updated the deployment code in the backend

.dockerignore

@@ -0,0 +1,9 @@
+.git
+.gitignore
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.pytest_cache/
+.mypy_cache/
+notebooks/

Dockerfile

@@ -0,0 +1,19 @@
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH" \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -10,3 +10,39 @@ short_description: This is an ml inference space
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+## Inference API
+
+This Space runs a FastAPI app with an XGBoost model loaded from `xg_bost.pkl`.
+
+- Health check: `GET /health`
+- Prediction endpoint: `POST /predit`
+
+### Sample request
+
+```json
+{
+  "distance_km": 7.93,
+  "weather_condition": "Windy",
+  "traffic_level": "Low",
+  "vehicle_type": "scooter",
+  "temperature_c": 23.0,
+  "humidity_pct": 55.0,
+  "precipitation_mm": 0.0,
+  "preparation_time_min": 12.0,
+  "courier_experience_yrs": 1.0,
+  "worker_age": 29.0,
+  "worker_rating": 4.7,
+  "order_type": "Unknown",
+  "weather_risk": 7.0,
+  "traffic_risk": 25.0
+}
+```
+
+### Sample response
+
+```json
+{
+  "prediction": 1.5953301191329956
+}
+```

app/main.py

@@ -0,0 +1,113 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, Union
+
+import joblib
+import pandas as pd
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel, Field
+
+MODEL_PATH = Path(__file__).resolve().parent.parent / "xg_bost.pkl"
+
+# LabelEncoder-style mappings (alphabetical order) for categorical features.
+CATEGORICAL_MAPPINGS = {
+    "weather_condition": {
+        "Clear": 0.0,
+        "Cloudy": 1.0,
+        "Fog": 2.0,
+        "Rain": 3.0,
+        "Snow": 4.0,
+        "Windy": 5.0,
+    },
+    "traffic_level": {
+        "High": 0.0,
+        "Low": 1.0,
+        "Medium": 2.0,
+        "Very High": 3.0,
+        "Very Low": 4.0,
+    },
+    "vehicle_type": {
+        "car": 0.0,
+        "cycle": 1.0,
+        "ev": 2.0,
+        "motorcycle": 3.0,
+        "scooter": 4.0,
+    },
+    "order_type": {
+        "Buffet": 0.0,
+        "Drinks": 1.0,
+        "Meal": 2.0,
+        "Snack": 3.0,
+        "Unknown": 4.0,
+    },
+}
+
+model = joblib.load(MODEL_PATH)
+FEATURE_ORDER = list(model.feature_names_in_)
+
+
+class PredictInput(BaseModel):
+    distance_km: float = Field(..., example=7.93)
+    weather_condition: Union[str, float, int] = Field(..., example="Windy")
+    traffic_level: Union[str, float, int] = Field(..., example="Low")
+    vehicle_type: Union[str, float, int] = Field(..., example="scooter")
+    temperature_c: float = Field(..., example=23.0)
+    humidity_pct: float = Field(..., example=55.0)
+    precipitation_mm: float = Field(..., example=0.0)
+    preparation_time_min: float = Field(..., example=12.0)
+    courier_experience_yrs: float = Field(..., example=1.0)
+    worker_age: float = Field(..., example=29.0)
+    worker_rating: float = Field(..., example=4.7)
+    order_type: Union[str, float, int] = Field(..., example="Unknown")
+    weather_risk: float = Field(..., example=7.0)
+    traffic_risk: float = Field(..., example=25.0)
+
+
+def _encode_categorical(feature_name: str, value: Any) -> float:
+    if isinstance(value, (int, float)) and not isinstance(value, bool):
+        return float(value)
+
+    mapping = CATEGORICAL_MAPPINGS[feature_name]
+    raw = str(value).strip()
+    if raw in mapping:
+        return mapping[raw]
+
+    lower_map = {k.lower(): v for k, v in mapping.items()}
+    if raw.lower() in lower_map:
+        return lower_map[raw.lower()]
+
+    allowed = ", ".join(mapping.keys())
+    raise HTTPException(
+        status_code=422,
+        detail=f"Unknown value '{value}' for '{feature_name}'. Allowed values: {allowed}",
+    )
+
+
+def _prepare_features(payload: PredictInput) -> pd.DataFrame:
+    values = payload.model_dump()
+    row: dict[str, float] = {}
+
+    for feature in FEATURE_ORDER:
+        value = values[feature]
+        if feature in CATEGORICAL_MAPPINGS:
+            row[feature] = _encode_categorical(feature, value)
+        else:
+            row[feature] = float(value)
+
+    return pd.DataFrame([row], columns=FEATURE_ORDER)
+
+
+app = FastAPI(title="Gitwire XGBoost Inference API", version="1.0.0")
+
+
+@app.get("/health")
+def health() -> dict[str, str]:
+    return {"status": "ok"}
+
+
+@app.post("/predit")
+def predit(payload: PredictInput) -> dict[str, float]:
+    features = _prepare_features(payload)
+    prediction = float(model.predict(features)[0])
+    return {"prediction": prediction}

requirements.txt

@@ -0,0 +1,7 @@
+fastapi
+uvicorn[standard]
+pandas
+numpy
+scikit-learn
+xgboost
+joblib

xg_bost.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc8263f811015caf03f53ddc1e8fbc2d96a214c3ae3fce18bf52735749b2a463
+size 1292025