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optimised-ocr

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# from fastapi import FastAPI, File, UploadFile, HTTPException
# from fastapi.middleware.cors import CORSMiddleware
# from typing import Dict
# import os
# import shutil
# import logging
# from s3_setup import s3_client
 
# import torch
# from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification

# from dotenv import load_dotenv
# import os

# from utils import doc_processing

# # Load .env file
# load_dotenv()

# # Access variables
# dummy_key = os.getenv("dummy_key")
# HUGGINGFACE_AUTH_TOKEN = dummy_key


# # Hugging Face model and token
# aadhar_model = "AuditEdge/doc_ocr_a"  # Replace with your fine-tuned model if applicable
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# print(f"Using device: {device}")

# # Load the processor (tokenizer + image processor)
# processor_aadhar = LayoutLMv3Processor.from_pretrained(
#     aadhar_model,
#     use_auth_token=HUGGINGFACE_AUTH_TOKEN
# )
# aadhar_model = LayoutLMv3ForTokenClassification.from_pretrained(
#     aadhar_model,
#     use_auth_token=HUGGINGFACE_AUTH_TOKEN
# )


# aadhar_model = aadhar_model.to(device)

# # pan model
# pan_model = "AuditEdge/doc_ocr_p"  # Replace with your fine-tuned model if applicable
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# print(f"Using device: {device}")



# # Load the processor (tokenizer + image processor)
# processor_pan = LayoutLMv3Processor.from_pretrained(
#     pan_model,
#     use_auth_token=HUGGINGFACE_AUTH_TOKEN
# )
# pan_model = LayoutLMv3ForTokenClassification.from_pretrained(
#     pan_model,
#     use_auth_token=HUGGINGFACE_AUTH_TOKEN
# )
# pan_model = pan_model.to(device)

# #
# # gst model
# gst_model = "AuditEdge/doc_ocr_new_g"  # Replace with your fine-tuned model if applicable
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# print(f"Using device: {device}")

# # Load the processor (tokenizer + image processor)
# processor_gst = LayoutLMv3Processor.from_pretrained(
#     gst_model,
#     use_auth_token=HUGGINGFACE_AUTH_TOKEN
# )
# gst_model = LayoutLMv3ForTokenClassification.from_pretrained(
#     gst_model,
#     use_auth_token=HUGGINGFACE_AUTH_TOKEN
# )
# gst_model = gst_model.to(device)

# #cheque model

# cheque_model = "AuditEdge/doc_ocr_new_c"  # Replace with your fine-tuned model if applicable
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# print(f"Using device: {device}")

# # Load the processor (tokenizer + image processor)
# processor_cheque = LayoutLMv3Processor.from_pretrained(
#     cheque_model,
#     use_auth_token=HUGGINGFACE_AUTH_TOKEN
# )
# cheque_model = LayoutLMv3ForTokenClassification.from_pretrained(
#     cheque_model,
#     use_auth_token=HUGGINGFACE_AUTH_TOKEN
# )
# cheque_model = cheque_model.to(device)






# # Verify model and processor are loaded
# print("Model and processor loaded successfully!")
# print(f"Model is on device: {next(aadhar_model.parameters()).device}")


# # Import inference modules
# from layoutlmv3FineTuning.Layoutlm_inference.ocr import prepare_batch_for_inference
# from layoutlmv3FineTuning.Layoutlm_inference.inference_handler import handle

# # Create FastAPI instance
# app = FastAPI(debug=True)

# # Enable CORS
# app.add_middleware(
#     CORSMiddleware,
#     allow_origins=["*"],
#     allow_credentials=True,
#     allow_methods=["*"],
#     allow_headers=["*"],
# )

# # Configure directories
# UPLOAD_FOLDER = './uploads/'
# processing_folder = "./processed_images"
# os.makedirs(UPLOAD_FOLDER, exist_ok=True)  # Ensure the main upload folder exists
# os.makedirs(processing_folder,exist_ok=True)

