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824 lines (743 loc) · 34.5 KB
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from flask import Flask, render_template_string, request
import os
import json
import pandas as pd
import numpy as np
import joblib
import importlib.util
from types import ModuleType
app = Flask(__name__)
TEMPLATE = """
<!doctype html>
<html>
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>IP City Prediction - Comparison</title>
<style>
body { font-family: Arial, sans-serif; margin: 24px; }
.row { display: flex; gap: 24px; }
.card { flex: 1; border: 1px solid #ddd; border-radius: 8px; padding: 16px; }
.muted { color: #666; font-size: 12px; }
input[type=text] { width: 360px; padding: 8px; }
button { padding: 8px 16px; }
table { border-collapse: collapse; width: 100%; }
th, td { text-align: left; border-bottom: 1px solid #eee; padding: 6px 8px; }
.topk { font-family: monospace; }
.err { color: #b00020; }
.ok { color: #0b7; }
.hdr { margin-bottom: 12px; }
.subtitle { color: #333; font-weight: bold; }
.features { white-space: pre-wrap; font-family: monospace; font-size: 12px; color: #333; background: #fafafa; padding: 8px; border: 1px dashed #ddd; border-radius: 4px; }
</style>
</head>
<body>
<h2>IP City Prediction - LightGBM vs RBF Approx SGD vs KNN vs Decision Tree</h2>
<form method="post">
<label>IP address:</label>
<input type="text" name="ip" placeholder="e.g. 1.6.68.129" value="{{ ip or '' }}" />
<button type="submit">Predict</button>
</form>
<div class="muted hdr">Models load once on server start. Update accuracy in config if needed.</div>
{% if error %}
<div class="err">{{ error }}</div>
{% endif %}
{% if lgbm or rbf %}
<div class="row">
<div class="card">
<div class="subtitle">LightGBM</div>
{% if lgbm.error %}
<div class="err">{{ lgbm.error }}</div>
{% else %}
<div><b>Predicted City:</b> {{ lgbm.predicted_city }}</div>
<div><b>Confidence (Top-1):</b> {{ '%.3f'|format(lgbm.topk[0].prob if lgbm.topk and lgbm.topk|length>0 else 0.0) }}</div>
<div><b>Top-k:</b></div>
<div class="topk">
{% for item in lgbm.topk %}
{{ item.city }}: {{ '%.3f'|format(item.prob) }}<br/>
{% endfor %}
</div>
<div><b>Used Fallback:</b> {{ lgbm.used_fallback_no_exact_range }}</div>
<div><b>Accuracy (config):</b> {{ config.lgbm_accuracy }}</div>
{% if lgbm_distance_commercial is not none %}
<div><b>Distance to Commercial Ref:</b> {{ '%.1f'|format(lgbm_distance_commercial) }} km</div>
{% endif %}
{% if lgbm_distance_whois is not none %}
<div><b>Distance to WHOIS Ref:</b> {{ '%.1f'|format(lgbm_distance_whois) }} km</div>
{% endif %}
<div style="margin-top:8px;"><b>Features</b></div>
<div class="features">{{ lgbm.used_features|tojson(indent=2) }}</div>
{% endif %}
</div>
<div class="card">
<div class="subtitle">RBF Approx SGD</div>
{% if rbf_error %}
<div class="err">{{ rbf_error }}</div>
{% else %}
<div><b>Predicted City:</b> {{ rbf.predicted_city }}</div>
<div><b>Confidence (Top-1):</b> {{ '%.3f'|format(rbf.top1_prob if rbf.top1_prob is not none else 0.0) }}</div>
{% if rbf.topk %}
<div><b>Top-k:</b></div>
<div class="topk">
{% for city, prob in rbf.topk %}
{{ city }}: {{ '%.3f'|format(prob) }}<br/>
{% endfor %}
</div>
{% endif %}
<div><b>Accuracy (config):</b> {{ config.rbf_accuracy }}</div>
{% if rbf_distance_commercial is not none %}
<div><b>Distance to Commercial Ref:</b> {{ '%.1f'|format(rbf_distance_commercial) }} km</div>
{% endif %}
{% if rbf_distance_whois is not none %}
<div><b>Distance to WHOIS Ref:</b> {{ '%.1f'|format(rbf_distance_whois) }} km</div>
{% endif %}
<div style="margin-top:8px;"><b>Matched Record (subset)</b></div>
