neww

.pre-commit-config.yaml

@@ -0,0 +1,86 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Pre-commit hooks. For more information see https://github.com/pre-commit/pre-commit-hooks/blob/main/README.md
+# Optionally remove from local hooks with 'rm .git/hooks/pre-commit'
+
+# Define bot property if installed via https://github.com/marketplace/pre-commit-ci
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: "[pre-commit.ci] pre-commit suggestions"
+  autoupdate_schedule: monthly
+  submodules: true
+
+# Exclude directories (optional)
+# exclude: 'docs/'
+
+# Define repos to run
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: check-case-conflict
+      # - id: check-yaml
+      - id: check-docstring-first
+      - id: detect-private-key
+
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.15.0
+    hooks:
+      - id: pyupgrade
+        name: Upgrade code
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.1.11
+    hooks:
+      - id: ruff
+        args: [--fix]
+
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.17
+    hooks:
+      - id: mdformat
+        name: MD formatting
+        additional_dependencies:
+          - mdformat-gfm
+          - mdformat-frontmatter
+          - mdformat-mkdocs
+        args:
+          - --wrap=no
+          - --number
+        exclude: 'docs/.*\.md'
+        # exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md"
+
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.6
+    hooks:
+      - id: codespell
+        exclude: "docs/de|docs/fr|docs/pt|docs/es|docs/mkdocs_de.yml"
+        args:
+          - --ignore-words-list=crate,nd,ned,strack,dota,ane,segway,fo,gool,winn,commend,bloc,nam,afterall
+
+  - repo: https://github.com/hadialqattan/pycln
+    rev: v2.4.0
+    hooks:
+      - id: pycln
+        args: [--all]
+#
+#  - repo: https://github.com/PyCQA/docformatter
+#    rev: v1.7.5
+#    hooks:
+#      - id: docformatter
+
+#  - repo: https://github.com/asottile/yesqa
+#    rev: v1.4.0
+#    hooks:
+#      - id: yesqa
+
+#  - repo: https://github.com/asottile/dead
+#    rev: v1.5.0
+#    hooks:
+#    -   id: dead
+
+#  - repo: https://github.com/ultralytics/pre-commit
+#    rev: bd60a414f80a53fb8f593d3bfed4701fc47e4b23
+#    hooks:
+#      - id: capitalize-comments

EasyOCR-Trainer

@@ -0,0 +1 @@
+Subproject commit b7e2a8df13046defb646ee5e24bc6659d7ddbbf7

LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot convey a
+covered work so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Remote Network Interaction; Use with the GNU General Public License.
+
+  Notwithstanding any other provision of this License, if you modify the
+Program, your modified version must prominently offer all users
+interacting with it remotely through a computer network (if your version
+supports such interaction) an opportunity to receive the Corresponding
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+from a network server at no charge, through some standard or customary
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+shall include the Corresponding Source for any work covered by version 3
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+permission to link or combine any covered work with a work licensed
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+3 of the GNU General Public License.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU Affero General Public License from time to time.  Such new versions
+will be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU Affero General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU Affero General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU Affero General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
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+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
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+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
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+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

README.md

@@ -1,11 +1,10 @@
 ---
 title: OCRDemo
-emoji: 💻
-colorFrom: red
-colorTo: indigo
+emoji: 🚀
+colorFrom: blue
+colorTo: green
 sdk: docker
+sdk_version: "1.0.0"  # Add this field if it's missing
+app_file: app.py
 pinned: false
-short_description: This is demo on OCR
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -102,10 +102,7 @@ def process_image():
     # Check contents in the root directory
     print("Current directory contents:", os.listdir('/'))
 
-    model = YOLOv10(f'./runs/detect/train3/weights/best.pt')
-    #device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    #model.to(device)
-
+    model = YOLOv10(f'./runs/detect/train3/weights/best (1).pt')
     dataset = sv.DetectionDataset.from_yolo(
         images_directory_path=f"./data/MyNewVersion5.0Dataset/valid/images",
         annotations_directory_path=f"./data/MyNewVersion5.0Dataset/valid/labels",
@@ -241,4 +238,8 @@ def download_image(filename):
 