# UPLOAD_DIRS = {
#     "aadhar_file": "uploads/aadhar/",
#     "pan_file": "uploads/pan/",
#     "cheque_file": "uploads/cheque/",
#     "gst_file": "uploads/gst/",
# }

# process_dirs = {
#     "aadhar_file": "processed_images/aadhar/",
#     "pan_file": "processed_images/pan/",
#     "cheque_file": "processed_images/cheque/",
#     "gst_file": "processed_images/gst/",

# }

# # Ensure individual directories exist
# for dir_path in UPLOAD_DIRS.values():
#     os.makedirs(dir_path, exist_ok=True)
    
# for dir_path in process_dirs.values():
#     os.makedirs(dir_path, exist_ok=True)
    
    

# # Logger configuration
# logging.basicConfig(level=logging.INFO)

# # Perform Inference
# def perform_inference(file_paths: Dict[str, str]):
#     # Dictionary to map document types to their respective model directories
#     model_dirs = {
#         "aadhar_file": aadhar_model,
#         "pan_file": pan_model,
#         "cheque_file": cheque_model,
#         "gst_file": gst_model,
#     }
#     try: 
#         # Dictionary to store results for each document type
#         inference_results = {}

#         # Loop through the file paths and perform inference
#         for doc_type, file_path in file_paths.items():
#             if doc_type in model_dirs:
#                 print(f"Processing {doc_type} using model at {model_dirs[doc_type]}")

#                 # Prepare batch for inference
#                 processed_file_p = file_path.split("&&")[0]
#                 unprocessed_file_path = file_path.split("&&")[1]

#                 images_path = [processed_file_p]
#                 inference_batch = prepare_batch_for_inference(images_path)

#                 # Prepare context for the specific document type
#                 # context = {"model_dir": model_dirs[doc_type]}
#                 #initialize s3 client 
#                 client = s3_client()

#                 local_file_path= unprocessed_file_path
#                 bucket_name = "edgekycdocs"
                
#                 file_name = unprocessed_file_path.split("/")[-1]
                
                


#                 # context = aadhar_model
#                 if doc_type == "aadhar_file":
#                     context = aadhar_model
#                     processor = processor_aadhar
#                     name = "aadhar"
#                     attachemnt_num = 3
#                     folder_name = "aadhardocs"

                    
#                 if doc_type == "pan_file":
#                     context = pan_model
#                     processor = processor_pan
#                     name = "pan"
#                     attachemnt_num = 2
#                     folder_name = "pandocs"
                    
#                 if doc_type == "gst_file":
#                     context = gst_model
#                     processor = processor_gst
#                     name = "gst"
#                     attachemnt_num = 4
#                     folder_name = "gstdocs"
                    
#                 if doc_type == "cheque_file":
#                     context = cheque_model
#                     processor = processor_cheque
#                     name = "cheque"
#                     attachemnt_num = 8
#                     folder_name = "bankchequedocs"
                

                
#                 # upload the document to s3 bucket here


#                 print("this is folder name",folder_name)

#                 response = client.upload_file(local_file_path,bucket_name,folder_name,file_name)

#                 print("The file has been uploaded to s3 bucket",response)
                    

#                 # Perform inference (replace `handle` with your actual function)
#                 result = handle(inference_batch, context,processor,name)
#                 # result["attachment_url": response["url"]]
#                 result["attachment_url"] = response["url"]
#                 result["detect"] = True

#                 print("result required",result)

#                 # if result[""]

#                 # Store the result
#                 inference_results["attachment_{}".format(attachemnt_num)] = result
#             else:
#                 print(f"Model directory not found for {doc_type}. Skipping.")
#             # print(Javed)

#             return inference_results
#     except:
#         return {
#                 "status": "error",
#                 "message": "Text extraction failed."
#                 }