<div class="features">{{ rbf.matched_record|tojson(indent=2) }}</div>
{% endif %}
</div>
<div class="card">
<div class="subtitle">K-Nearest Neighbor (KNN) Algorithm</div>
{% if knn_error %}
<div class="err">{{ knn_error }}</div>
{% else %}
<div><b>Predicted City:</b> {{ knn.predicted_city }}</div>
<div><b>Confidence (Top-1):</b> {{ '%.3f'|format(knn.top1_prob if knn.top1_prob is not none else 0.0) }}</div>
{% if knn.topk %}
<div><b>Top-k:</b></div>
<div class="topk">
{% for city, prob in knn.topk %}
{{ city }}: {{ '%.3f'|format(prob) }}<br/>
{% endfor %}
</div>
{% endif %}
{% if knn.predicted_lat is not none %}
<div><b>Predicted Lat (KNN):</b> {{ '%.5f'|format(knn.predicted_lat) }}</div>
{% endif %}
{% if knn.predicted_reachable is not none %}
<div><b>Predicted Reachable (KNN):</b> {{ knn.predicted_reachable }}</div>
{% endif %}
{% if knn_distance_commercial is not none %}
<div><b>Distance to Commercial Ref:</b> {{ '%.1f'|format(knn_distance_commercial) }} km</div>
{% endif %}
{% if knn_distance_whois is not none %}
<div><b>Distance to WHOIS Ref:</b> {{ '%.1f'|format(knn_distance_whois) }} km</div>
{% endif %}
<div style="margin-top:8px;"><b>Used Features</b></div>
<div class="features">{{ knn.used_features|tojson(indent=2) }}</div>
{% endif %}
</div>
<div class="card">
<div class="subtitle">Decision Tree</div>
{% if dt_error %}
<div class="err">{{ dt_error }}</div>
{% else %}
<div><b>Predicted City:</b> {{ dt.predicted_city }}</div>
{% if dt.topk %}
<div><b>Top-k:</b></div>
<div class="topk">
{% for city, prob in dt.topk %}
{{ city }}: {{ '%.3f'|format(prob) }}<br/>
{% endfor %}
</div>
{% endif %}
{% if dt.predicted_lat is not none %}
<div><b>Predicted Lat (DT):</b> {{ '%.5f'|format(dt.predicted_lat) }}</div>
{% endif %}
{% if dt.predicted_lon is not none %}
<div><b>Predicted Lon (DT):</b> {{ '%.5f'|format(dt.predicted_lon) }}</div>
{% endif %}
{% if dt.predicted_reachable is not none %}
<div><b>Predicted Reachable (DT):</b> {{ dt.predicted_reachable }}</div>
{% endif %}
{% if dt_distance_commercial is not none %}
<div><b>Distance to Commercial Ref:</b> {{ '%.1f'|format(dt_distance_commercial) }} km</div>
{% endif %}
{% if dt_distance_whois is not none %}
<div><b>Distance to WHOIS Ref:</b> {{ '%.1f'|format(dt_distance_whois) }} km</div>
{% endif %}
<div style="margin-top:8px;"><b>Used Features</b></div>
<div class="features">{{ dt.used_features|tojson(indent=2) }}</div>
{% endif %}
</div>
</div>
{% endif %}
</body>
</html>
"""
# --- Configurable paths and (optional) accuracy display ---
MODEL_LGBM = os.environ.get("MODEL_LGBM", "3.city_lgbm.txt")
LABEL_LGBM = os.environ.get("LABEL_LGBM", "3.city_label_encoder.joblib")
PREPROC_LGBM = os.environ.get("PREPROC_LGBM", "3..joblib")
MODEL_RBF = os.environ.get("MODEL_RBF", "2.RBF_city_rbf_approx_sgd_pipeline.joblib")
CSV_LOOKUP = os.environ.get("CSV_LOOKUP", "new_indian_training_data.csv")
# KNN artifacts
KNN_CITY = os.environ.get("KNN_CITY", "4.knn_city.joblib")
KNN_LABEL = os.environ.get("KNN_LABEL", "4.label_encoder.joblib")
KNN_PREPROC = os.environ.get("KNN_PREPROC", "4.preproc.joblib")
KNN_LAT = os.environ.get("KNN_LAT", "4.knn_lat.joblib")
KNN_REACH = os.environ.get("KNN_REACH", "4.knn_reach.joblib")
# Decision Tree artifacts
DT_CITY = os.environ.get("DT_CITY", "5.dt_city.joblib")
DT_LAT = os.environ.get("DT_LAT", "5.dt_lat.joblib")
DT_LON = os.environ.get("DT_LON", "5.dt_lon.joblib")
DT_REACH= os.environ.get("DT_REACH","5.dt_reach.joblib")
DT_LABEL= os.environ.get("DT_LABEL","5.label_encoder_city.joblib")
DT_PRE = os.environ.get("DT_PRE", "5.preproc_objects.joblib")
CONFIG = {
"lgbm_accuracy": os.environ.get("LGBM_ACCURACY", "N/A"),
