def classifyZipcode(location_text):
classifier = recognition_heuristic.naivebayes(recognition_heuristic.getwords)
classifier.setdb('test_100.db')
classified_zipcode = classifier.classify(location_text)
zipcode = str(classified_zipcode[0])
confidence = str(classfied_zipcode[1])
#query MYSQL database and append to python list
cur = db.cursor()
cur.execute("SELECT latitude,longitude FROM sqlbook.zipcounty WHERE zipcode = %s", zipcode)
rows = cur.fetchall()
latitude = rows[0][0]
longitude = rows[0][1]
likely_zipcode = {"location_text": location_text, "zipcode": zipcode, "latitude": latitude, "longitude": longitude}
weather = get__weather_data.getWeather(likely_zipcode['zipcode'])
likely_zipcode.update(weather)
return likely_zipcode
location_text = 'Beverly Hills'
classified_zipcode = classifier.classify('Beverly Hills')
zipcode = str(classified_zipcode[0])
confidence = str(classfied_zipcode[1])
#query MYSQL database and append to python list
db = MySQLdb('')
cur = db.cursor()
cur.execute("SELECT latitude,longitude FROM sqlbook.zipcounty WHERE zipcode = %s", zipcode)
rows = cur.fetchall()
latitude = rows[0][0]
longitude = rows[0][1]
likely_zipcode = {"location_text": location_text, "zipcode": zipcode, "latitude": latitude, "longitude": longitude}
weather = get__weather_data.getWeather(likely_zipcode['zipcode'])
likely_zipcode.update(weather)
print likely_zipcode
#if not close enough
#select 10 close locations to the one classified.
cur.execute("""SELECT z.zipcode, z.state, zco.poname, distcirc, population, hh,
hhmedincome, z.latitude, z.longitude
FROM (SELECT zips.*,
ACOS(COS(comp.latrad)*COS(zips.latrad)*
COS(comp.longrad - zips.longrad) +
SIN(comp.latrad)*SIN(zips.latrad))*radius as distcirc
FROM (SELECT zc.*, latitude*PI()/180 as latrad,
longitude*PI()/180 as longrad, 3949.9 as radius