# # Routes
# @app.get("/")
# def greet_json():
#     return {"Hello": "World!"}

# @app.post("/api/aadhar_ocr")
# async def aadhar_ocr(
#     aadhar_file: UploadFile = File(None),
#     pan_file: UploadFile = File(None),
#     cheque_file: UploadFile = File(None),
#     gst_file: UploadFile = File(None),
# ):
#     # try:
#         # Handle file uploads
#     file_paths = {}
#     for file_type, folder in UPLOAD_DIRS.items():
#         file = locals()[file_type]  # Dynamically access the file arguments
#         if file:
#             # Save the file in the respective directory
#             file_path = os.path.join(folder, file.filename)

#             print("this is the filename",file.filename)
#             with open(file_path, "wb") as buffer:
#                 shutil.copyfileobj(file.file, buffer)
#             file_paths[file_type] = file_path

#     # Log received files
#     logging.info(f"Received files: {list(file_paths.keys())}")
#     print("file_paths",file_paths)
    
#     files = {}
#     for key, value in file_paths.items():
#         name = value.split("/")[-1].split(".")[0]
#         id_type = key.split("_")[0]
#         doc_type = value.split("/")[-1].split(".")[-1]
#         f_path = value

#         print("variables required",name,id_type,doc_type,f_path)
#         preprocessing = doc_processing(name,id_type,doc_type,f_path)
#         response = preprocessing.process()

#         print("response after preprocessing",response)

#         files[key] = response["output_p"] + "&&" + f_path
#         # files["unprocessed_file_path"] = f_path
#         print("response",response)

    
#     # Perform inference
#     result = perform_inference(files)

#     print("this is the result we got",result)
#     if "status" in list(result.keys()):
#         raise Exception("Custom error message")
#     # if result["status"] == "error":
        


#     return {"status": "success", "result": result}


#     # except Exception as e:
#     #     logging.error(f"Error processing files: {e}")
#     #     # raise HTTPException(status_code=500, detail="Internal Server Error")
#     #     return {
#     #             "status": 400,
#     #             "message": "Text extraction failed."
#     #             }
    
    





from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from typing import Dict
import os
import shutil
import torch
import logging
from s3_setup import s3_client
import requests
from fastapi import FastAPI, HTTPException, Request
from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
from dotenv import load_dotenv
import urllib.parse
from utils import doc_processing

# Load .env file
load_dotenv()

# Access variables
dummy_key = os.getenv("dummy_key")
HUGGINGFACE_AUTH_TOKEN = dummy_key

# Hugging Face model and token
aadhar_model = "AuditEdge/doc_ocr_a"  # Replace with your fine-tuned model if applicable
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# Load the processor (tokenizer + image processor)
processor_aadhar = LayoutLMv3Processor.from_pretrained(
    aadhar_model, use_auth_token=HUGGINGFACE_AUTH_TOKEN
)
aadhar_model = LayoutLMv3ForTokenClassification.from_pretrained(
    aadhar_model, use_auth_token=HUGGINGFACE_AUTH_TOKEN
)


aadhar_model = aadhar_model.to(device)

# pan model
pan_model = "AuditEdge/doc_ocr_p"  # Replace with your fine-tuned model if applicable
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")


# Load the processor (tokenizer + image processor)
processor_pan = LayoutLMv3Processor.from_pretrained(
    pan_model, use_auth_token=HUGGINGFACE_AUTH_TOKEN
)
pan_model = LayoutLMv3ForTokenClassification.from_pretrained(
    pan_model, use_auth_token=HUGGINGFACE_AUTH_TOKEN
)
pan_model = pan_model.to(device)

#
# gst model
gst_model = (
    "AuditEdge/doc_ocr_new_g"  # Replace with your fine-tuned model if applicable
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# Load the processor (tokenizer + image processor)
processor_gst = LayoutLMv3Processor.from_pretrained(
    gst_model, use_auth_token=HUGGINGFACE_AUTH_TOKEN
)
gst_model = LayoutLMv3ForTokenClassification.from_pretrained(
    gst_model, use_auth_token=HUGGINGFACE_AUTH_TOKEN
)
gst_model = gst_model.to(device)