"rbf_accuracy": os.environ.get("RBF_ACCURACY", "N/A"),
}
# --- Utility: make objects JSON-serializable (convert numpy/pandas types) ---
def _to_serializable(obj):
import numpy as _np
import pandas as _pd
if obj is None:
return None
# numpy scalars
if isinstance(obj, (_np.integer,)):
return int(obj)
if isinstance(obj, (_np.floating,)):
return float(obj)
if isinstance(obj, (_np.bool_,)):
return bool(obj)
# pandas scalars
if isinstance(obj, (_pd.Timestamp,)):
return obj.isoformat()
# sequences
if isinstance(obj, (_np.ndarray,)):
return obj.tolist()
if isinstance(obj, (list, tuple)):
return [ _to_serializable(x) for x in obj ]
# mappings
if isinstance(obj, dict):
return { str(k): _to_serializable(v) for k, v in obj.items() }
# pandas Series/DataFrame
if isinstance(obj, _pd.Series):
return { str(k): _to_serializable(v) for k, v in obj.items() }
if isinstance(obj, _pd.DataFrame):
return [ _to_serializable(rec) for rec in obj.to_dict(orient="records") ]
# fallback
return obj
# --- Dynamically load LightGBM inference from file (filename starts with digit) ---
def _load_lgbm_predict_func():
base_dir = os.path.dirname(os.path.abspath(__file__))
script_path = os.path.join(base_dir, "3.run_lgbm_ip_inference.py")
if not os.path.exists(script_path):
return None
spec = importlib.util.spec_from_file_location("lgbm_infer", script_path)
if spec is None or spec.loader is None:
return None
module = importlib.util.module_from_spec(spec) # type: ModuleType
try:
spec.loader.exec_module(module) # type: ignore[attr-defined]
except Exception:
return None
return getattr(module, "predict_for_ip", None)
lgbm_predict = _load_lgbm_predict_func()
# --- Load RBF pipeline and lookup once ---
try:
rbf_pipeline = joblib.load(MODEL_RBF) if os.path.exists(MODEL_RBF) else None
except Exception:
rbf_pipeline = None
def load_lookup_df(csv_path: str) -> pd.DataFrame:
try:
df = pd.read_csv(csv_path, low_memory=False)
df.replace({"-": np.nan, "no_rdns": np.nan, "": np.nan}, inplace=True)
for col in ("ip_from", "ip_to", "ip_numeric"):
if col in df.columns:
df[col] = pd.to_numeric(df[col], errors="coerce")
if (("ip_from" not in df.columns or df["ip_from"].isna().all()) and
("ip_start" in df.columns and "ip_end" in df.columns)):
import ipaddress
def conv(s):
try:
return int(ipaddress.ip_address(str(s)))
except Exception:
return np.nan
df["ip_from"] = df["ip_start"].apply(conv)
df["ip_to"] = df["ip_end"].apply(conv)
if {"ip_from", "ip_to"}.issubset(df.columns):
df = df.dropna(subset=["ip_from", "ip_to"]).reset_index(drop=True)
return df
except Exception:
return pd.DataFrame()
LOOKUP_DF = load_lookup_df(CSV_LOOKUP)
# Precompute simple state centroids from lookup as a WHOIS proxy
STATE_CENTROIDS = {}
try:
if not LOOKUP_DF.empty and {"state","lat","lon"}.issubset(LOOKUP_DF.columns):
grp = LOOKUP_DF.dropna(subset=["lat","lon"]).groupby("state")
cent = grp[["lat","lon"]].mean().reset_index()
STATE_CENTROIDS = { str(r["state"]): (float(r["lat"]), float(r["lon"])) for _, r in cent.iterrows() }
except Exception:
STATE_CENTROIDS = {}
def rbf_predict_from_ip(ip_str: str, topk: int = 5):
if rbf_pipeline is None or LOOKUP_DF.empty:
return None, "RBF pipeline or lookup not available"
# Find matching record
import ipaddress
try:
ipn = int(ipaddress.ip_address(ip_str))
except Exception:
return None, "Invalid IP"
mask = (LOOKUP_DF["ip_from"] <= ipn) & (LOOKUP_DF["ip_to"] >= ipn)
matches = LOOKUP_DF.loc[mask]
if matches.empty:
return None, "No matching IP range"
rec = matches.assign(range_size=(matches["ip_to"] - matches["ip_from"]))\
.sort_values("range_size").iloc[0]
# Infer feature columns from the pipeline if possible
feature_cols = None
try:
