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app.py

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+import streamlit as st
+from streamlit_cropper import st_cropper
+from PIL import Image
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel, DonutProcessor
+import torch
+import re
+import pytesseract
+
+
+def predict_arabic(img, model_name="UBC-NLP/Qalam"):
+  # if img is None:
+  #   _,generated_text=main(image)
+  #   return generated_text
+  # else:
+    # model_name = "UBC-NLP/Qalam"
+    processor = TrOCRProcessor.from_pretrained(model_name)
+    model = VisionEncoderDecoderModel.from_pretrained(model_name)
+    images = img.convert("RGB")
+    pixel_values = processor(images, return_tensors="pt").pixel_values
+    generated_ids = model.generate(pixel_values, max_length=256)
+    generated_text = processor.batch_decode(
+        generated_ids, skip_special_tokens=True)[0]
+    return generated_text
+
+
+def predict_english(img, model_name="naver-clova-ix/donut-base-finetuned-cord-v2"):
+    processor = DonutProcessor.from_pretrained(model_name)
+    model = VisionEncoderDecoderModel.from_pretrained(model_name)
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+
+    task_prompt = "<s_cord-v2>"
+    decoder_input_ids = processor.tokenizer(
+        task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+
+    image = img.convert("RGB")
+
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+
+    outputs = model.generate(
+        pixel_values.to(device),
+        decoder_input_ids=decoder_input_ids.to(device),
+        max_length=model.decoder.config.max_position_embeddings,
+        early_stopping=True,
+        pad_token_id=processor.tokenizer.pad_token_id,
+        eos_token_id=processor.tokenizer.eos_token_id,
+        use_cache=True,
+        num_beams=1,
+        bad_words_ids=[[processor.tokenizer.unk_token_id]],
+        return_dict_in_generate=True,
+    )
+
+    sequence = processor.batch_decode(outputs.sequences)[0]
+    sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(
+        processor.tokenizer.pad_token, "")
+    sequence = re.sub(r"<.*?>", "", sequence).strip()
+    return sequence
+
+
+def predict_tesseract(img):
+    text = pytesseract.image_to_string(Image.open(img))
+    return text
+
+
+st.set_option('deprecation.showfileUploaderEncoding', False)
+
+st.set_page_config(
+    page_title="Ex-stream-ly Cool App",
+    page_icon="🖊️",
+    layout="wide",
+    initial_sidebar_state="expanded",
+    menu_items={
+        'Get Help': 'https://www.extremelycoolapp.com/help',
+        'Report a bug': "https://www.extremelycoolapp.com/bug",
+        'About': "# This is a header. This is an *extremely* cool app!"
+    }
+)
+
+# Upload an image and set some options for demo purposes
+st.header("Qalam: A Multilingual OCR System")
+img_file = st.sidebar.file_uploader(label='Upload a file', type=['png', 'jpg'])
+realtime_update = st.sidebar.checkbox(label="Update in Real Time", value=True)
+# box_color = st.sidebar.color_picker(label="Box Color", value='#0000FF')
+aspect_choice = st.sidebar.radio(label="Aspect Ratio", options=[
+                                 "Free"])
+aspect_dict = {
+    "Free": None
+}
+aspect_ratio = aspect_dict[aspect_choice]
+Lng = st.sidebar.selectbox(label="Language", options=[
+    "Arabic", "English", "French", "Korean", "Chinese"])
+
+Models = {
+    "Arabic": "Qalam",
+    "English": "Donut",
+    "French": "Tesseract",
+    "Korean": "Donut",
+    "Chinese": "Donut"
+}
+
+st.sidebar.write("# Model: ", Models[Lng])
+
+if img_file:
+    img = Image.open(img_file)
+    if not realtime_update:
+        st.write("Double click to save crop")
+
+    col1, col2 = st.columns(2)
+    with col1:
+        st.header("Select Input Image")
+    # Get a cropped image from the frontend
+        cropped_img = st_cropper(
+            img,
+            realtime_update=realtime_update,
+            box_color="#FF0000",
+            aspect_ratio=aspect_ratio,
+            should_resize_image=True,
+        )
+
+    with col2:
+        # Manipulate cropped image at will
+        st.header("Output Image")
+        # _ = cropped_img.thumbnail((150, 150))
+        st.image(cropped_img)
+        button = st.button("Run OCR")
+        if button:
+            if Lng == "Arabic":
+                st.write("# Arabic Text:")
+                st.write(predict_arabic(cropped_img))
+            elif Lng == "English":
+                st.write("# English Text:")
+                st.write(predict_english(cropped_img))
+            elif Lng == "French":
+                st.write("# French Text:")
+                st.write(predict_tesseract(cropped_img))
+            elif Lng == "Korean":
+                st.write("# Korean Text:")
+                st.write(predict_english(cropped_img))
+            elif Lng == "Chinese":
+                st.write("# Chinese Text:")
+                st.write(predict_english(cropped_img))

requirements.txt

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+streamlit
+streamlit-cropper
+transformers
+torch
+pytesseract
+re
+pillow