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app.py

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+from flask import Flask, jsonify, request
+import joblib
+import pandas as pd
+import numpy as np
+import requests
+import os
+
+app = Flask(__name__)
+
+model        = joblib.load("safety_model.pkl")
+le           = joblib.load("label_encoder.pkl")
+grid         = pd.read_csv("grid_lookup.csv")
+FEATURE_COLS = open("feature_cols.txt").read().strip().split("\n")
+
+FAST2SMS_KEY   = "YOUR_FAST2SMS_API_KEY"
+GUARDIAN_PHONE = "91XXXXXXXXXX"
+
+def send_sms(lat, lon, risk_score):
+    message = (
+        f"SAFETY ALERT! Unsafe location detected. "
+        f"Location: https://maps.google.com/?q={lat},{lon} "
+        f"Risk Score: {risk_score:.2f}. Please check immediately!"
+    )
+    url     = "https://www.fast2sms.com/dev/bulkV2"
+    payload = {
+        "route"    : "v3",
+        "sender_id": "TXTIND",
+        "message"  : message,
+        "language" : "english",
+        "flash"    : 0,
+        "numbers"  : GUARDIAN_PHONE
+    }
+    headers = {"authorization": FAST2SMS_KEY, "Content-Type": "application/json"}
+    try:
+        resp = requests.post(url, json=payload, headers=headers, timeout=10)
+        return resp.json()
+    except Exception as e:
+        return {"error": str(e)}
+
+@app.route("/predict", methods=["GET"])
+def predict():
+    try:
+        lat = float(request.args.get("lat"))
+        lon = float(request.args.get("lon"))
+    except (TypeError, ValueError):
+        return jsonify({"error": "Missing or invalid lat/lon"}), 400
+
+    distances   = np.sqrt((grid["latitude"] - lat)**2 + (grid["longitude"] - lon)**2)
+    nearest_row = grid.iloc[distances.idxmin()]
+    features    = pd.DataFrame([nearest_row[FEATURE_COLS].values], columns=FEATURE_COLS)
+
+    prediction  = int(model.predict(features)[0])
+    risk_score  = round(float(model.predict_proba(features)[0][1]), 4)
+
+    sms_sent = False
+    if prediction == 1:
+        send_sms(lat, lon, risk_score)
+        sms_sent = True
+
+    return jsonify({
+        "latitude"  : lat,
+        "longitude" : lon,
+        "label"     : "UNSAFE" if prediction == 1 else "SAFE",
+        "risk_score": risk_score,
+        "sms_sent"  : sms_sent,
+        "area_id"   : nearest_row["area_id"]
+    })
+
+@app.route("/", methods=["GET"])
+def home():
+    return jsonify({"status": "Women Safety API is running", "usage": "/predict?lat=13.05&lon=80.25"})
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 5000))
+    app.run(host="0.0.0.0", port=port, debug=False)

feature_cols.txt

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+latitude
+longitude
+total_incidents
+incidents_5yr
+night_incidents
+sexual_offences
+harassment_count
+assault_count
+avg_severity
+repeat_offender_cases
+street_lighting
+cctv_density
+proximity_transit
+population_density
+slum_density
+nightlife_density
+incident_density
+weighted_risk_score
+trend_12month
+risk_index
+area_type_enc

label_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b97bc2f6c50067e6fde16dbb96fbe5c918a36e25dd1d95da477b3c9d33db7354
+size 525

requirements.txt

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+flask
+scikit-learn
+pandas
+numpy
+joblib
+requests
+gunicorn

runtime.txt

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+python-3.11.0

safety_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c7177c4d5a005cc4efe0255c5b87ea701ea6cd81182887541759365f1288d911
+size 196809