 @app.route('/uploads/<filename>')
 def serve_uploaded_file(filename):
-    return send_from_directory('./app/data1', filename)
+    return send_from_directory('./app/data1', filename)
+
+
+
+

app1.py

@@ -0,0 +1,232 @@
+import os
+import csv
+import easyocr
+import shutil
+import random
+import cv2
+from glob import glob
+from ultralytics import YOLOv10
+import random
+import cv2
+from glob import glob
+from ultralytics import YOLOv10
+
+import supervision as sva
+from ultralytics import YOLOv10
+
+import supervision as sv
+import supervision as sv
+import os
+import csv
+import cv2
+from flask import Flask, request, jsonify, send_from_directory, render_template
+import shutil
+from ultralytics import YOLOv10
+import random
+import cv2
+import csv
+import re
+import textwrap
+import easyocr
+import re
+import textwrap
+import supervision as sv
+import os
+import re
+app = Flask(__name__)
+output_dir1 = "/var/www/html/python/OCR-AI/OCR-Project/Folder1"
+output_dir2 = "/var/www/html/python/OCR-AI/OCR-Project/Folder2"
+output_dir3 = "/var/www/html/python/OCR-AI/OCR-Project/Folder3"
+UPLOAD_FOLDER = "/var/www/html/python/OCR-AI/OCR-Project/data1"
+os.makedirs(output_dir1, exist_ok=True)
+os.makedirs(output_dir2, exist_ok=True)
+os.makedirs(output_dir3, exist_ok=True)
+
+@app.route('/')
+def index():
+    return render_template('index3.html')  # This will serve your HTML page
+
+    
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    if 'invoice-upload' not in request.files:
+        return jsonify({'error': 'No file part'}), 400
+    file = request.files['invoice-upload']
+    if file.filename == '':
+        return jsonify({'error': 'No selected file'}), 400
+    if file:
+        # Save uploaded file
+        file_path = os.path.join(UPLOAD_FOLDER, file.filename)
+        file.save(file_path)
+
+        # Process the file
+        output_image, output_csv = process_image()
+
+        return jsonify({
+            'image_path': output_image,
+            'csv_path': output_csv
+        })
+
+def enhance_contrast(image):
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    equalized_image = cv2.equalizeHist(gray_image)
+    return equalized_image
+
+def calculate_iou(bbox1, bbox2):
+    x1_max = max(bbox1[0], bbox2[0])
+    y1_max = max(bbox1[1], bbox2[1])
+    x2_min = min(bbox1[2], bbox2[2])
+    y2_min = min(bbox1[3], bbox2[3])
+
+    inter_area = max(0, x2_min - x1_max) * max(0, y2_min - y1_max)
+
+    bbox1_area = (bbox1[2] - bbox1[0]) * (bbox1[3] - bbox1[1])
+    bbox2_area = (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1])
+
+    iou = inter_area / float(bbox1_area + bbox2_area - inter_area) if (bbox1_area + bbox2_area - inter_area) > 0 else 0
+    return iou
+def process_image():
+  cropped_dir = "/var/www/html/python/OCR-AI/OCR-Project/cropped_images/"
+  if os.path.exists(cropped_dir):
+    shutil.rmtree(cropped_dir)
+  os.makedirs(cropped_dir, exist_ok=True)
+  model = YOLOv10(f'/var/www/html/python/OCR-AI/OCR-Project/runs/detect/train3/weights/best (1).pt')
+  dataset = sv.DetectionDataset.from_yolo(
+    images_directory_path=f"/var/www/html/python/OCR-AI/OCR-Project/data/MyNewVersion5.0Dataset/valid/images",
+    annotations_directory_path=f"/var/www/html/python/OCR-AI/OCR-Project/data/MyNewVersion5.0Dataset/valid/labels",
+    data_yaml_path=f"/var/www/html/python/OCR-AI/OCR-Project/data/MyNewVersion5.0Dataset/data.yaml"
+    )
+  bounding_box_annotator = sv.BoundingBoxAnnotator()
+  label_annotator = sv.LabelAnnotator()
+  files=os.listdir('/var/www/html/python/OCR-AI/OCR-Project/data1/')