# cheque model

cheque_model = (
    "AuditEdge/doc_ocr_new_c"  # Replace with your fine-tuned model if applicable
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# Load the processor (tokenizer + image processor)
processor_cheque = LayoutLMv3Processor.from_pretrained(
    cheque_model, use_auth_token=HUGGINGFACE_AUTH_TOKEN
)
cheque_model = LayoutLMv3ForTokenClassification.from_pretrained(
    cheque_model, use_auth_token=HUGGINGFACE_AUTH_TOKEN
)
cheque_model = cheque_model.to(device)


# Verify model and processor are loaded
print("Model and processor loaded successfully!")
print(f"Model is on device: {next(aadhar_model.parameters()).device}")


# Import inference modules
from layoutlmv3FineTuning.Layoutlm_inference.ocr import prepare_batch_for_inference
from layoutlmv3FineTuning.Layoutlm_inference.inference_handler import handle

# Create FastAPI instance
app = FastAPI(debug=True)

# Enable CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Configure directories
UPLOAD_FOLDER = "./uploads/"
processing_folder = "./processed_images"
os.makedirs(UPLOAD_FOLDER, exist_ok=True)  # Ensure the main upload folder exists
os.makedirs(processing_folder, exist_ok=True)


UPLOAD_DIRS = {
    "pan_file": "uploads/pan/",
    "aadhar_file": "uploads/aadhar/",
    "gst_file": "uploads/gst/",
    "msme_file": "uploads/msme/",
    "cin_llpin_file": "uploads/cin_llpin/",
    "cheque_file": "uploads/cheque/",
}


process_dirs = {
    "aadhar_file": "processed_images/aadhar/",
    "pan_file": "processed_images/pan/",
    "cheque_file": "processed_images/cheque/",
    "gst_file": "processed_images/gst/",
}

# Ensure individual directories exist
for dir_path in UPLOAD_DIRS.values():
    os.makedirs(dir_path, exist_ok=True)

for dir_path in process_dirs.values():
    os.makedirs(dir_path, exist_ok=True)


# Logger configuration
logging.basicConfig(level=logging.INFO)


# Perform Inference with optional S3 upload
def perform_inference(file_paths: Dict[str, str], upload_to_s3: bool):
    model_dirs = {
        "pan_file": pan_model,
        "gst_file": gst_model,
        "cheque_file": cheque_model,
    }
    try:
        inference_results = {}

        for doc_type, file_path in file_paths.items():
            if doc_type in model_dirs:
                print(f"Processing {doc_type} using model at {model_dirs[doc_type]}")

                processed_file_p = file_path.split("&&")[0]
                unprocessed_file_path = file_path.split("&&")[1]
                images_path = [processed_file_p]
                inference_batch = prepare_batch_for_inference(images_path)

                context = model_dirs[doc_type]
                processor = globals()[f"processor_{doc_type.split('_')[0]}"]
                name = doc_type.split("_")[0]
                attachemnt_num = {
                    "pan_file": 2,
                    "gst_file": 4,
                    "msme_file": 5,
                    "cin_llpin_file": 6,
                    "cheque_file": 8,
                }[doc_type]

                if upload_to_s3:
                    client = s3_client()
                    bucket_name = "edgekycdocs"
                    folder_name = f"{name}docs"
                    file_name = unprocessed_file_path.split("/")[-1]
                    response = client.upload_file(
                        unprocessed_file_path, bucket_name, folder_name, file_name
                    )
                    print("The file has been uploaded to S3 bucket", response)
                    attachment_url = response["url"]
                else:
                    attachment_url = None

                result = handle(inference_batch, context, processor, name)
                result["attachment_url"] = attachment_url
                result["detect"] = True

                inference_results[f"attachment_{attachemnt_num}"] = result
            else:
                print(f"Model directory not found for {doc_type}. Skipping.")

        return inference_results
    except:
        return {"status": "error", "message": "Text extraction failed."}


