test

.devcontainer/Dockerfile

@@ -0,0 +1,8 @@
+# syntax=docker/dockerfile:1
+FROM python:3.11.4-bullseye
+
+# run this before copying requirements for cache efficiency
+RUN pip install --upgrade pip
+COPY .devcontainer/requirements.txt .
+
+RUN pip install -r requirements.txt

.devcontainer/devcontainer.json

@@ -0,0 +1,38 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the README at:
+// https://github.com/microsoft/vscode-dev-containers/tree/v0.241.1/containers/docker-existing-dockerfile
+{
+	"name": "Existing Dockerfile",
+
+	// Sets the run context to one level up instead of the .devcontainer folder.
+	"context": "..",
+
+	// Update the 'dockerFile' property if you aren't using the standard 'Dockerfile' filename.
+	"dockerFile": "Dockerfile",
+	"customizations": {
+		"vscode": {
+			"extensions": [
+				"ms-python.python",
+				"ms-python.vscode-pylance",
+				"ms-toolsai.jupyter",
+				"ms-python.black-formatter",
+				"GitHub.copilot",
+				"GitHub.copilot-chat"
+			]
+		}
+	},
+
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	"forwardPorts": [7860]
+
+	// Uncomment the next line to run commands after the container is created - for example installing curl.
+	// "postCreateCommand": "apt-get update && apt-get install -y curl",
+
+	// Uncomment when using a ptrace-based debugger like C++, Go, and Rust
+	// "runArgs": [ "--cap-add=SYS_PTRACE", "--security-opt", "seccomp=unconfined" ],
+
+	// Uncomment to use the Docker CLI from inside the container. See https://aka.ms/vscode-remote/samples/docker-from-docker.
+	// "mounts": [ "source=/var/run/docker.sock,target=/var/run/docker.sock,type=bind" ],
+
+	// Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root.
+	// "remoteUser": "vscode"
+}

.devcontainer/requirements.txt

@@ -0,0 +1,3 @@
+transformers
+torch
+gradio

.gitignore

@@ -0,0 +1 @@
+.DS_Store

app.py

@@ -4,8 +4,6 @@ from transformers import pipeline
 pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
 def predict(input_img):
-    print('hello!')
-    print(input_img)
     predictions = pipeline(input_img)
     return {p["label"]: p["score"] for p in predictions}