Create app.py

app.py

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+# app.py — Single-file Transit Tracker API for Hugging Face (No extra files required)
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import Dict, Any
+import numpy as np
+import joblib
+import os
+import math
+import time
+
+app = FastAPI(title="Transit Tracker API - Single File Version")
+
+# -----------------------------
+# In-memory Vehicle Locations
+# -----------------------------
+VEHICLES: Dict[str, Dict[str, Any]] = {}
+
+# -----------------------------
+# Train a lightweight ETA model
+# -----------------------------
+def train_and_save_model():
+    from sklearn.ensemble import RandomForestRegressor
+
+    np.random.seed(42)
+    N = 3000
+
+    dist = np.random.exponential(400, N)
+    speed = np.random.uniform(5, 15, N)
+    tod = np.random.uniform(0, 86400, N)
+    dow = np.random.randint(0, 7, N)
+
+    tod_sin = np.sin(2*np.pi * tod/86400)
+    tod_cos = np.cos(2*np.pi * tod/86400)
+
+    eta = dist/(speed*0.277) + np.random.randint(10, 120, N)
+
+    X = np.column_stack([dist, speed, tod_sin, tod_cos, dow])
+    y = eta
+
+    model = RandomForestRegressor(n_estimators=30)
+    model.fit(X, y)
+    joblib.dump(model, "model.joblib")
+    print("MODEL TRAINED AND SAVED ✔")
+
+# -----------------------------
+# Load or Train Model
+# -----------------------------
+def load_model():
+    if not os.path.exists("model.joblib"):
+        train_and_save_model()
+    return joblib.load("model.joblib")
+
+MODEL = load_model()
+
+# -----------------------------
+# JSON Schemas
+# -----------------------------
+class VehicleUpdate(BaseModel):
+    vehicle_id: str
+    lat: float
+    lon: float
+    speed: float = 0.0
+
+class PredictRequest(BaseModel):
+    distance_to_stop_m: float
+    speed_mps: float = 4.0
+
+
+# -----------------------------
+# Utils
+# -----------------------------
+def haversine(lat1, lon1, lat2, lon2):
+    R = 6371000
+    p1, p2 = math.radians(lat1), math.radians(lat2)
+    dphi = math.radians(lat2 - lat1)
+    dl = math.radians(lon2 - lon1)
+    a = math.sin(dphi / 2) ** 2 + math.cos(p1) * math.cos(p2) * math.sin(dl / 2) ** 2
+    return R * 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
+
+
+# -----------------------------
+# ROOT
+# -----------------------------
+@app.get("/")
+def root():
+    return {"message": "Transit Tracker API Running", "vehicles": len(VEHICLES)}
+
+
+# -----------------------------
+# LIVE VEHICLE LOCATION APIs
+# -----------------------------
+@app.post("/vehicle/update")
+def update_vehicle(v: VehicleUpdate):
+    VEHICLES[v.vehicle_id] = {
+        "lat": v.lat,
+        "lon": v.lon,
+        "speed": v.speed,
+        "timestamp": time.time()
+    }
+    return {"success": True, "vehicle": VEHICLES[v.vehicle_id]}
+
+
+@app.get("/vehicles")
+def get_all_vehicles():
+    return VEHICLES
+
+
+# -----------------------------
+# ETA PREDICTION
+# -----------------------------
+@app.post("/predict")
+def predict_eta(req: PredictRequest):
+    dist = req.distance_to_stop_m
+    if dist < 0:
+        raise HTTPException(400, "distance cannot be negative")
+
+    t = time.gmtime()
+    seconds = t.tm_hour * 3600 + t.tm_min * 60 + t.tm_sec
+    tod_sin = np.sin(2*np.pi * seconds/86400)
+    tod_cos = np.cos(2*np.pi * seconds/86400)
+    dow = t.tm_wday
+
+    X = np.array([[dist, req.speed_mps, tod_sin, tod_cos, dow]])
+    eta_sec = float(MODEL.predict(X)[0])
+
+    return {"eta_seconds": eta_sec, "eta_minutes": eta_sec / 60}