Auto-deploy from GitHub Actions

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

.github/workflows/ci_deploy_hf.yml

@@ -0,0 +1,37 @@
+name: CI + Deploy to Hugging Face Space
+
+on:
+  push:
+    branches: ["main"]
+  workflow_dispatch:
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Git
+        run: |
+          git config --global user.email "actions@github.com"
+          git config --global user.name "github-actions"
+
+      - name: Deploy to Hugging Face Space
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          HF_SPACE: ${{ secrets.HF_SPACE }} # like: username/space_name
+        run: |
+          rm -rf hf_space
+          git clone https://user:$HF_TOKEN@huggingface.co/spaces/$HF_SPACE hf_space
+
+          rsync -av --delete \
+            --exclude ".git" \
+            --exclude "hf_space" \
+            ./ hf_space/
+
+          cd hf_space
+          git add .
+          git commit -m "Auto-deploy from GitHub Actions" || echo "No changes to commit"
+          git push

Dockerfile

@@ -1,18 +1,14 @@
-FROM python:3.11-slim
-
-ENV PYTHONDONTWRITEBYTECODE=1
-ENV PYTHONUNBUFFERED=1
-
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential gcc \
- && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-COPY . .
-
-EXPOSE 7860
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+FROM python:3.11-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+CMD ["uvicorn", "app_ml:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,11 +0,0 @@
----
-title: Class10 Ml Fastapi
-emoji: 🚀
-colorFrom: gray
-colorTo: green
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py → app_ml.py

