Build your first AI app using Serverless AI Inferencing

The Rust code snippets below are taken from the Fermyon Serverless AI Examples.

$ spin new http-rust
Enter a name for your new application: hello-world
Description: My first Serverless AI app
HTTP base: /
HTTP path: /...

The Python code snippets below are taken from the Fermyon Serverless AI Examples.

$ spin new http-py
Enter a name for your new application: hello-world
Description: My first Serverless AI app
HTTP base: /
HTTP path: /...

The TypeScript code snippets below are taken from the Fermyon Serverless AI Examples.

$ spin new http-ts
Enter a name for your new application: hello-world
Description: My first Serverless AI app
HTTP base: /
HTTP path: /...

spin_manifest_version = "1"
authors = ["Your Name <your-name@example.com>"]
description = ""
name = "hello-world"
trigger = { type = "http", base = "/" }
version = "0.1.0"
[[component]]
id = "hello-world"
source = "target/wasm32-wasi/release/hello-world.wasm"
allowed_http_hosts = []
ai_models = ["llama2-chat"]
key_value_stores = ["default"]
[component.trigger]
route = "/..."
[component.build]
command = "cargo build --target wasm32-wasi --release"
watch = ["src/**/*.rs", "Cargo.toml"]

spin_manifest_version = "1"
authors = ["Your Name <your-name@example.com>"]
description = ""
name = "hello-world"
trigger = { type = "http", base = "/" }
version = "0.1.0"
[[component]]
id = "hello-world"
source = "target/hello-world.wasm"
exclude_files = ["**/node_modules"]
ai_models = ["llama2-chat"]
[component.trigger]
route = "/..."
[component.build]
command = "npm run build"
watch = ["src/index.ts"]

spin_manifest_version = "1"
authors = ["Your Name <your-name@example.com>"]
description = ""
name = "hello-world"
trigger = { type = "http", base = "/" }
version = "0.1.0"  
[[component]]
id = "hello-world"
source = "target/hello-world.wasm"
exclude_files = ["**/node_modules"]
ai_models = ["llama2-chat"]
[component.trigger]
route = "/..."
[component.build]
command = "spin py2wasm app -o app.wasm"
watch = ["app.py", "Pipfile"]

use anyhow::{Context, Result};
use spin_sdk::{
   http::{Request, Response},
   http_component, llm,
};
/// A simple Spin HTTP component.
#[http_component]
fn hello_world(_req: Request) -> Result<Response> {
   let model = llm::InferencingModel::Llama2Chat;
   let inference = llm::infer(model, "Can you tell me a joke about cats".into());
   Ok(http::Response::builder()
       .status(200)
       .body(Some(format!("{:?}", inference).into()))?)
}  

import { Llm, InferencingModels, HandleRequest, HttpRequest, HttpResponse } from  "@fermyon/spin-sdk"
const  model = InferencingModels.Llama2Chat
export  const  handleRequest: HandleRequest = async  function (request: HttpRequest): Promise<HttpResponse> {
const  prompt = "Can you tell me a joke about cats"
const  out = Llm.infer(model, prompt)
return {
	status:  200,
	body:  out.text
	}
}

from spin_http import Response
from spin_llm import llm_infer
import json
import re

def handle_request(request):
    try:
        result = llm_infer("llama2-chat", "Can you tell me a joke abut cats")
        return Response(200, {"content-type": "text/plain"}, bytes(result.text, "utf-8"))
    except Exception as e:
        return Response(500, {"content-type": "text/plain"}, bytes(f"Error: {str(e)}", "utf-8"))

$ spin build

$ npm install
$ spin build

$ spin build