if hasattr(rbf_pipeline, "named_steps"):
from sklearn.compose import ColumnTransformer
for name, step in rbf_pipeline.named_steps.items():
if isinstance(step, ColumnTransformer):
cols = []
for t in step.transformers_:
if len(t) >= 3:
cs = t[2]
if isinstance(cs, (list, tuple)):
cols.extend(list(cs))
else:
cols.append(cs)
feature_cols = [c for c in cols if isinstance(c, str)]
break
except Exception:
feature_cols = None
if feature_cols is None:
feature_cols = ["state", "asn_description", "rdns_hostname", "lat", "lon", "asn", "reachable_flag", "is_rtt_missing"]
# Build a feature row
row = {}
for c in feature_cols:
if c in rec.index:
val = rec[c]
if c in ("lat", "lon", "asn", "reachable_flag", "is_rtt_missing", "ip_numeric", "ip_from", "ip_to"):
try:
row[c] = float(val) if (val is not None and not pd.isna(val)) else np.nan
except Exception:
row[c] = np.nan
else:
row[c] = "__MISSING__" if pd.isna(val) else str(val)
else:
row[c] = np.nan if c in ("lat", "lon", "asn", "reachable_flag", "is_rtt_missing") else "__MISSING__"
if "is_rtt_missing" in feature_cols and pd.isna(row.get("is_rtt_missing")):
row["is_rtt_missing"] = 1 if pd.isna(rec.get("local_rtt_ms")) else 0
X = pd.DataFrame([row], columns=feature_cols)
pred_city = rbf_pipeline.predict(X)[0]
topk_list = None
top1_prob = None
if hasattr(rbf_pipeline, "predict_proba"):
try:
probs = rbf_pipeline.predict_proba(X)[0]
classes = getattr(rbf_pipeline, "classes_", None)
if classes is not None:
idx = np.argsort(probs)[::-1][:topk]
topk_list = [(classes[i], float(probs[i])) for i in idx]
top1_prob = float(probs[idx[0]]) if len(idx) else None
except Exception:
pass
matched_rec_subset = {}
for k in ("ip_start", "ip_end", "ip_from", "ip_to", "country", "state", "city", "asn", "asn_description", "rdns_hostname", "local_rtt_ms", "reachable"):
if k in rec.index:
matched_rec_subset[k] = rec[k]
return {
"ip": ip_str,
"predicted_city": pred_city,
"topk": topk_list,
"top1_prob": top1_prob,
"matched_record": matched_rec_subset,
}, None
def _build_base_features_for_ip_record(rec: pd.Series, ip_str: str) -> pd.DataFrame:
# Use the same scheme as LightGBM features
TEXT_RDNS = "rdns_hostname"
TEXT_ASND = "asn_description"
NUMERIC = ["ip_mid", "asn", "lat", "lon", "local_rtt_ms", "is_rtt_missing", "reachable_flag"]
CATEGORICAL = ["state"]
if rec is None:
# fallback minimal features using ip_mid only
try:
import ipaddress as _ip
ipn = int(_ip.ip_address(ip_str))
except Exception:
return pd.DataFrame()
feat = {}
for c in NUMERIC + CATEGORICAL + [TEXT_RDNS, TEXT_ASND]:
if c in NUMERIC:
feat[c] = float(ipn) if c == "ip_mid" else np.nan
elif c in CATEGORICAL:
feat[c] = "__MISSING__"
else:
feat[c] = ""
return pd.DataFrame([feat])[NUMERIC + CATEGORICAL + [TEXT_RDNS, TEXT_ASND]]
# construct from record
ip_from = rec.get("ip_from") if "ip_from" in rec else np.nan
ip_to = rec.get("ip_to") if "ip_to" in rec else np.nan
ip_numeric = rec.get("ip_numeric") if "ip_numeric" in rec else np.nan
if pd.isna(ip_from) and not pd.isna(ip_numeric):
ip_from = ip_numeric
if pd.isna(ip_to) and not pd.isna(ip_numeric):
ip_to = ip_numeric
feat = {
"ip_mid": float((pd.to_numeric(ip_from, errors="coerce") + pd.to_numeric(ip_to, errors="coerce")) / 2.0) if not (pd.isna(ip_from) and pd.isna(ip_to)) else np.nan,
"asn": float(rec.get("asn")) if not pd.isna(rec.get("asn")) else np.nan,
"lat": float(rec.get("lat")) if not pd.isna(rec.get("lat")) else np.nan,
"lon": float(rec.get("lon")) if not pd.isna(rec.get("lon")) else np.nan,
"local_rtt_ms": float(rec.get("local_rtt_ms")) if not pd.isna(rec.get("local_rtt_ms")) else np.nan,
"is_rtt_missing": 1 if pd.isna(rec.get("local_rtt_ms")) else 0,
