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| import easyocr
import pathlib
import textwrap
import mysql.connector
from paddleocr import PaddleOCR, draw_ocr
import sys
import os
import google.generativeai as genai
import time
from config import import_env
from random import randint
import urllib.request
from PIL import Image
import shutil
import json
import_env()
from IPython.display import display
from IPython.display import Markdown
import redis
import os.path
# reader = easyocr.Reader(['en'], gpu=False)
ocr = PaddleOCR(use_angle_cls=True, lang="en", use_gpu=False )
genai.configure(api_key=os.environ.get('GOOGLE_API_KEY'))
def paddle_nlp_image(path):
dst = path
if os.path.exists(dst)==False:
src = os.environ.get('OSS_HOST') + dst
print("image src " + src)
try:
urllib.request.urlretrieve(src, dst)
except:
return False
print("processing the images " + dst)
result = ocr.ocr(dst, cls=True)
lists = []
for idx in range(len(result)):
res = result[idx]
try:
for line in res:
# print(line)
print(line[1][0])
lists.append(line[1][0])
except:
print("Error")
return []
return lists
def nlp_image(path):
dst = path
if os.path.exists(dst)==False:
src = os.environ.get('OSS_HOST') + dst
print("image src " + src)
try:
urllib.request.urlretrieve(src, dst)
except ValueError:
return False
# shutil.copyfile(src, dst)
result = reader.readtext(path, detail = 0)
print(result)
return result
def search_from_google_ai(path):
model = genai.GenerativeModel('gemini-pro-vision')
dst = path
if os.path.exists(dst)==False:
src = os.environ.get('OSS_HOST') + dst
print("image src " + src)
try:
urllib.request.urlretrieve(src, dst)
except:
return False
# shutil.copyfile(src, dst)
img = Image.open(dst)
try:
response = model.generate_content(img)
print(response.text)
return response.text
except ValueError:
#print(response.text)
# print(response.parts)
return False
print(response)
print(response.text)
print(response.parts)
return response.text
db = mysql.connector.connect(
host=os.environ.get('DB_HOST'),
user=os.environ.get('DB_USERNAME'),
password=os.environ.get('DB_PASSWORD'),
database=os.environ.get('DB_DATABASE')
)
r = redis.Redis(host=os.environ.get('REDIS_HOST'), port=os.environ.get('REDIS_PORT'), db=os.environ.get('REDIS_DB'), password=os.environ.get('REDIS_PASSWORD'), decode_responses=True, protocol=3)
cursor = db.cursor()
cursor.execute("SELECT * from sd_goods_gallery where img_desc is null order by img_id desc limit 10000")
items = cursor.fetchall()
for item in items:
print("start " + str(item[0]))
# result = nlp_image(item[5])
print("images " + item[5])
result = paddle_nlp_image(item[5])
cache_key = str(item[0])+"_gallery_ai_images"
ai_result = r.get(cache_key)
print(ai_result)
if(r.exists(cache_key)==False):
ai_result = search_from_google_ai(item[5])
if(ai_result!=False):
r.set(cache_key, ai_result)
y = json.dumps(result)
print(y)
print(ai_result)
if (y=='[]'):
y = []
y.append(ai_result)
y = json.dumps(y)
if(y!='[]'):
cursor.execute(u'''Update sd_goods_gallery set img_desc=%s where img_id=%s;''', (y, item[0]))
db.commit()
# this needs to run only once to load the model into memory
# nlp_image()
|