# Routes
@app.get("/")
def greet_json():
    return {"Hello": "World!"}


@app.post("/api/aadhar_ocr")
async def aadhar_ocr(

    aadhar_file: UploadFile = File(None),

    pan_file: UploadFile = File(None),

    cheque_file: UploadFile = File(None),

    gst_file: UploadFile = File(None),

    msme_file: UploadFile = File(None),

    cin_llpin_file: UploadFile = File(None),

    upload_to_s3: bool = True,

):
    # try:
    # Handle file uploads
    file_paths = {}
    for file_type, folder in UPLOAD_DIRS.items():
        file = locals()[file_type]  # Dynamically access the file arguments
        if file:
            # Save the file in the respective directory
            file_path = os.path.join(folder, file.filename)

            print("this is the filename", file.filename)
            with open(file_path, "wb") as buffer:
                shutil.copyfileobj(file.file, buffer)
            file_paths[file_type] = file_path

    # Log received files
    logging.info(f"Received files: {list(file_paths.keys())}")
    print("file_paths", file_paths)

    files = {}
    for key, value in file_paths.items():
        name = value.split("/")[-1].split(".")[0]
        id_type = key.split("_")[0]
        doc_type = value.split("/")[-1].split(".")[-1]
        f_path = value

        print("variables required", name, id_type, doc_type, f_path)
        preprocessing = doc_processing(name, id_type, doc_type, f_path)
        response = preprocessing.process()

        print("response after preprocessing", response)

        files[key] = response["output_p"] + "&&" + f_path
        # files["unprocessed_file_path"] = f_path
        print("response", response)

    # Perform inference
    result = perform_inference(files, upload_to_s3)

    print("this is the result we got", result)
    if "status" in list(result.keys()):
        raise Exception("Custom error message")
    # if result["status"] == "error":

    return {"status": "success", "result": result}


@app.post("/api/document_ocr")
async def document_ocr_s3(request: Request):
    try:
        body = await request.json()  # Read JSON body
        logging.info(f"Received request body: {body}")
    except Exception as e:
        logging.error(f"Failed to parse JSON request: {e}")
        raise HTTPException(status_code=400, detail="Invalid JSON payload")

    # Extract file URLs
    url_mapping = {
        "pan_file": body.get("pan_file"),
        "gst_file": body.get("gst_file"),
        "msme_file": body.get("msme_file"),
        "cin_llpin_file": body.get("cin_llpin_file"),
        "cheque_file": body.get("cheque_file"),
    }
    upload_to_s3 = body.get("upload_to_s3", False)
    logging.info(f"URL Mapping: {url_mapping}")
    file_paths = {}
    for file_type, url in url_mapping.items():
        if url:
            # local_filename = url.split("/")[-1]
            local_filename = urllib.parse.unquote(url.split("/")[-1]).replace(" ", "_")
            file_path = os.path.join(UPLOAD_DIRS[file_type], local_filename)

            try:
                logging.info(f"Attempting to download {url} for {file_type}...")
                response = requests.get(url, stream=True)
                response.raise_for_status()

                with open(file_path, "wb") as buffer:
                    shutil.copyfileobj(response.raw, buffer)

                file_paths[file_type] = file_path
                logging.info(f"Successfully downloaded {file_type} to {file_path}")

            except requests.exceptions.RequestException as e:
                logging.error(f"Failed to download {url}: {e}")
                raise HTTPException(
                    status_code=400, detail=f"Failed to download file from {url}"
                )

    logging.info(f"Downloaded files: {list(file_paths.keys())}")

    files = {}
    for key, value in file_paths.items():
        name = value.split("/")[-1].split(".")[0]
        id_type = key.split("_")[0]
        doc_type = value.split("/")[-1].split(".")[-1]
        f_path = value

        preprocessing = doc_processing(name, id_type, doc_type, f_path)
        response = preprocessing.process()

        files[key] = response["output_p"] + "&&" + f_path

    result = perform_inference(files, upload_to_s3)

    if "status" in list(result.keys()):
        raise HTTPException(status_code=500, detail="Custom error message")

    return {"status": "success", "result": result}