+  for ii in files:
+    random_image_data = cv2.imread('/var/www/html/python/OCR-AI/OCR-Project/data1/'+ii)
+    random_image_data1 = cv2.imread('/var/www/html/python/OCR-AI/OCR-Project/data1/'+ii)
+    results = model(source='/var/www/html/python/OCR-AI/OCR-Project/data1/', conf=0.07)[0]
+    detections = sv.Detections.from_ultralytics(results)
+    annotated_image = bounding_box_annotator.annotate(scene=random_image_data, detections=detections)
+    annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)
+    save_path="/var/www/html/python/OCR-AI/OCR-Project/Folder1/"+"detection"+ii
+    cv2.imwrite(save_path, annotated_image)
+    print(f"Annotated image saved at {save_path}")
+    bounding_boxes = results.boxes.xyxy.cpu().numpy()
+    class_ids = results.boxes.cls.cpu().numpy()
+    confidences = results.boxes.conf.cpu().numpy()
+    bounding_box_save_path = "/var/www/html/python/OCR-AI/OCR-Project/bounding_boxes.txt"
+    with open(bounding_box_save_path, 'w') as f:
+      for i, (bbox, class_id, confidence) in enumerate(zip(bounding_boxes, class_ids, confidences)):
+        x1, y1, x2, y2 = map(int, bbox)
+        f.write(f"Object {i+1}: Class {class_id}, Confidence: {confidence:.2f}, "
+                f"Bounding box: ({x1}, {y1}, {x2}, {y2})\n")
+        cropped_image = random_image_data1[y1:y2, x1:x2]
+        cropped_image_path = os.path.join(cropped_dir, f"cropped_object_{i+1}.jpg")
+        cv2.imwrite(cropped_image_path,  cropped_image)
+        print(f"Enhanced cropped image saved at {cropped_image_path}")
+    print(f"Bounding box coordinates saved at {bounding_box_save_path}")
+    reader = easyocr.Reader(
+    ['en'],
+    #detector=False,
+    recog_network='en_sample',
+    model_storage_directory='/var/www/html/python/OCR-AI/OCR-Project/EasyOCR-Trainer/EasyOCR/easyocr/model',
+    user_network_directory='/var/www/html/python/OCR-AI/OCR-Project/EasyOCR-Trainer/EasyOCR/user_network')
+    input_file_path = '/var/www/html/python/OCR-AI/OCR-Project/bounding_boxes.txt'
+    cropped_images_folder = '/var/www/html/python/OCR-AI/OCR-Project/cropped_images/'
+    output_csv_path = '/var/www/html/python/OCR-AI/OCR-Project/Folder2/'+ii+'bounding_boxes_with_recognition.csv'
+    print(output_csv_path)
+    with open(input_file_path, 'r') as infile:
+      lines = infile.readlines()
+      print(lines)
+    with open(output_csv_path, 'w', newline='', encoding='utf-8') as csvfile:
+      csv_writer = csv.writer(csvfile)
+      csv_writer.writerow(['Object ID', 'Bounding Box', 'Image Name', 'Recognized Text'])
+      for i, line in enumerate(lines):
+        print(f"Processing line {i+1}/{len(lines)}: {line.strip()}")
+        object_id = f"Object_{i+1}"
+        bounding_box_info = line.strip()
+        cropped_image_name = f"cropped_object_{i+1}.jpg"
+        cropped_image_path = os.path.join(cropped_images_folder, cropped_image_name)
+        print(f"Processing Object {i}, cropped image path: {cropped_image_path}")
+        if os.path.exists(cropped_image_path):
+            bbox_match = re.search(r"Bounding box: \((\d+), (\d+), (\d+), (\d+)\)", bounding_box_info)
+            if bbox_match:
+               x1, y1, x2, y2 = map(int, bbox_match.groups())
+               detected_boxes = [[x1, x2, y1, y2]]
+            else:
+              print("No bounding box found in the info.")
+     
+            cropped_image = cv2.imread(cropped_image_path, cv2.IMREAD_GRAYSCALE)
+            print(cropped_image.shape)
+               
+            
+            horizontal_list1,free_list1=reader.detect(cropped_image)
