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import pandas as pd
import numpy as np
from flask import Flask, request, jsonify
from flask_cors import CORS
from datetime import datetime
# ---------------- CONFIG ----------------
LAT_MIN = 12.70
LAT_MAX = 13.30
LON_MIN = 77.30
LON_MAX = 78.00
LAT_GRIDS = 50
LON_GRIDS = 50
THRESHOLD = 0.6
FEATURES = [
"grid_x",
"grid_y",
"day_of_week",
"is_weekend",
"month",
"crime_lag_1",
"crime_lag_7",
"crime_lag_30"
]
# ---------------- APP ----------------
app = Flask(__name__)
CORS(app)
# ---------------- LOAD MODELS ----------------
model1 = joblib.load("model1.pkl")
model2 = joblib.load("model2.pkl")
le = joblib.load("label_encoder.pkl")
# ---------------- LOAD DATA ----------------
full = pd.read_csv("crime_grid_daily_features.csv")
full["FIR_DATE"] = pd.to_datetime(full["FIR_DATE"])
# ---------------- HELPERS ----------------
def latlon_to_grid(lat, lon):
return (
int((lat - LAT_MIN) / ((LAT_MAX - LAT_MIN) / LAT_GRIDS)),
int((lon - LON_MIN) / ((LON_MAX - LON_MIN) / LON_GRIDS))
)
def get_lags(grid_x, grid_y):
hist = full[
(full["grid_x"] == grid_x) &
(full["grid_y"] == grid_y)
].sort_values("FIR_DATE")
if hist.empty:
return 0, 0, 0
return (
hist.tail(1)["crime_count"].mean(),
hist.tail(7)["crime_count"].mean(),
hist.tail(30)["crime_count"].mean()
)
# ---------------- ROUTES ----------------
@app.route("/health", methods=["GET"])
def health():
return jsonify({"status": "ok"})
@app.route("/historical", methods=["GET"])
def historical():
points = raw_points.dropna().sample(min(5000, len(raw_points)))
return jsonify(points.to_dict(orient="records"))
@app.route("/predict", methods=["POST"])
def predict():
data = request.json
lat = float(data["latitude"])
lon = float(data["longitude"])
date = datetime.strptime(data["date"], "%Y-%m-%d")
grid_x, grid_y = latlon_to_grid(lat, lon)
crime_lag_1, crime_lag_7, crime_lag_30 = get_lags(grid_x, grid_y)
row = pd.DataFrame([{
"grid_x": grid_x,
"grid_y": grid_y,
"day_of_week": date.weekday(),
"is_weekend": int(date.weekday() >= 5),
"month": date.month,
"crime_lag_1": crime_lag_1,
"crime_lag_7": crime_lag_7,
"crime_lag_30": crime_lag_30
}])
prob = model1.predict_proba(row[FEATURES])[0][1]
response = {
"crime_probability": float(prob),
"risk": "UNSAFE" if prob > THRESHOLD else "SAFE"
}
if prob > THRESHOLD:
probs = model2.predict_proba(row[FEATURES])[0]
top = np.argsort(probs)[-3:][::-1]
response["top_crimes"] = [
{
"type": le.inverse_transform([i])[0],
"confidence": float(probs[i])
}
for i in top
]
return jsonify(response)
@app.route("/hotspots", methods=["GET"])
def hotspots():
# Load the cleaned dataset
dataset = pd.read_csv("dataset_cleaned.csv")
# Group by location and count occurrences
hotspot_data = dataset.groupby(['Latitude', 'Longitude']).size().reset_index(name='count')
# Filter hotspots based on a threshold (e.g., at least 5 occurrences)
hotspots = hotspot_data[hotspot_data['count'] >= 5]
return jsonify(hotspots.to_dict(orient="records"))
# ---------------- RUN ----------------
if __name__ == "__main__":
app.run(host="0.0.0.0", port=5000) |