Update app.py

app.py

@@ -1,120 +1,102 @@
-# import gradio as gr
-# import tensorflow as tf
-# import requests
-# import os
-# import numpy as np
-# import pandas as pd
-# import huggingface_hub
-# from huggingface_hub import Repository
-# from datetime import datetime
-# import scipy.ndimage.interpolation as inter
-# import datasets
-# from datasets import load_dataset, Image
-# from PIL import Image
-# from paddleocr import PaddleOCR
-# from doctr.io import DocumentFile
+import gradio as gr
+import tensorflow as tf
+import requests
+import os
+import numpy as np
+import pandas as pd
+import huggingface_hub
+from huggingface_hub import Repository
+from datetime import datetime
+import scipy.ndimage.interpolation as inter
+import datasets
+from datasets import load_dataset, Image
+from PIL import Image
+from paddleocr import PaddleOCR
+from doctr.io import DocumentFile
 
 
-# os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # Use GPU 0, adjust if needed
-# os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # Use GPU 0, adjust if needed
+os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
 
 
-# from doctr.models import ocr_predictor
-# model = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True)
+from doctr.models import ocr_predictor
+model = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True)
 
 
 
-# """
-# Perform OCR with doctr
-# """
-# def ocr_with_doctr(file):
-#     text_output = ''
+"""
+Perform OCR with doctr
+"""
+def ocr_with_doctr(file):
+    text_output = ''
     
-#     # Load the document
-#     doc = DocumentFile.from_pdf(file)
+    # Load the document
+    doc = DocumentFile.from_pdf(file)
     
-#     # Perform OCR
-#     result = ocr_model(doc)
+    # Perform OCR
+    result = ocr_model(doc)
     
-#     # Extract text from OCR result
-#     for page in result.pages:
-#         for block in page.blocks:
-#             for line in block.lines:
-#                 text_output += " ".join([word.value for word in line.words]) + "\n"
+    # Extract text from OCR result
+    for page in result.pages:
+        for block in page.blocks:
+            for line in block.lines:
+                text_output += " ".join([word.value for word in line.words]) + "\n"
     
-#     return text_output
-
-# """
-# Paddle OCR
-# """
-# def ocr_with_paddle(img):
-#     finaltext = ''
-#     ocr = PaddleOCR(lang='en', use_angle_cls=True, use_gpu=True)
-#     # img_path = 'exp.jpeg'
-#     result = ocr.ocr(img)
+    return text_output
+
+"""
+Paddle OCR
+"""
+def ocr_with_paddle(img):
+    finaltext = ''
+    ocr = PaddleOCR(lang='en', use_angle_cls=True, use_gpu=True)
+    # img_path = 'exp.jpeg'
+    result = ocr.ocr(img)
     
-#     for i in range(len(result[0])):
-#         text = result[0][i][1][0]
-#         finaltext += ' '+ text
-#     return finaltext
-
-# def generate_ocr(Method, file):
-#     text_output = ''
-#     if isinstance(file, bytes):  # Handle file uploaded as bytes
-#         file = io.BytesIO(file)
-
-#     if file.name.endswith('.pdf'):
-#         # Perform OCR on the PDF using doctr
-#         text_output = ocr_with_doctr(file)
-
-#     else:
-#         # Handle image file
-#         img_np = np.array(Image.open(file))
-#         text_output = generate_text_from_image(Method, img_np)
+    for i in range(len(result[0])):
+        text = result[0][i][1][0]
+        finaltext += ' '+ text
+    return finaltext
+
+def generate_ocr(Method, file):
+    text_output = ''
+    if isinstance(file, bytes):  # Handle file uploaded as bytes
+        file = io.BytesIO(file)
+
+    if file.name.endswith('.pdf'):
+        # Perform OCR on the PDF using doctr
+        text_output = ocr_with_doctr(file)
+
+    else:
+        # Handle image file
+        img_np = np.array(Image.open(file))
+        text_output = generate_text_from_image(Method, img_np)
     
-#     return text_output
-
-# def generate_text_from_image(Method, img):
-#     text_output = ''
-#     if Method == 'PaddleOCR':
-#         text_output = ocr_with_paddle(img)
-#     return text_output
-
-
-# import gradio as gr
+    return text_output
 
-# image_or_pdf = gr.File(label="Upload an image or PDF")
-# method = gr.Radio(["PaddleOCR"], value="PaddleOCR")
-# output = gr.Textbox(label="Output")
+def generate_text_from_image(Method, img):
+    text_output = ''
+    if Method == 'PaddleOCR':
+        text_output = ocr_with_paddle(img)
+    return text_output
 
-# demo = gr.Interface(
-#     generate_ocr,
-#     [method, image_or_pdf],
-#     output,
-#     title="Optical Character Recognition",
-#     css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}",
-#     article="""<p style='text-align: center;'>Feel free to give us your thoughts on this demo and please contact us at 
-#                 <a href="mailto:[email protected]" target="_blank">[email protected]</a> 
-#                 <p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>"""
-# )
 
-# demo.launch(share=True)
+import gradio as gr
 
-import subprocess
-
-try:
-    nvcc_version = subprocess.check_output(["nvcc", "--version"]).decode("utf-8")
-    print("CUDA Version:", nvcc_version)
-except FileNotFoundError:
-    print("nvcc not found. CUDA may not be installed or not in the system's PATH.")
-
-import paddle
-print("PaddlePaddle Version:", paddle.__version__)
-print("Is GPU available:", paddle.is_compiled_with_cuda())
-import tensorflow as tf
-print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
-import tensorflow as tf
-print("Where tensorflow is built with cuda:",tf.test.is_built_with_cuda())
+image_or_pdf = gr.File(label="Upload an image or PDF")
+method = gr.Radio(["PaddleOCR"], value="PaddleOCR")
+output = gr.Textbox(label="Output")
 
+demo = gr.Interface(
+    generate_ocr,
+    [method, image_or_pdf],
+    output,
+    title="Optical Character Recognition",
+    css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}",
+    article="""<p style='text-align: center;'>Feel free to give us your thoughts on this demo and please contact us at 
+                <a href="mailto:[email protected]" target="_blank">[email protected]</a> 
+                <p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>"""
+)
 
+demo.launch(share=True)