+            print("-----")
+            free_list1 = free_list1 if free_list1 is not None else []
+            horizontal_list1 = [box for sublist in horizontal_list1 for box in sublist]
+            free_list1=[]
+            horizontal_list_for_recognize = detected_boxes if not horizontal_list1 else horizontal_list1
+            print(horizontal_list1)
+            horizontal_list1=[]
+            if horizontal_list1:
+              print("-----")
+              result = reader.recognize(cropped_image ,detail=0,horizontal_list= horizontal_list1,free_list=free_list1)
+              print("-----")
+            else:
+               result = reader.recognize( random_image_data1,detail=0,horizontal_list=detected_boxes,free_list=free_list1)
+            print(result)
+            recognized_text = ' '.join(result) if result else ''
+        else:
+            recognized_text = 'No image found'
+        csv_writer.writerow([object_id, bounding_box_info, cropped_image_name, recognized_text])
+    print(f"CSV file with recognition results saved at {output_csv_path}")
+    image_path = "/var/www/html/python/OCR-AI/OCR-Project/data1/"+ii
+    csv_file_path =   output_csv_path = '/var/www/html/python/OCR-AI/OCR-Project/Folder2/'+ii+'bounding_boxes_with_recognition.csv'
+    image = cv2.imread(image_path)
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    font_scale = 1.3
+    font_thickness = 2
+    color = (255, 0, 255)
+    bboxes = []
+    recognized_texts = []
+    with open(csv_file_path, 'r') as csvfile:
+      csv_reader = csv.DictReader(csvfile)  # Use DictReader to access columns by header name
+      for row in csv_reader:
+        # Extract the bounding box using regex to find coordinates in the 'Bounding Box' field
+        bbox_match = re.search(r'\((\d+), (\d+), (\d+), (\d+)\)', row['Bounding Box'])
+        if bbox_match:
+            bbox = [int(bbox_match.group(i)) for i in range(1, 5)]  # Extract and convert to integers
+            bboxes.append(bbox)
+            # Extract the recognized text from the 'Recognized Text' field
+            recognized_texts.append(row['Recognized Text'])
+    filtered_bboxes = []
+    filtered_texts = []
+    iou_threshold = 0.5
+    for i, bbox1 in enumerate(bboxes):
+      keep = True
+      for j, bbox2 in enumerate(filtered_bboxes):
+        if calculate_iou(bbox1, bbox2) > iou_threshold:
+            keep = False  # If IoU exceeds the threshold, ignore this bounding box
+            break
+      if keep:
+        filtered_bboxes.append(bbox1)
+        filtered_texts.append(recognized_texts[i])
+    for bbox, recognized_text in zip(filtered_bboxes, filtered_texts):
+      x1, y1, x2, y2 = bbox
+      cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
+      max_chars_per_line = 60  
+      wrapped_text = textwrap.wrap(recognized_text, width=max_chars_per_line)
+      text_y = y1 - 10 if y1 - 10 > 10 else y1 + 10 
+      for line in wrapped_text:
+        cv2.putText(image, line, (x1, text_y), font, font_scale, color, font_thickness)
+        text_y += int(font_scale * 20) 
+        output_image_path ="/var/www/html/python/OCR-AI/OCR-Project/Folder3/"+"annotated"+ii+".png"
+        cv2.imwrite(output_image_path, image)
+        print(f"Annotated image saved at {output_image_path}")
+
+@app.route('/download_csv/<filename>')
+def download_csv(filename):
+    return send_from_directory(output_dir2, filename, as_attachment=True)
+
+@app.route('/download_image/<filename>')
+def download_image(filename):
+    return send_from_directory(output_dir3, filename, as_attachment=True)
+

