File size: 3,078 Bytes
98689fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | #ml_service.py
import os
import certifi
import faiss
import numpy as np
import pandas as pd
import sqlite3
from fastapi import FastAPI
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer
import uvicorn
import re, string
# Set SSL certificate path
os.environ['REQUESTS_CA_BUNDLE'] = certifi.where()
os.environ['SSL_CERT_FILE'] = certifi.where()
DB_FILE = "pincodes.db"
MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
FAISS_FILE = "pincode_faiss.index"
BATCH_SIZE = 2048
app = FastAPI(title="AI Delivery Mapper - ML Microservice", version="2.0")
def normalize_text(s: str) -> str:
"""Cleans and normalizes address text."""
s = s.lower()
s = re.sub(r"[\W_]+", " ", s) # remove punctuation
s = re.sub(r"\s+", " ", s)
s = s.strip()
return s
print(" Loading dataset and model...")
conn = sqlite3.connect(DB_FILE)
df = pd.read_sql("SELECT officename, district, state, pincode FROM pincodes", conn)
conn.close()
df["text"] = df[["officename", "district", "state", "pincode"]].astype(str).agg(" ".join, axis=1)
df["text_norm"] = df["text"].apply(normalize_text)
model = SentenceTransformer(MODEL_NAME)
def build_index():
print(f" Building FAISS index for {len(df)} records...")
embeddings = model.encode(df["text_norm"].tolist(), batch_size=64, show_progress_bar=True, convert_to_numpy=True)
# Normalize for cosine similarity
faiss.normalize_L2(embeddings)
dim = embeddings.shape[1]
index = faiss.IndexFlatIP(dim) # inner product = cosine similarity
index.add(embeddings)
faiss.write_index(index, FAISS_FILE)
np.save(FAISS_FILE + ".meta.npy", df[["officename", "district", "state", "pincode"]].to_numpy())
print(f" FAISS index built and saved to {FAISS_FILE}")
return index
try:
index = faiss.read_index(FAISS_FILE)
meta = np.load(FAISS_FILE + ".meta.npy", allow_pickle=True)
print(" Loaded existing FAISS index.")
except:
index = build_index()
meta = np.load(FAISS_FILE + ".meta.npy", allow_pickle=True)
class MatchRequest(BaseModel):
text: str
top_k: int = 5
@app.post("/match")
def match_address(req: MatchRequest):
query = normalize_text(req.text)
query_vec = model.encode([query], convert_to_numpy=True)
faiss.normalize_L2(query_vec)
D, I = index.search(query_vec, req.top_k)
results = []
for idx, score in zip(I[0], D[0]):
if idx == -1:
continue
office, district, state, pin = meta[idx]
results.append({
"officename": str(office),
"district": str(district),
"state": str(state),
"pincode": str(pin),
"confidence": round(float(score), 4)
})
return {"query": req.text, "normalized": query, "matches": results}
@app.get("/")
def root():
return {"status": "ok", "records": len(df)}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8002)
#curl -X POST "http://127.0.0.1:8002/match" \
# -H "Content-Type: application/json" \
# -d '{"text": "koramangala bangalore 560034"}'
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