gradio space

apps/gradio-space/README.md

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+# gradio-space
+
+Gradio chat UI package for the hackathon Space. Entrypoint: `python -m gradio_space.app`.
+
+Space card metadata lives in the [repository root README.md](../../README.md).

apps/gradio-space/pyproject.toml

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+[project]
+name = "gradio-space"
+version = "0.1.0"
+description = "Gradio chat UI for the Build Small Hackathon Space"
+readme = "README.md"
+authors = [
+    { name = "MSGhais", email = "msghais135@gmail.com" }
+]
+requires-python = ">=3.12"
+dependencies = [
+    "gradio>=5.0.0",
+    "inference",
+]
+
+[tool.uv.sources]
+inference = { workspace = true }
+
+[build-system]
+requires = ["uv_build>=0.8.13,<0.9.0"]
+build-backend = "uv_build"

apps/gradio-space/src/gradio_space/__init__.py

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+from gradio_space.app import build_demo, demo, main
+
+__all__ = ["build_demo", "demo", "main"]

apps/gradio-space/src/gradio_space/app.py

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+import os
+
+import gradio as gr
+
+from inference.factory import get_backend
+
+_backend = get_backend()
+_model_ready = False
+_load_error: str | None = None
+
+
+def _ensure_model_loaded() -> str | None:
+    global _model_ready, _load_error
+
+    if _model_ready:
+        return None
+
+    if _load_error:
+        return _load_error
+
+    try:
+        _backend.load()
+        _model_ready = True
+        return None
+    except Exception as exc:  # noqa: BLE001 — surface model load failures in the UI
+        _load_error = f"Failed to load model: {exc}"
+        return _load_error
+
+
+def chat(message: str, history: list) -> str:
+    load_error = _ensure_model_loaded()
+    if load_error:
+        return load_error
+
+    messages: list[dict[str, str]] = []
+    for item in history:
+        if isinstance(item, dict):
+            messages.append({"role": item["role"], "content": item["content"]})
+        else:
+            user_msg, assistant_msg = item
+            messages.append({"role": "user", "content": user_msg})
+            if assistant_msg:
+                messages.append({"role": "assistant", "content": assistant_msg})
+
+    messages.append({"role": "user", "content": message})
+    return _backend.chat(messages)
+
+
+def build_demo() -> gr.Blocks:
+    model_repo = os.environ.get("MODEL_REPO", "Qwen/Qwen2.5-3B-Instruct-GGUF")
+    model_file = os.environ.get("MODEL_FILE", "qwen2.5-3b-instruct-q4_k_m.gguf")
+    backend_name = os.environ.get("INFERENCE_BACKEND", "llama_cpp")
+
+    with gr.Blocks(title="Small Model Hackathon") as demo:
+        gr.Markdown(
+            f"""
+# Small Model Chat
+
+Local inference via **{backend_name}**. Model loads on first message.
+
+- **Repo:** `{model_repo}`
+- **File:** `{model_file}`
+
+Part of the [Build Small Hackathon](https://huggingface.co/build-small-hackathon).
+"""
+        )
+        gr.ChatInterface(
+            fn=chat,
+            examples=["Hello! What can you help me with?", "Explain llama.cpp in one sentence."],
+        )
+
+    return demo
+
+
+demo = build_demo()
+
+
+def main() -> None:
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=int(os.environ.get("PORT", "7860")),
+    )
+
+
+if __name__ == "__main__":
+    main()