"reachable_flag": 1 if str(rec.get("reachable", "")).upper() == "TRUE" else 0,
"state": rec.get("state", "__MISSING__") if not pd.isna(rec.get("state")) else "__MISSING__",
"rdns_hostname": rec.get("rdns_hostname", "") if not pd.isna(rec.get("rdns_hostname")) else "",
"asn_description": rec.get("asn_description", "") if not pd.isna(rec.get("asn_description")) else "",
}
cols_order = ["ip_mid", "asn", "lat", "lon", "local_rtt_ms", "is_rtt_missing", "reachable_flag", "state", "rdns_hostname", "asn_description"]
return pd.DataFrame([feat])[cols_order]
def _transform_with_preproc(preproc_obj, Xrow: pd.DataFrame):
# If this is a full sklearn Pipeline/Transformer, use it directly
if hasattr(preproc_obj, "transform"):
try:
return preproc_obj.transform(Xrow)
except Exception:
pass
# Else expect dict parts like the LightGBM preproc
try:
# Read dynamic column lists from preproc dict
text_cols = preproc_obj.get("text_cols", ["rdns_hostname","asn_description"])
numeric_cols = preproc_obj.get("numeric_cols", ["ip_mid","asn","local_rtt_ms","is_rtt_missing","reachable_flag"])
categorical_cols = preproc_obj.get("categorical_cols", ["state"])
tf_rdns = preproc_obj["tfidf_rdns"].transform(Xrow[text_cols[0]])
tf_asnd = preproc_obj["tfidf_asnd"].transform(Xrow[text_cols[1]])
Xnum = preproc_obj["num_imputer"].transform(Xrow[numeric_cols])
Xnum = preproc_obj["scaler"].transform(Xnum)
# ohe_state is key used by KNN training artifacts
ohe = preproc_obj.get("ohe_state") or preproc_obj.get("onehot")
Xcat = ohe.transform(Xrow[categorical_cols]) if ohe is not None else None
from scipy.sparse import hstack, csr_matrix
parts = [csr_matrix(tf_rdns), csr_matrix(tf_asnd), csr_matrix(Xnum)]
if Xcat is not None:
parts.append(csr_matrix(Xcat))
return hstack(parts).tocsr()
except Exception:
return None
def knn_predict_from_ip(ip_str: str, topk: int = 5):
# Ensure artifacts exist
if not (os.path.exists(KNN_CITY) and os.path.exists(KNN_LABEL) and os.path.exists(KNN_PREPROC)):
return None, "KNN artifacts not available"
# Load artifacts lazily
try:
knn_city = joblib.load(KNN_CITY)
knn_label = joblib.load(KNN_LABEL)
knn_pre = joblib.load(KNN_PREPROC)
knn_lat = joblib.load(KNN_LAT) if os.path.exists(KNN_LAT) else None
knn_reach = joblib.load(KNN_REACH) if os.path.exists(KNN_REACH) else None
except Exception as e:
return None, f"Failed to load KNN artifacts: {e}"
# Lookup matching record
import ipaddress
try:
ipn = int(ipaddress.ip_address(ip_str))
except Exception:
return None, "Invalid IP"
rec = None
if not LOOKUP_DF.empty and {"ip_from","ip_to"}.issubset(LOOKUP_DF.columns):
mask = (LOOKUP_DF["ip_from"] <= ipn) & (LOOKUP_DF["ip_to"] >= ipn)
matches = LOOKUP_DF.loc[mask]
if not matches.empty:
rec = matches.assign(range_size=(matches["ip_to"] - matches["ip_from"]))\
.sort_values("range_size").iloc[0]
Xrow = _build_base_features_for_ip_record(rec, ip_str)
if Xrow.empty:
return None, "Failed to build features for IP"
Xfull = _transform_with_preproc(knn_pre, Xrow)
if Xfull is None:
return None, "KNN preprocessor could not transform features"
# City prediction
try:
pred_idx = int(knn_city.predict(Xfull)[0])
pred_city = knn_label.inverse_transform([pred_idx])[0] if hasattr(knn_label, "inverse_transform") else str(pred_idx)
except Exception as e:
return None, f"KNN city prediction failed: {e}"
# Probabilities/topk if available
topk_list = None
top1_prob = None
if hasattr(knn_city, "predict_proba"):
try:
probs = knn_city.predict_proba(Xfull)[0]
classes = getattr(knn_city, "classes_", None)
if classes is not None:
# Map class indices back to label encoder indices if needed
idx_sorted = np.argsort(probs)[::-1][:topk]
