Update app.py

app.py

@@ -1,94 +1,100 @@
-import gradio as gr
-import requests
-import os
-from datasets import load_dataset, Image
-from PIL import Image
-from paddleocr import PaddleOCR
-from doctr.io import DocumentFile
+# import gradio as gr
+# import requests
+# import os
+# from datasets import load_dataset, Image
+# from PIL import Image
+# from paddleocr import PaddleOCR
+# from doctr.io import DocumentFile
 
 
-# Set environment variable for PyTorch usage
-os.environ['USE_TF'] = '0'  # Set TensorFlow to off
-os.environ['USE_TORCH'] = '1'  # Set PyTorch to on
+# # Set environment variable for PyTorch usage
+# os.environ['USE_TF'] = '0'  # Set TensorFlow to off
+# os.environ['USE_TORCH'] = '1'  # Set PyTorch to on
 
-from doctr.models import ocr_predictor
-ocr_model = ocr_predictor(det_arch='db_resnet50', reco_arch='crnn_vgg16_bn', pretrained=True)
+# from doctr.models import ocr_predictor
+# ocr_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
+#     return text_output
+
+# def generate_text_from_image(Method, img):
+#     text_output = ''
+#     if Method == 'PaddleOCR':
+#         text_output = ocr_with_paddle(img)
+#     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
 
-import gradio as gr
+# image_or_pdf = gr.File(label="Upload an image or PDF")
+# method = gr.Radio(["PaddleOCR"], value="PaddleOCR")
+# output = gr.Textbox(label="Output")
 
-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 = 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 os
 
-demo.launch(share=True)
+# Disable TensorFlow to ensure PyTorch is used
+os.environ['USE_TF'] = '0'
 
+import torch
+print(torch.cuda.is_available())  # Should return True if GPU is available