build v8 structure

AI_Model/ArabicOCR.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dd3844fc58d898dec0f19ffc33ee09f19701c96c40e334c8ecca29aff9bc9a19
+size 87671593

README.md

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+HuggingFace: https://huggingface.co/rakib72642/Arabic_OCR
+
+sudo apt install iproute2 && sudo apt install wget && sudo apt install unzip && sudo apt install nvtop && sudo apt-get install git-lfs && sudo apt-get update && sudo apt-get install libgl1 && curl -s https://ngrok-agent.s3.amazonaws.com/ngrok.asc | sudo tee /etc/apt/trusted.gpg.d/ngrok.asc >/dev/null && echo "deb https://ngrok-agent.s3.amazonaws.com buster main" | sudo tee /etc/apt/sources.list.d/ngrok.list && sudo apt update && sudo apt install ngrok && ngrok config add-authtoken 2Qm8hS1zPhVXiLjEdlI4738tLzF_2QJwGJMK5oTbQD33QSVXS && sudo apt update && sudo apt upgrade && ngrok http --domain=hawkeyes.ngrok.app 8000
+
+
+git clone https://huggingface.co/rakib72642/Arabic_OCR && cd Arabic_OCR && pip install -r requirements.txt && sudo apt update && sudo apt upgrade && python ocr_api.py
+
+cd Arabic_OCR && python ocr_api.py
+
+hypercorn ocr_api:app --bind 127.0.0.1:8000 --workers 4
+
+
+OLD OCR : 
+# ************************************************************
+ngrok config add-authtoken 2Q8xOjna6gvwQRiMTZayN1uEgWy_6uRD8M1b6rZtYMz4yLzAw
+
+ngrok http --domain=dominant-eagerly-deer.ngrok-free.app 8000

data/data.py

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+
+
+name_distribute = {
+                    "anti-dandruff":"Anti-Dandruff",
+                    "clear":"Clear",
+                    "confidence":"Confidence",
+                    "revival":"Revival",
+                    }

data/model.py

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+from ultralytics import YOLO
+
+OCR_model = YOLO("AI_Model/ArabicOCR.pt").cuda()
+
+OCR_model.to(device=0)

ocr_api.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import asyncio
+from typing import List, Union
+from ocr_main import *
+import uvicorn
+import logging
+from datetime import datetime
+import pytz
+import torch
+import json
+
+logging.basicConfig(filename="ArabicOCR.log",
+                    filemode='w')
+logger = logging.getLogger("OCR")
+logger.setLevel(logging.DEBUG)
+file_handler = logging.FileHandler("ArabicOCR.log")
+logger.addHandler(file_handler)
+total_done = 0
+total_error = 0
+
+app = FastAPI()
+
+class Item(BaseModel):
+    url: str
+
+def get_bd_time():
+    bd_timezone = pytz.timezone("Asia/Dhaka")
+    time_now = datetime.now(bd_timezone)
+    current_time = time_now.strftime("%I:%M:%S %p")
+    return current_time
+
+
+async def process_item(item: Item):
+    try:
+        result = await mainDet(item.url)
+        result = json.loads(result)
+        return result
+    finally:
+        torch.cuda.empty_cache()
+        pass
+
+async def process_items(items: Union[Item, List[Item]]):
+    print(type(items))
+    if type(items)==list:
+        coroutines = [process_item(item) for item in items]
+        results = await asyncio.gather(*coroutines)
+        print("multi : ",results)
+    else:
+        results = await process_item(items)
+        print("single : ", results)
+    return results
+
+
+
+@app.get("/status")
+async def status():
+    return "AI Server in running"
+
+@app.post("/arabicOCR")
+async def create_items(items: Union[Item, List[Item]]):
+    try:
+        # global total_done
+        # total_done +=1
+        results = await process_items(items)
+        print("Result Sent to User:", results)
+        print("###################################################################################################")
+        print(items)
+        print("Last Execution Time : ", get_bd_time())
+        # logger.info(f"Time:{get_bd_time()}, Execution Done and Total Successfull Execution : {total_done}")
+        return results
+    except Exception as e:
+        global total_error
+        total_error += 1
+        logger.info(f"Time:{get_bd_time()}, Execution Failed and Total Failed Execution : {total_error}, Payload:{items}")
+        logger.error(str(e))
+        return {"AI": f"Error: {str(e)}"}
+    finally:
+        global total_done
+        total_done +=1
+        logger.info(f"Time:{get_bd_time()}, Execution Done and Total Successfull Execution : {total_done}, Payload:{items}")
+        torch.cuda.empty_cache()
+        pass
+
+if __name__ == "__main__":
+    try:
+        del OCR_model
+        uvicorn.run(app, host="127.0.0.1", port=4444)
+    finally:
+        torch.cuda.empty_cache()