@@ -1,112 +1,113 @@
-# ---------- Demo Data Example ----------
-DEMO_PREDICT_BODY = {
-    "sepal_length": 5.1,
-    "sepal_width": 3.5,
-    "petal_length": 1.4,
-    "petal_width": 0.2
-}
-
-# app_ml.py
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel, Field
-from typing import List, Dict
-import os
-
-import numpy as np
-import joblib
-
-from sklearn.datasets import load_iris
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.model_selection import train_test_split
-
-APP_VERSION = "1.0.0"
-MODEL_DIR = "/tmp/models"
-MODEL_PATH = os.path.join(MODEL_DIR, "iris_rf.joblib")
-
-app = FastAPI(
-    title="Class 8 - ML Model Serving (Iris)",
-    version=APP_VERSION,
-    description="Serve a scikit-learn model via FastAPI with input validation."
-)
-
-# ---------- Schemas ----------
-class IrisFeatures(BaseModel):
-    sepal_length: float = Field(..., ge=0.0, le=10.0)
-    sepal_width: float = Field(..., ge=0.0, le=10.0)
-    petal_length: float = Field(..., ge=0.0, le=10.0)
-    petal_width: float = Field(..., ge=0.0, le=10.0)
-
-class PredictResponse(BaseModel):
-    ok: bool
-    model_version: str
-    predicted_label: str
-    predicted_class_index: int
-    probabilities: Dict[str, float]
-
-# ---------- Model utilities ----------
-def train_and_save_model(path: str):
-    os.makedirs(os.path.dirname(path), exist_ok=True)
-
-    iris = load_iris()
-    X = iris.data
-    y = iris.target
-    class_names = iris.target_names
-
-    X_train, X_test, y_train, y_test = train_test_split(
-        X, y, test_size=0.2, random_state=42, stratify=y
-    )
-
-    model = RandomForestClassifier(
-        n_estimators=200,
-        random_state=42
-    )
-    model.fit(X_train, y_train)
-
-    payload = {
-        "model": model,
-        "class_names": class_names.tolist(),
-        "feature_names": iris.feature_names,
-        "version": APP_VERSION
-    }
-    joblib.dump(payload, path)
-
-def load_model(path: str):
-    if not os.path.exists(path):
-        train_and_save_model(path)
-    return joblib.load(path)
-
-MODEL_BUNDLE = load_model(MODEL_PATH)
-MODEL = MODEL_BUNDLE["model"]
-CLASS_NAMES = MODEL_BUNDLE["class_names"]
-MODEL_VERSION = MODEL_BUNDLE.get("version", "unknown")
-
-# ---------- Endpoints ----------
-@app.get("/health")
-def health():
-    return {"status": "ok", "model_loaded": True, "model_version": MODEL_VERSION}
-
-@app.post("/v1/predict", response_model=PredictResponse)
-def predict(features: IrisFeatures):
-    try:
-        x = np.array([[
-            features.sepal_length,
-            features.sepal_width,
-            features.petal_length,
-            features.petal_width
-        ]], dtype=float)
-
-        proba = MODEL.predict_proba(x)[0]
-        idx = int(np.argmax(proba))
-        label = CLASS_NAMES[idx]
-
-        prob_map = {CLASS_NAMES[i]: float(proba[i]) for i in range(len(CLASS_NAMES))}
-
-        return PredictResponse(
-            ok=True,
-            model_version=MODEL_VERSION,
-            predicted_label=label,
-            predicted_class_index=idx,
-            probabilities=prob_map
-        )
-    except Exception:
-        raise HTTPException(status_code=500, detail="Prediction failed")
+# ---------- Demo Data Example ----------
+DEMO_PREDICT_BODY = {
+    "sepal_length": 5.1,
+    "sepal_width": 3.5,
+    "petal_length": 1.4,
+    "petal_width": 0.5
+}
+
+# app_ml.py
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+from typing import List, Dict
+import os
+
+import numpy as np
+import joblib
+
+from sklearn.datasets import load_iris
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+
+APP_VERSION = "1.0.0"
+MODEL_DIR = "/tmp/models"
+MODEL_PATH = os.path.join(MODEL_DIR, "iris_rf.joblib")
+
+app = FastAPI(
+    title="Class 8 - ML Model Serving (Iris)",
+    version=APP_VERSION,
+    description="Serve a scikit-learn model via FastAPI with input validation."
+)
+
+# ---------- Schemas ----------
+class IrisFeatures(BaseModel):
+    sepal_length: float = Field(..., ge=0.0, le=10.0)
+    sepal_width: float = Field(..., ge=0.0, le=10.0)
+    petal_length: float = Field(..., ge=0.0, le=10.0)
+    petal_width: float = Field(..., ge=0.0, le=10.0)
+
+class PredictResponse(BaseModel):
+    ok: bool
+    model_version: str
+    predicted_label: str
+    predicted_class_index: int
+    probabilities: Dict[str, float]
+
+# ---------- Model utilities ----------
+def train_and_save_model(path: str):
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+
+    iris = load_iris()
+    X = iris.data
+    y = iris.target
+    class_names = iris.target_names
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=42, stratify=y
+    )
+
+    model = RandomForestClassifier(
+        n_estimators=200,
+        random_state=42
+    )
+    model.fit(X_train, y_train)
+
+    payload = {
+        "model": model,
+        "class_names": class_names.tolist(),
+        "feature_names": iris.feature_names,
+        "version": APP_VERSION
+    }
+    joblib.dump(payload, path)
+
+def load_model(path: str):
+    if not os.path.exists(path):
+        train_and_save_model(path)
+    return joblib.load(path)
+
+MODEL_BUNDLE = load_model(MODEL_PATH)
+MODEL = MODEL_BUNDLE["model"]
+CLASS_NAMES = MODEL_BUNDLE["class_names"]
+MODEL_VERSION = MODEL_BUNDLE.get("version", "unknown")
+
+# ---------- Endpoints ----------
+@app.get("/health")
+def health():
+    return {"status": "ok", "model_loaded": True, "model_version": MODEL_VERSION}
+
+@app.post("/v1/predict", response_model=PredictResponse)
+def predict(features: IrisFeatures):
+    try:
+        x = np.array([[
+            features.sepal_length,
+            features.sepal_width,
+            features.petal_length,
+            features.petal_width
+        ]], dtype=float)
+
+        proba = MODEL.predict_proba(x)[0]
+        idx = int(np.argmax(proba))
+        label = CLASS_NAMES[idx]
+
+        prob_map = {CLASS_NAMES[i]: float(proba[i]) for i in range(len(CLASS_NAMES))}
+
+        return PredictResponse(
+            ok=True,
+            model_version=MODEL_VERSION,
+            predicted_label=label,
+            predicted_class_index=idx,
+            probabilities=prob_map
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Prediction failed")
+

render.yaml

@@ -0,0 +1,14 @@
+services:
+  - type: web
+    name: class10-ml-api
+    runtime: docker
+    plan: free
+    region: oregon
+    healthCheckPath: /health
+    envVars:
+      - key: PYTHONUNBUFFERED
+        value: "1"
+      - key: PYTHONDONTWRITEBYTECODE
+        value: "1"
+    dockerCommand: >
+      uvicorn app_ml:app --host 0.0.0.0 --port $PORT

requirements.txt

@@ -2,4 +2,4 @@ fastapi
 uvicorn[standard]
 numpy
 scikit-learn
-joblib
+joblib