r\n```bash\r\nnpm install flymyai-js-client\r\n```\r\n\r\nor\r\n\r\n```bash\r\nyarn add flymyai-js-client\r\n```\r\n\r\n## Usage\r\n\r\n### Initialization\r\n\r\nTo use the FlyMyAI SDK, you need to initialize it with your API key.\r\n\r\n```typescript\r\nimport { FlyMyAI } from \"flymyai-js-client\";\r\n\r\nconst client = new FlyMyAI({ apiKey: \"YOUR_API_KEY\" });\r\n```\r\n\r\n### Making Predictions\r\n\r\nYou can make predictions by calling the `predict` method. Here’s an example:\r\n\r\n```typescript\r\nconst payload = {\r\n  prompt: \"Funny cat with stupid dog\",\r\n  height: 1024,\r\n  width: 1024,\r\n  num_inference_steps: 26,\r\n  guidance_scale: \"0\",\r\n  seed: 1654,\r\n  negative_prompt: \"0\",\r\n};\r\n\r\nconst model = \"flymyai/HiDream-I1-dev\";\r\nconst apiKey = process.env.REACT_APP_FLYMYAI_API_KEY || \"\";\r\n\r\nconst client = new FlyMyAI({ apiKey });\r\n\r\ntry {\r\n  const result = await client.predict(payload, model);\r\n  console.log(\"Prediction result:\", result.output_data[\"sample\"][0]);\r\n} catch (error) {\r\n  console.error(\"Error making prediction:\", error);\r\n}\r\n```\r\n\r\n### Streaming Predictions\r\n\r\nIf you want to stream predictions, you can use the `stream` method. Here’s how:\r\n\r\n```typescript\r\nconst payload = {\r\n  i_prompt:\r\n    \"An astronaut riding a rainbow unicorn, cinematic, dramatic, photorealistic\",\r\n  i_negative_prompt: \"Dark colors, gloomy atmosphere, horror\",\r\n};\r\n\r\nconst model = \"flymyai/SDTurboFMAAceleratedH100\";\r\n\r\nasync function streamPredictions() {\r\n  try {\r\n    for await (const result of client.stream(payload, model)) {\r\n      console.log(\r\n        \"Streaming result:\",\r\n        (result.output_data?.output || [])[0] || \"\"\r\n      );\r\n    }\r\n  } catch (error) {\r\n    console.error(\"Error during streaming:\", error);\r\n  }\r\n}\r\n\r\nstreamPredictions();\r\n```\r\n\r\n### Long-Running Predictions with Polling\r\n\r\nFor operations requiring extended processing time, use predictAsync with polling:\r\n\r\n```typescript\r\nconst client = new FlyMyAI({\r\n  apiKey: \"YOUR_API_KEY\",\r\n});\r\n\r\nconst model = \"flymyai/flux-schnell-lora\";\r\nconst payload = {\r\n  prompt: \"a robotic funny cat with robotic stupid dog\",\r\n  height: 1024,\r\n  width: 1024,\r\n  num_inference_steps: 4,\r\n  guidance_scale: \"0\",\r\n  seed: 1654,\r\n  lora_url:\r\n    \"https://civitai.com/api/download/models/730973?type=Model&format=SafeTensor\",\r\n  lora_scale: \"0.9\",\r\n};\r\n\r\ntry {\r\n  const result = await client.predictAsync(payload, model, {\r\n    interval: 3000, // Poll every 3 seconds\r\n    signal: AbortSignal.timeout(3_600_000), // 1 hour timeout\r\n  });\r\n  console.log(\r\n    \"Prediction URL:\",\r\n    result.inference_responses[0].response.sample[0].url\r\n  );\r\n} catch (error) {\r\n  if (error instanceof FlyMyAIError) {\r\n    console.error(\"Prediction failed:\", error.message);\r\n  }\r\n}\r\n```\r\n\r\n### Waiting for all requests to be completed\r\n\r\n```typescript\r\nimport { FlyMyAI } from \"your-module-path\";\r\n\r\nasync function main() {\r\n  const apiKey = \"fly-secret-key\";\r\n  const model = \"flymyai/model-name\";\r\n  const client = new FlyMyAI({ apiKey });\r\n\r\n  const payloads: Array<Record<string, any>> = Array.from(\r\n    { length: 9 },\r\n    (_, count) => ({\r\n      prompt: \"a robotic funny cat with robotic stupid dog\",\r\n      height: 1024,\r\n      width: 1024,\r\n      num_inference_steps: 4,\r\n      guidance_scale: \"0\",\r\n      seed: 1654,\r\n      lora_url:\r\n        \"https://civitai.com/api/download/models/730973?type=Model&format=SafeTensor\",\r\n      lora_scale: \"0.9\",\r\n    })\r\n  );\r\n\r\n  try {\r\n    const results = await Promise.all(\r\n      payloads.map((payload) =>\r\n        client.predictAsync<AsyncPredictionResult>(payload, model, {\r\n          interval: 3000,\r\n          signal: createAbortSignal(36_000_000),\r\n        })\r\n      )\r\n    );\r\n\r\n    for (const result of results) {\r\n      const url = result.inference_responses?.[0]?.response?.sample?.[0]?.url;\r\n      if (url) {\r\n        console.log(result.inference_responses[0].response.sample[0].url);\r\n      }\r\n    }\r\n  } catch (error) {\r\n    console.error(\"Error processing predictions:\", error);\r\n  }\r\n}\r\n\r\nmain().catch(console.error);\r\n```\r\n\r\n### Separately getting predict_id and the result of generation\r\n\r\nGet predict_id\r\n\r\n```typescript\r\ntry {\r\n  // Start the async prediction and get the prediction ID\r\n  const predictionId = await client.asyncPredict(payload, model);\r\n  console.log(\"Async prediction started. Prediction ID:\", predictionId);\r\n\r\n  // You can now store this predictionId to check results later\r\n  // or pass it to another system component\r\n} catch (error) {\r\n  if (error instanceof FlyMyAIError) {\r\n    console.error(\"Failed to start async prediction:\", error.message);\r\n  } else {\r\n    console.error(\"Unknown error:\", error);\r\n  }\r\n}\r\n```\r\n\r\nRetrieve the final results when ready\r\n\r\n```typescript\r\ntry {\r\n  const result = await client.checkAsyncResult(model, storedPredictionId);\r\n\r\n  if (result.status === \"pending\") {\r\n    console.log(\"Prediction is still processing\");\r\n  } else {\r\n    console.log(\r\n      \"Final prediction result:\",\r\n      result.inference_responses[0].response.sample[0].url\r\n    );\r\n  }\r\n} catch (error) {\r\n  console.error(\"Error checking prediction status:\", error);\r\n}\r\n```\r\n\r\n### Error Handling\r\n\r\nUses custom error handling. If an error occurs, a `FlyMyAIError` will be thrown. You can catch it like this:\r\n\r\n```typescript\r\ntry {\r\n  const response = await flyMyAI.predict(payload, model);\r\n} catch (error) {\r\n  if (error instanceof FlyMyAIError) {\r\n    console.error(\"FlyMyAI error:\", error.message);\r\n  } else {\r\n    console.error(\"Unknown error:\", error);\r\n  }\r\n}\r\n```\r\n","readmeFilename":"README.md"}