new file: server.py

Dockerfile

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+FROM python:3.10-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+ENV PORT=7860
+
+EXPOSE 7860
+
+CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "7860"]

rf_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ddc856310178b00a3743ea7cfce981641154fb85f12177c536025df2ebc5ecd0
+size 143497

server.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from fastapi.middleware.cors import CORSMiddleware
+import numpy as np
+from collections import deque
+import joblib
+import time
+import datetime
+from features import extract_features
+
+app = FastAPI()
+
+# -----------------------------
+# ✅ CORS FIX (IMPORTANT)
+# -----------------------------
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# -----------------------------
+# CONFIG
+# -----------------------------
+WINDOW_SIZE = 120
+CRASH_THRESHOLD = 0.5
+COOLDOWN = 10
+
+model = joblib.load("rf_model.pkl")
+buffer = deque(maxlen=WINDOW_SIZE)
+
+last_crash_time = 0
+
+# -----------------------------
+# 🌍 LOCATION STORAGE
+# -----------------------------
+latest_location = {"lat": 0.0, "lon": 0.0}
+
+
+@app.post("/location")
+def update_location(data: dict):
+    global latest_location
+    latest_location = {
+        "lat": float(data.get("lat", 0)),
+        "lon": float(data.get("lon", 0))
+    }
+    print(f"📍 Location Updated: {latest_location}")
+    return {"status": "location updated"}
+
+
+@app.get("/location")
+def get_location():
+    return latest_location
+
+
+# -----------------------------
+# SENSOR MODEL
+# -----------------------------
+class SensorData(BaseModel):
+    ax: float
+    ay: float
+    az: float
+    gx: float
+    gy: float
+    gz: float
+    speed: float
+    timestamp: float
+
+
+@app.post("/sensor")
+def sensor(data: SensorData):
+    global last_crash_time
+
+    try:
+        buffer.append([
+            data.ax, data.ay, data.az,
+            data.gx, data.gy, data.gz,
+            data.speed
+        ])
+
+        if len(buffer) < WINDOW_SIZE:
+            return {"status": "collecting"}
+
+        window = np.array(buffer)
+
+        # ML prediction
+        features = extract_features(window)
+        prob = model.predict_proba([features])[0][1]
+
+        # physics boost
+        acc = np.sqrt(window[:,0]**2 + window[:,1]**2 + window[:,2]**2)
+        if np.max(acc) > 40:
+            prob += 0.2
+
+        prob = float(min(prob, 1.0))
+
+        # smoothing
+        if not hasattr(sensor, "history"):
+            sensor.history = []
+
+        sensor.history.append(prob)
+        sensor.history = sensor.history[-5:]
+
+        avg_prob = float(sum(sensor.history) / len(sensor.history))
+
+        # confirmation
+        if not hasattr(sensor, "trigger_count"):
+            sensor.trigger_count = 0
+
+        if avg_prob > CRASH_THRESHOLD:
+            sensor.trigger_count += 1
+        else:
+            sensor.trigger_count = 0
+
+        current_time = float(time.time())
+
+        crash_detected = (
+            sensor.trigger_count >= 2 and
+            (current_time - last_crash_time > COOLDOWN)
+        )
+
+        if crash_detected:
+            last_crash_time = current_time
+
+            event_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            confidence = float(min(1.0, avg_prob + 0.2))
+
+            log_message = f"""
+CRASH DETECTED!
+Time: {event_time}
+Confidence: {round(confidence, 3)}
+-----------------------------------
+"""
+
+            print("\033[91m" + log_message + "\033[0m")
+
+            with open("crash_log.txt", "a", encoding="utf-8") as f:
+                f.write(log_message)
+
+            return {
+                "event": "CRASH_DETECTED",
+                "confidence": confidence,
+                "timestamp": current_time
+            }
+
+        return {"status": "monitoring"}
+
+    except Exception as e:
+        print("SERVER ERROR:", e)
+        return {"error": str(e)}
+
+
+@app.get("/")
+def home():
+    return {"status": "Crash Detection Server Running 🚀"}