# Convert class labels (which should be encoded ints) to city names via label encoder
cities = knn_label.inverse_transform(classes[idx_sorted].astype(int)) if hasattr(knn_label, "inverse_transform") else classes[idx_sorted]
topk_list = [(str(cities[i]), float(probs[idx_sorted][i])) for i in range(len(idx_sorted))]
top1_prob = float(probs[idx_sorted][0]) if len(idx_sorted) else None
except Exception:
pass
# Optional regress/classify aux targets
pred_lat = None
if knn_lat is not None:
try:
pred_lat = float(knn_lat.predict(Xfull)[0])
except Exception:
pred_lat = None
pred_reach = None
if knn_reach is not None:
try:
pred_reach = int(knn_reach.predict(Xfull)[0])
except Exception:
pred_reach = None
used_features = Xrow.iloc[0].to_dict()
used_features = _to_serializable(used_features)
return {
"ip": ip_str,
"predicted_city": pred_city,
"topk": topk_list,
"top1_prob": top1_prob,
"predicted_lat": pred_lat,
"predicted_reachable": pred_reach,
"used_features": used_features,
}, None
def dt_predict_from_ip(ip_str: str, topk: int = 5):
# Ensure artifacts exist
needed = [DT_CITY, DT_LAT, DT_LON, DT_REACH, DT_LABEL, DT_PRE]
if not all(os.path.exists(p) for p in needed):
return None, "Decision Tree artifacts not available"
# Load artifacts lazily
try:
dt_city = joblib.load(DT_CITY)
dt_lat = joblib.load(DT_LAT)
dt_lon = joblib.load(DT_LON)
dt_reach= joblib.load(DT_REACH)
dt_label= joblib.load(DT_LABEL)
dt_pre = joblib.load(DT_PRE)
except Exception as e:
return None, f"Failed to load Decision Tree artifacts: {e}"
# Lookup matching record
import ipaddress
try:
ipn = int(ipaddress.ip_address(ip_str))
except Exception:
return None, "Invalid IP"
rec = None
if not LOOKUP_DF.empty and {"ip_from","ip_to"}.issubset(LOOKUP_DF.columns):
mask = (LOOKUP_DF["ip_from"] <= ipn) & (LOOKUP_DF["ip_to"] >= ipn)
matches = LOOKUP_DF.loc[mask]
if not matches.empty:
rec = matches.assign(range_size=(matches["ip_to"] - matches["ip_from"]))\
.sort_values("range_size").iloc[0]
# Build row with dt_pre column lists
feat = {}
# text
for t in dt_pre.get("text_cols", ["rdns_hostname","asn_description"]):
v = rec.get(t, "") if (rec is not None and t in rec.index) else ""
feat[t] = v if not pd.isna(v) else ""
# numeric
for n in dt_pre.get("numeric_cols", ["ip_mid","asn","lat","lon","local_rtt_ms","is_rtt_missing","reachable_flag"]):
if rec is not None and n in rec.index:
try:
feat[n] = float(rec[n]) if not pd.isna(rec[n]) else np.nan
except Exception:
feat[n] = np.nan
else:
feat[n] = np.nan
# categorical
for c in dt_pre.get("categorical_cols", ["state"]):
v = rec.get(c, "__MISSING__") if (rec is not None and c in rec.index) else "__MISSING__"
feat[c] = v if not pd.isna(v) else "__MISSING__"
# ensure ip_mid
if pd.isna(feat.get("ip_mid", None)):
ipf = rec.get("ip_from", np.nan) if (rec is not None and "ip_from" in rec.index) else np.nan
ipt = rec.get("ip_to", np.nan) if (rec is not None and "ip_to" in rec.index) else np.nan
ipn2 = rec.get("ip_numeric", np.nan) if (rec is not None and "ip_numeric" in rec.index) else np.nan
ipf_val = ipf if not pd.isna(ipf) else ipn2
ipt_val = ipt if not pd.isna(ipt) else ipn2
if not pd.isna(ipf_val) or not pd.isna(ipt_val):
try:
feat["ip_mid"] = float(((ipf_val if not pd.isna(ipf_val) else ipn2) + (ipt_val if not pd.isna(ipt_val) else ipn2)) / 2.0)
except Exception:
feat["ip_mid"] = np.nan
cols = dt_pre.get("text_cols", []) + dt_pre.get("numeric_cols", []) + dt_pre.get("categorical_cols", [])
row_df = pd.DataFrame([feat])[cols]
# Transform to model input (dense)
try:
tf_rdns = dt_pre["tfidf_rdns"].transform(row_df[dt_pre["text_cols"][0]])
tf_asnd = dt_pre["tfidf_asnd"].transform(row_df[dt_pre["text_cols"][1]])
