Initial deployment

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI
+from fastapi.responses import HTMLResponse, JSONResponse
+from pydantic import BaseModel
+from typing import List, Any, Dict, Union
+import pandas as pd
+import joblib
+from pathlib import Path
+
+APP_DIR = Path(__file__).resolve().parent
+MODEL_PATH = APP_DIR / "pricing_model.joblib"
+
+FEATURE_ORDER = [
+    "model_key",
+    "mileage",
+    "engine_power",
+    "fuel",
+    "paint_color",
+    "car_type",
+    "private_parking_available",
+    "has_gps",
+    "has_air_conditioning",
+    "automatic_car",
+    "has_getaround_connect",
+    "has_speed_regulator",
+    "winter_tires",
+]
+
+app = FastAPI(
+    title="Getaround API",
+    version="1.0.0",
+    docs_url=None,   # we serve a custom /docs page to match the project requirement
+    redoc_url=None
+)
+
+model = joblib.load(MODEL_PATH)
+
+class PredictIn(BaseModel):
+    input: List[Any]  # accepts list[dict] OR list[list] (see docs)
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+@app.post("/predict")
+def predict(payload: PredictIn):
+    rows = payload.input
+
+    # Accept either:
+    # 1) list[dict] with keys matching FEATURE_ORDER
+    # 2) list[list] with values in FEATURE_ORDER order
+    if len(rows) == 0:
+        return JSONResponse({"prediction": []})
+
+    first = rows[0]
+    if isinstance(first, dict):
+        X = pd.DataFrame(rows)
+        # Ensure column order & missing columns are handled
+        for c in FEATURE_ORDER:
+            if c not in X.columns:
+                X[c] = None
+        X = X[FEATURE_ORDER]
+    else:
+        # list-like
+        X = pd.DataFrame(rows, columns=FEATURE_ORDER)
+
+    preds = model.predict(X)
+    return {"prediction": [float(p) for p in preds]}
+
+
+from fastapi.responses import HTMLResponse
+from pathlib import Path
+
+BASE_DIR = Path(__file__).resolve().parent
+
+@app.get("/docs", response_class=HTMLResponse)
+def docs():
+    html = (BASE_DIR / "docs.html").read_text(encoding="utf-8")
+    return HTMLResponse(content=html)
+

pricing_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de412ea007542517311520a5c4ec04548e33870de58e9435e74e7099913c34e1
+size 61410759

requirements.txt

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+fastapi==0.111.0
+uvicorn[standard]==0.30.1
+pandas==2.2.2
+numpy==2.0.0
+scikit-learn==1.5.1
+joblib==1.4.2
+streamlit==1.36.0
+openpyxl==3.1.5