Update app.py

app.py

@@ -1,92 +1,28 @@
-# Copyright (C) 2021-2024, Mindee.
-
-# This program is licensed under the Apache License 2.0.
-# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
-
-import numpy as np
-import torch
-
-from doctr.models import ocr_predictor
-from doctr.models.predictor import OCRPredictor
-
-DET_ARCHS = [
-    "fast_base",
-    "fast_small",
-    "fast_tiny",
-    "db_resnet50",
-    "db_resnet34",
-    "db_mobilenet_v3_large",
-    "linknet_resnet18",
-    "linknet_resnet34",
-    "linknet_resnet50",
-]
-RECO_ARCHS = [
-    "crnn_vgg16_bn",
-    "crnn_mobilenet_v3_small",
-    "crnn_mobilenet_v3_large",
-    "master",
-    "sar_resnet31",
-    "vitstr_small",
-    "vitstr_base",
-    "parseq",
-]
-
-
-def load_predictor(
-    det_arch: str,
-    reco_arch: str,
-    assume_straight_pages: bool,
-    straighten_pages: bool,
-    export_as_straight_boxes: bool,
-    disable_page_orientation: bool,
-    disable_crop_orientation: bool,
-    bin_thresh: float,
-    box_thresh: float,
-    device: torch.device,
-) -> OCRPredictor:
-    """Load a predictor from doctr.models
-    Args:
-        det_arch: detection architecture
-        reco_arch: recognition architecture
-        assume_straight_pages: whether to assume straight pages or not
-        straighten_pages: whether to straighten rotated pages or not
-        export_as_straight_boxes: whether to export boxes as straight or not
-        disable_page_orientation: whether to disable page orientation or not
-        disable_crop_orientation: whether to disable crop orientation or not
-        bin_thresh: binarization threshold for the segmentation map
-        box_thresh: minimal objectness score to consider a box
-        device: torch.device, the device to load the predictor on
-    Returns:
-        instance of OCRPredictor
-    """
-    predictor = ocr_predictor(
-        det_arch,
-        reco_arch,
-        pretrained=True,
-        assume_straight_pages=assume_straight_pages,
-        straighten_pages=straighten_pages,
-        export_as_straight_boxes=export_as_straight_boxes,
-        detect_orientation=not assume_straight_pages,
-        disable_page_orientation=disable_page_orientation,
-        disable_crop_orientation=disable_crop_orientation,
-    ).to(device)
-    predictor.det_predictor.model.postprocessor.bin_thresh = bin_thresh
-    predictor.det_predictor.model.postprocessor.box_thresh = box_thresh
-    return predictor
-
-
-def forward_image(predictor: OCRPredictor, image: np.ndarray, device: torch.device) -> np.ndarray:
-    """Forward an image through the predictor
-    Args:
-        predictor: instance of OCRPredictor
-        image: image to process
-        device: torch.device, the device to process the image on
-    Returns:
-        segmentation map
-    """
-    with torch.no_grad():
-        processed_batches = predictor.det_predictor.pre_processor([image])
-        out = predictor.det_predictor.model(processed_batches[0].to(device), return_model_output=True)
-        seg_map = out["out_map"].to("cpu").numpy()
-
-    return seg_map
+import gradio as gr
+import pytesseract
+from pdf2image import convert_from_path
+import tempfile
+import os
+
+def ocr_pdf(pdf_file):
+    with tempfile.TemporaryDirectory() as path:
+        pdf_path = os.path.join(path, "temp.pdf")
+        with open(pdf_path, 'wb') as f:
+            f.write(pdf_file.read())
+        
+        images = convert_from_path(pdf_path)
+        text = ""
+        for image in images:
+            text += pytesseract.image_to_string(image)
+    return text
+
+iface = gr.Interface(
+    fn=ocr_pdf,
+    inputs=gr.File(label="Upload PDF", type="binary"),
+    outputs=gr.Textbox(label="Extracted Text"),
+    title="PDF OCR with PyTesseract",
+    description="Upload a PDF file to extract its text using PyTesseract."
+)
+
+if __name__ == "__main__":
+    iface.launch()