num_arr = dt_pre["num_imputer"].transform(row_df[dt_pre["numeric_cols"]])
ohe = dt_pre.get("ohe_state")
cat_arr = ohe.transform(row_df[dt_pre["categorical_cols"]]) if ohe is not None else None
from scipy.sparse import hstack, csr_matrix
parts = [csr_matrix(tf_rdns), csr_matrix(tf_asnd), csr_matrix(num_arr)]
if cat_arr is not None:
parts.append(csr_matrix(cat_arr))
Xstack = hstack(parts).tocsr()
Xin = Xstack.toarray()
except Exception as e:
return None, f"DT preprocessing failed: {e}"
# Predictions
try:
city_idx = int(dt_city.predict(Xin)[0])
city_name = dt_label.inverse_transform([city_idx])[0] if hasattr(dt_label, "inverse_transform") else str(city_idx)
except Exception as e:
return None, f"DT city prediction failed: {e}"
topk_list = None
try:
probs = dt_city.predict_proba(Xin)[0]
idx = np.argsort(probs)[::-1][:topk]
topk_list = [(dt_label.inverse_transform([int(i)])[0], float(probs[i])) for i in idx]
except Exception:
pass
pred_lat = None
pred_lon = None
pred_reach = None
try:
pred_lat = float(dt_lat.predict(Xin)[0])
except Exception:
pass
try:
pred_lon = float(dt_lon.predict(Xin)[0])
except Exception:
pass
try:
pred_reach = bool(int(dt_reach.predict(Xin)[0]))
except Exception:
pass
used_features = row_df.iloc[0].to_dict()
used_features = _to_serializable(used_features)
return {
"ip": ip_str,
"predicted_city": city_name,
"topk": topk_list,
"predicted_lat": pred_lat,
"predicted_lon": pred_lon,
"predicted_reachable": pred_reach,
"used_features": used_features,
}, None
@app.route("/", methods=["GET", "POST"])
def index():
ip = None
error = None
lgbm = None
rbf = None
rbf_error = None
knn = None
knn_error = None
dt = None
dt_error = None
# per-model distances
lgbm_distance_commercial = None
lgbm_distance_whois = None
rbf_distance_commercial = None
rbf_distance_whois = None
knn_distance_commercial = None
knn_distance_whois = None
dt_distance_commercial = None
dt_distance_whois = None
if request.method == "POST":
ip = (request.form.get("ip") or "").strip()
if not ip:
error = "Please enter an IP address"
else:
if lgbm_predict is None:
lgbm = {"error": "LightGBM inference script not available"}
else:
try:
lgbm = lgbm_predict(
ip=ip,
model_path=MODEL_LGBM,
label_encoder_path=LABEL_LGBM,
preproc_path=PREPROC_LGBM,
csv_lookup_path=CSV_LOOKUP,
topk=5,
)
except Exception as e:
lgbm = {"error": str(e)}
rbf, rbf_error = rbf_predict_from_ip(ip, topk=5)
knn, knn_error = knn_predict_from_ip(ip, topk=5)
dt, dt_error = dt_predict_from_ip(ip, topk=5)
# Sanitize objects for Jinja tojson (convert numpy/pandas types)
if isinstance(lgbm, dict):
lgbm = _to_serializable(lgbm)
if isinstance(rbf, dict):
rbf = _to_serializable(rbf)
if isinstance(knn, dict):
knn = _to_serializable(knn)
if isinstance(dt, dict):
dt = _to_serializable(dt)
# Helper: haversine in KM
def _haversine_km(lat1, lon1, lat2, lon2):
from math import radians, sin, cos, asin, sqrt
try:
lat1, lon1, lat2, lon2 = float(lat1), float(lon1), float(lat2), float(lon2)
except Exception:
return None
R = 6371.0
dlat = radians(lat2 - lat1)
dlon = radians(lon2 - lon1)
a = sin(dlat/2)**2 + cos(radians(lat1)) * cos(radians(lat2)) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
return R * c
# Find reference record for this IP (commercial proxy)
ref_rec = None
try:
import ipaddress as _ip
ipn = int(_ip.ip_address(ip))
if not LOOKUP_DF.empty and {"ip_from","ip_to"}.issubset(LOOKUP_DF.columns):
m = (LOOKUP_DF["ip_from"] <= ipn) & (LOOKUP_DF["ip_to"] >= ipn)
hits = LOOKUP_DF.loc[m]
if not hits.empty:
ref_rec = hits.assign(range_size=(hits["ip_to"] - hits["ip_from"]))\
.sort_values("range_size").iloc[0]
except Exception:
ref_rec = None
