base gradio app

.env.example

@@ -0,0 +1,3 @@
+MODEL_DIR=models
+MODEL_FILE=model.pt
+GRADIO_WATCH_DIRS="planparser,src"

.gitattributes

@@ -0,0 +1 @@
+*.jpg filter=lfs diff=lfs merge=lfs -text

planparser/app.py

@@ -0,0 +1,42 @@
+import os
+import gradio as gr
+
+MODEL_DIR = os.getenv("MODEL_DIR", "models")
+MODEL_FILE = os.getenv("MODEL_FILE", "model.pt")
+
+_model = None
+
+
+def get_model_path() -> str:
+    path = os.path.join(MODEL_DIR, MODEL_FILE)
+    if not os.path.exists(path):
+        raise FileNotFoundError(
+            f"Model not found: {path}. Put the file there or set MODEL_DIR and MODEL_FILE."
+        )
+    return path
+
+
+def load_model():
+    global _model
+    if _model is not None:
+        return _model
+    import torch
+
+    _model = torch.jit.load(get_model_path(), map_location="cpu").eval()
+    return _model
+
+
+def predict(text: str) -> str:
+    _ = load_model()
+    return f"Loaded local model: {os.path.join(MODEL_DIR, MODEL_FILE)}. Input: {text}"
+
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(label="Input"),
+    outputs=gr.Textbox(label="Output"),
+    title="Minimal demo",
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

pyproject.toml

@@ -0,0 +1,11 @@
+[project]
+name = "planparser"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "gradio>=6.2.0",
+    "torch>=2.9.1",
+    "torchvision>=0.24.1",
+]