Extending Apps

1curl -fsSL https://cli.inference.sh | sh

1belt login

1mkdir my-app && cd my-app2belt init

1my-app/2├── inf.yml           # Configuration3├── inference.py      # Your code4├── requirements.txt  # Dependencies5└── ...

1from inferencesh import BaseApp, BaseAppInput, BaseAppOutput, File2from pydantic import Field3from typing import Optional45class AppInput(BaseAppInput):6    prompt: str = Field(description="What to generate")7    style: str = Field(default="default", description="Output style")89class AppOutput(BaseAppOutput):10    result: str = Field(description="Generated text")11    image: Optional[File] = Field(default=None, description="Generated image")1213class App(BaseApp):14    async def setup(self, metadata):15        """Called once when the worker starts.16        Load models, initialize resources here."""17        # self.model = load_your_model()18        pass19    20    async def run(self, input_data: AppInput, metadata) -> AppOutput:21        """Called for each request.22        Process input and return output."""23        24        # Access inputs25        prompt = input_data.prompt26        style = input_data.style27        28        # Log progress (visible in real-time)29        metadata.log("Processing...")30        31        # Do your work32        result = f"Generated: {prompt} in {style} style"33        34        # Return output35        return AppOutput(result=result)36    37    async def unload(self):38        """Called on shutdown. Cleanup resources."""39        pass

1async def run(self, input_data: AppInput, metadata) -> AppOutput:2    # File is ready to use3    image_path = input_data.image.path4    5    with open(image_path, "rb") as f:6        data = f.read()

1async def run(self, input_data: AppInput, metadata) -> AppOutput:2    # Write to a file3    output_path = "/tmp/result.png"4    save_image(output_path)5    6    # Return it7    return AppOutput(image=File(path=output_path))

1namespace: myteam2name: my-app3title: My App4description: What my app does5category: image6kernel: python-3.1178resources:9  gpu:10    count: 111    vram: 8000       # MB12    type: nvidia13  ram: 16000         # MB

1torch>=2.0.02transformers>=4.30.03accelerate4huggingface_hub5pillow>=9.0.0

1ffmpeg2libgl1-mesa-glx

1# inf.yml2secrets:3  - key: OPENAI_API_KEY4    description: For GPT-4 API calls56  - key: REPLICATE_API_TOKEN7    description: For model inference8    optional: true910credentials:11  - key: google.sheets12    description: Read/write spreadsheets1314  - key: x.tweet.write15    description: Post tweets16    optional: true

1from inferencesh import BaseApp, BaseAppInput, BaseAppOutput, File2from pydantic import Field34class UpscaleInput(BaseAppInput):5    image: File = Field(description="Image to upscale")6    scale: int = Field(default=2, description="Scale factor")78class UpscaleOutput(BaseAppOutput):9    image: File = Field(description="Upscaled image")1011class RemoveBgInput(BaseAppInput):12    image: File = Field(description="Image to process")1314class RemoveBgOutput(BaseAppOutput):15    image: File = Field(description="Image with background removed")1617class App(BaseApp):18    async def setup(self, metadata):19        # Load models for all functions20        pass2122    async def upscale(self, input_data: UpscaleInput, metadata) -> UpscaleOutput:23        # Upscale logic24        ...2526    async def remove_bg(self, input_data: RemoveBgInput, metadata) -> RemoveBgOutput:27        # Background removal logic28        ...

1# Test a specific function2belt app test --function upscale --input upscale_input.json34# Test another function5belt app test --function remove_bg --input removebg_input.json

1belt app run user/image-tools --function upscale --input input.json

1belt app deploy

1┌──────────────────────────────────────┐2│         deployment summary           │3├──────────────────────────────────────┤4│                                      │5│ final status: success                │6│ duration: 45s                        │7│ user: yourname                       │8│ app: my-app                          │9│ link: https://app.inference.sh/...   │10│                                      │11└──────────────────────────────────────┘

1belt app test --save-example2# Creates input.json

1belt app test --input input.json23# Multi-function apps: specify which function to call4belt app test --function upscale --input input.json

1belt app test --input input.json --debug

1❌ Don't: One app that does everything2✓ Do: Single-purpose apps that compose well

1async def setup(self, metadata):2    # Good: Download on first run, cached afterward3    # (huggingface_hub must be in requirements.txt)4    from huggingface_hub import hf_hub_download5    self.model_path = hf_hub_download("model-name", "model.bin")

1async def run(self, input_data: AppInput, metadata) -> AppOutput:2    metadata.log("Step 1: Loading image...")3    # ...4    metadata.log("Step 2: Processing...")5    # ...6    metadata.log("Step 3: Saving result...")

1async def run(self, input_data: AppInput, metadata) -> AppOutput:2    if not input_data.image.exists():3        raise ValueError("Image file not found")4    5    try:6        result = process(input_data)7    except ProcessingError as e:8        metadata.log(f"Error: {e}")9        raise RuntimeError(f"Processing failed: {e}")