# Prepare references
ref_lat = float(ref_rec.get("lat")) if (ref_rec is not None and not pd.isna(ref_rec.get("lat"))) else None
ref_lon = float(ref_rec.get("lon")) if (ref_rec is not None and not pd.isna(ref_rec.get("lon"))) else None
whois_lat = None
whois_lon = None
st = None
if ref_rec is not None and not pd.isna(ref_rec.get("state")):
st = str(ref_rec.get("state"))
elif isinstance(rbf, dict) and rbf.get("matched_record") and rbf["matched_record"].get("state"):
st = str(rbf["matched_record"]["state"])
if st and st in STATE_CENTROIDS:
whois_lat, whois_lon = STATE_CENTROIDS[st]
# Estimate model lat/lon where possible
# KNN has explicit lat
knn_lat = knn.get("predicted_lat") if isinstance(knn, dict) else None
knn_lon = knn.get("predicted_lon") if isinstance(knn, dict) else None
# Decision Tree has explicit lat/lon
dt_lat = dt.get("predicted_lat") if isinstance(dt, dict) else None
dt_lon = dt.get("predicted_lon") if isinstance(dt, dict) else None
# LightGBM/RBF: no lat predictor; approximate with ref_rec lat/lon if matched
# This still gives distance 0 to commercial reference; for WHOIS, we use centroid distance
lgbm_lat = ref_lat if isinstance(lgbm, dict) else None
lgbm_lon = ref_lon if isinstance(lgbm, dict) else None
rbf_lat = ref_lat if isinstance(rbf, dict) else None
rbf_lon = ref_lon if isinstance(rbf, dict) else None
# Compute distances
if knn_lat is not None and ref_lat is not None and (knn_lon is not None or ref_lon is not None):
use_lon = knn_lon if knn_lon is not None else ref_lon
knn_distance_commercial = _haversine_km(knn_lat, use_lon, ref_lat, ref_lon)
if knn_lat is not None and whois_lat is not None and (knn_lon is not None or whois_lon is not None):
use_lon = knn_lon if knn_lon is not None else whois_lon
knn_distance_whois = _haversine_km(knn_lat, use_lon, whois_lat, whois_lon)
if lgbm_lat is not None and ref_lat is not None and lgbm_lon is not None and ref_lon is not None:
lgbm_distance_commercial = _haversine_km(lgbm_lat, lgbm_lon, ref_lat, ref_lon)
if lgbm_lat is not None and whois_lat is not None and lgbm_lon is not None and whois_lon is not None:
lgbm_distance_whois = _haversine_km(lgbm_lat, lgbm_lon, whois_lat, whois_lon)
if rbf_lat is not None and ref_lat is not None and rbf_lon is not None and ref_lon is not None:
rbf_distance_commercial = _haversine_km(rbf_lat, rbf_lon, ref_lat, ref_lon)
if rbf_lat is not None and whois_lat is not None and rbf_lon is not None and whois_lon is not None:
rbf_distance_whois = _haversine_km(rbf_lat, rbf_lon, whois_lat, whois_lon)
# DT distances
dt_distance_commercial = None
dt_distance_whois = None
if dt_lat is not None and ref_lat is not None and (dt_lon is not None or ref_lon is not None):
use_lon = dt_lon if dt_lon is not None else ref_lon
dt_distance_commercial = _haversine_km(dt_lat, use_lon, ref_lat, ref_lon)
if dt_lat is not None and whois_lat is not None and (dt_lon is not None or whois_lon is not None):
use_lon = dt_lon if dt_lon is not None else whois_lon
dt_distance_whois = _haversine_km(dt_lat, use_lon, whois_lat, whois_lon)
return render_template_string(TEMPLATE, ip=ip, lgbm=lgbm, rbf=rbf, rbf_error=rbf_error, knn=knn, knn_error=knn_error, dt=dt, dt_error=dt_error, lgbm_distance_commercial=lgbm_distance_commercial, lgbm_distance_whois=lgbm_distance_whois, rbf_distance_commercial=rbf_distance_commercial, rbf_distance_whois=rbf_distance_whois, knn_distance_commercial=knn_distance_commercial, knn_distance_whois=knn_distance_whois, dt_distance_commercial=dt_distance_commercial, dt_distance_whois=dt_distance_whois, error=error, config=CONFIG)
if __name__ == "__main__":
app.run(host="0.0.0.0", port=int(os.environ.get("PORT", "5000")), debug=True)