app2.py

@@ -0,0 +1,241 @@
+import os
+import csv
+import easyocr
+import shutil
+import random
+import cv2
+from glob import glob
+from ultralytics import YOLOv10
+import random
+from glob import glob
+from ultralytics import YOLOv10
+import supervision as sva
+from ultralytics import YOLOv10
+import supervision as sv
+import supervision as sv
+from flask import Flask, request, jsonify, send_from_directory, render_template
+
+import textwrap
+app = Flask(__name__)
+
+def enhance_contrast(image):
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    equalized_image = cv2.equalizeHist(gray_image)
+    return equalized_image
+
+
+def calculate_iou(bbox1, bbox2):
+    x1_max = max(bbox1[0], bbox2[0])
+    y1_max = max(bbox1[1], bbox2[1])
+    x2_min = min(bbox1[2], bbox2[2])
+    y2_min = min(bbox1[3], bbox2[3])
+
+    inter_area = max(0, x2_min - x1_max) * max(0, y2_min - y1_max)
+
+    bbox1_area = (bbox1[2] - bbox1[0]) * (bbox1[3] - bbox1[1])
+    bbox2_area = (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1])
+
+    iou = inter_area / float(bbox1_area + bbox2_area - inter_area) if (bbox1_area + bbox2_area - inter_area) > 0 else 0
+    return iou
+
+
+cropped_dir = "./app/cropped_images/"
+if os.path.exists(cropped_dir):
+        shutil.rmtree(cropped_dir)
+os.makedirs(cropped_dir, exist_ok=True)
+
+output_dir1 = "./app/Folder1"
+output_dir2 = "./app/Folder2"
+output_dir3 = "./app/Folder3"
+UPLOAD_FOLDER = "./app/data1"
+os.makedirs(output_dir1, exist_ok=True)
+os.makedirs(output_dir2, exist_ok=True)
+os.makedirs(output_dir3, exist_ok=True)
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+@app.route('/')
+def index():
+    return render_template('index3.html')  # This will serve your HTML page
+
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    if 'invoice-upload' not in request.files:
+        return jsonify({'error': 'No file part'}), 400
+    file = request.files['invoice-upload']
+    if file.filename == '':
+        return jsonify({'error': 'No selected file'}), 400
+    if file:
+        file_path = os.path.join(UPLOAD_FOLDER, file.filename)
+        file.save(file_path)
+        output_image, output_csv = process_image()
+
+        return jsonify({
+            'image_path': output_image,
+            'csv_path': output_csv
+        })
+
+def process_image():
+    print("Current working directory:", os.getcwd())
+    
+    # Check contents in the root directory
+    print("Current directory contents:", os.listdir('/'))
+
+    model = YOLOv10(f'./runs/detect/train3/weights/best (1).pt')
+    dataset = sv.DetectionDataset.from_yolo(
+        images_directory_path=f"./data/MyNewVersion5.0Dataset/valid/images",
+        annotations_directory_path=f"./data/MyNewVersion5.0Dataset/valid/labels",
+        data_yaml_path=f"./data/MyNewVersion5.0Dataset/data.yaml"
+    )
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    label_annotator = sv.LabelAnnotator()
+    image_dir = "./app/data1"
+    files = os.listdir('./app/data1')
+    files.sort()
+    files = files[0:100]
+    print(files)
+    counter = 0
+    for ii in files:
+        random_image_data = cv2.imread('./app/data1/' + ii)
+        random_image_data1 = cv2.imread('./app/data1/' + ii)
+        results = model(source='./app/data1/' + ii, conf=0.07)[0]
+        detections = sv.Detections.from_ultralytics(results)
+        annotated_image = bounding_box_annotator.annotate(scene=random_image_data, detections=detections)
+        annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)
+        save_path = "./app/Folder1/" + "detection" + ii
+        cv2.imwrite(save_path, annotated_image)
+        print(f"Annotated image saved at {save_path}")