ocr_main.py

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+import json
+import pandas as pd
+import asyncio
+import base64
+from PIL import Image, ImageDraw
+from aiohttp import ClientSession
+from io import BytesIO
+from data.model import OCR_model
+from data.data import name_distribute
+
+
+async def getImage(img_url):
+    async with ClientSession() as session:
+        try:
+            async with session.get(img_url) as response:
+                img_data = await response.read()
+                return BytesIO(img_data)
+        except Exception as e:
+            print({"Error in getImage":str(e)})
+
+
+
+
+async def detection(model,img_content):
+    try:
+        img = Image.open(img_content)
+        # result = model(img)
+        result = model(img,device=0)
+        
+        detection = {}
+        data = json.loads(result[0].tojson())
+        rect_data = []
+        rec_rect = []
+        for items in data:
+            rect_data.append(items["box"])
+        await create_rectangle(img_content,rect_data)
+        if len(data) == 0:
+            res = {"AI": "No Detection"}
+            detection.update(res)
+        else:
+            df = pd.DataFrame(data)
+            name_counts = df['name'].value_counts().sort_index()
+            
+            for name, count in name_counts.items():
+                res = {name: count}
+                detection.update(res)
+        return detection
+    except Exception as e:
+        print({"Error in detection":str(e)})
+
+
+async def share_iamge(img):
+    try:
+        buffer = BytesIO()
+        img.save(buffer, format="JPEG")
+        base64_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
+        return base64_image
+    except Exception as e:
+        print({"Error in share_iamge":str(e)})
+
+async def create_rectangle(image_data,cords):
+    try:
+        drawing = ImageDraw.Draw(image_data)
+
+        for item in cords:
+            x1 = item["x1"]
+            x2 = item["x2"]
+            y1 = item["y1"]
+            y2 = item["y2"]
+            drawing.rectangle([x1,y1,x2,y2],outline="red",width=5)
+        return drawing
+    except Exception as e:
+        print({"Error in create_rectangle":str(e)})
+
+
+
+
+
+
+async def format_result(res,conv):
+    try:
+        result = {}
+        for key,value in conv.items():
+            if key in res:
+                data = {value:res[key]}
+                result.update(data)
+        return result
+    except Exception as e:
+        print({"Error in format_result":str(e)})
+
+
+
+
+
+
+async def mainDet(url):
+    try:
+        image = await asyncio.create_task(getImage(url))
+        detect_data = await asyncio.create_task(detection(OCR_model, image))
+        tab_data = await asyncio.create_task(format_result(detect_data,name_distribute))
+        img_data = await asyncio.create_task(share_iamge(image))
+
+        result = {
+                    "tabularData":tab_data,
+                    "imageData":img_data,
+                    }
+        return json.dumps(result)
+
+    except Exception as e:
+        print({"Error in mainDet":str(e)})
+

requirements.txt

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+fastapi
+pydantic
+uvicorn 
+ultralytics
+aiohttp
+pytz
+regex
+hypercorn