+        bounding_boxes = results.boxes.xyxy.cpu().numpy()
+        class_ids = results.boxes.cls.cpu().numpy()
+        confidences = results.boxes.conf.cpu().numpy()
+        bounding_box_save_path = "./bounding_boxes.txt"
+        with open(bounding_box_save_path, 'w') as f:
+            for i, (bbox, class_id, confidence) in enumerate(zip(bounding_boxes, class_ids, confidences)):
+                x1, y1, x2, y2 = map(int, bbox)
+                f.write(f"Object {i + 1}: Class {class_id}, Confidence: {confidence:.2f}, "
+                        f"Bounding box: ({x1}, {y1}, {x2}, {y2})\n")
+                cropped_image = random_image_data1[y1:y2, x1:x2]
+                cropped_image_path = os.path.join(cropped_dir, f"cropped_object_{i + 1}.jpg")
+                cv2.imwrite(cropped_image_path, cropped_image)
+                print(f"Enhanced cropped image saved at {cropped_image_path}")
+        print(f"Checking contents of /app/data: {bounding_box_save_path}")
+        print(f"Directory listing: {os.listdir('./app/Folder1')}")
+        print(f"Bounding box coordinates saved at {bounding_box_save_path}")
+        try:
+          reader = easyocr.Reader(['en'],recog_network='en_sample',model_storage_directory='./EasyOCR-Trainer/EasyOCR/easyocr/model', user_network_directory='./EasyOCR-Trainer/EasyOCR/user_network')
+        except Exception as e:
+                print(f"Error initializing EasyOCR Reader: {e}")
+                raise
+        reader = easyocr.Reader(
+            ['en'],
+            recog_network='en_sample',
+            model_storage_directory='./EasyOCR-Trainer/EasyOCR/easyocr/model',
+            user_network_directory='./EasyOCR-Trainer/EasyOCR/user_network')
+        import re
+        input_file_path = './bounding_boxes.txt'
+        cropped_images_folder = './app/cropped_images/'
+        output_csv_path = './Folder2/' + ii + 'bounding_boxes_with_recognition.csv'
+        print(f"Checking contents of ./app/data: {bounding_box_save_path}")
+        print(f"Directory listing: {os.listdir('./app/data')}")
+
+        with open(input_file_path, 'r') as infile:
+            lines = infile.readlines()
+        with open(output_csv_path, 'w', newline='', encoding='utf-8') as csvfile:
+            csv_writer = csv.writer(csvfile)
+            csv_writer.writerow(['Object ID', 'Bounding Box', 'Image Name', 'Recognized Text'])
+            for i, line in enumerate(lines):
+                object_id = f"Object_{i + 1}"
+                bounding_box_info = line.strip()
+                cropped_image_name = f"cropped_object_{i + 1}.jpg"
+                cropped_image_path = os.path.join(cropped_images_folder, cropped_image_name)
+                if os.path.exists(cropped_image_path):
+                    bbox_match = re.search(r"Bounding box: \((\d+), (\d+), (\d+), (\d+)\)", bounding_box_info)
+                    if bbox_match:
+                        x1, y1, x2, y2 = map(int, bbox_match.groups())
+                        detected_boxes = [[x1, x2, y1, y2]]
+                    else:
+                        print("No bounding box found in the info.")
+                    cropped_image = cv2.imread(cropped_image_path, cv2.IMREAD_GRAYSCALE)
+                    horizontal_list1, free_list1 = reader.detect(cropped_image)
+                    free_list1 = free_list1 if free_list1 is not None else []
+                    horizontal_list1 = [box for sublist in horizontal_list1 for box in sublist]
+                    free_list1 = []
+                    horizontal_list_for_recognize = detected_boxes if not horizontal_list1 else horizontal_list1
+                    if horizontal_list1:
+                        result = reader.recognize(cropped_image, detail=0, horizontal_list=horizontal_list1,
+                                                  free_list=free_list1)
+                    else:
+                        result = reader.recognize(random_image_data1, detail=0, horizontal_list=detected_boxes,
+                                                  free_list=free_list1)
+                    recognized_text = ' '.join(result) if result else ''
+                else:
+                    recognized_text = 'No image found'
+                csv_writer.writerow([object_id, bounding_box_info, cropped_image_name, recognized_text])
+        print(f"CSV file with recognition results saved at {output_csv_path}")
+
+        def calculate_iou(bbox1, bbox2):
+            x1_max = max(bbox1[0], bbox2[0])
+            y1_max = max(bbox1[1], bbox2[1])
+            x2_min = min(bbox1[2], bbox2[2])
+            y2_min = min(bbox1[3], bbox2[3])
+
+            inter_area = max(0, x2_min - x1_max) * max(0, y2_min - y1_max)
+
+            bbox1_area = (bbox1[2] - bbox1[0]) * (bbox1[3] - bbox1[1])
+            bbox2_area = (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1])
+
+            iou = inter_area / float(bbox1_area + bbox2_area - inter_area) if (bbox1_area + bbox2_area - inter_area) > 0 else 0
+            return iou
+
+        image_path = "/data1" + ii
+        csv_file_path = output_csv_path = '/Folder2/' + ii + 'bounding_boxes_with_recognition.csv'
+        image = cv2.imread(image_path)
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        font_scale = 1.3
+        font_thickness = 2
+        color = (255, 0, 255)
+        bboxes = []
+        recognized_texts = []
+        with open(csv_file_path, 'r', encoding='utf-8') as csvfile:
+            csv_reader = csv.DictReader(csvfile)
+            for row in csv_reader:
+                bbox_match = re.search(r'\((\d+), (\d+), (\d+), (\d+)\)', row['Bounding Box'])
+                if bbox_match:
+                    bbox = [int(bbox_match.group(i)) for i in range(1, 5)]
+                    bboxes.append(bbox)
+                    recognized_texts.append(row['Recognized Text'])
+        filtered_bboxes = []
+        filtered_texts = []
+        iou_threshold = 0.4
+        for i, bbox1 in enumerate(bboxes):
+            keep = True
+            for j, bbox2 in enumerate(filtered_bboxes):
+                if calculate_iou(bbox1, bbox2) > iou_threshold:
+                    keep = False
+                    break
+            if keep:
+                filtered_bboxes.append(bbox1)
+                filtered_texts.append(recognized_texts[i])
+        for bbox, recognized_text in zip(filtered_bboxes, filtered_texts):
+            x1, y1, x2, y2 = bbox
+            cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
+            max_chars_per_line = 60
+            wrapped_text = textwrap.wrap(recognized_text, width=max_chars_per_line)
+            text_y = y1 - 10 if y1 - 10 > 10 else y1 + 10
+            for line in wrapped_text:
+                cv2.putText(image, line, (x1, text_y), font, font_scale, color, font_thickness)
+                text_y += int(font_scale * 20)
+            output_image_path = "/Folder3/" + "annotated" + ii + ".png"
+            cv2.imwrite(output_image_path, image)
+            print(f"Annotated image saved at {output_image_path}")
+            counter += 1
+
+@app.route('/download_csv/<filename>')
+def download_csv(filename):
+    return send_from_directory(output_dir2, filename, as_attachment=True)
+
+@app.route('/download_image/<filename>')
+def download_image(filename):
+    return send_from_directory(output_dir3, filename, as_attachment=True)
+
+
+