Delete ocr_app.py

ocr_app.py

@@ -1,167 +0,0 @@
-import io
-
-import pandas as pd
-import streamlit as st
-from streamlit_drawable_canvas import st_canvas
-import hashlib
-import pypdfium2
-
-from texify.inference import batch_inference
-from texify.model.model import load_model
-from texify.model.processor import load_processor
-from texify.settings import settings
-import subprocess
-import re
-from PIL import Image
-
-MAX_WIDTH = 1000
-
-
-def replace_katex_invalid(string):
-    # KaTeX cannot render all LaTeX, so we need to replace some things
-    string = re.sub(r'\\tag\{.*?\}', '', string)
-    string = re.sub(r'\\Big\{(.*?)\}|\\big\{(.*?)\}', r'\1\2', string)
-    return string
-
-@st.cache_resource()
-def load_model_cached():
-    return load_model()
-
-
-@st.cache_resource()
-def load_processor_cached():
-    return load_processor()
-
-
-@st.cache_data()
-def infer_image(pil_image, bbox, temperature):
-    input_img = pil_image.crop(bbox)
-    model_output = batch_inference([input_img], model, processor, temperature=temperature)
-    return model_output[0]
-
-
-def open_pdf(pdf_file):
-    stream = io.BytesIO(pdf_file.getvalue())
-    return pypdfium2.PdfDocument(stream)
-
-
-@st.cache_data()
-def get_page_image(pdf_file, page_num, dpi=96):
-    doc = open_pdf(pdf_file)
-    renderer = doc.render(
-        pypdfium2.PdfBitmap.to_pil,
-        page_indices=[page_num - 1],
-        scale=dpi / 72,
-    )
-    png = list(renderer)[0]
-    png_image = png.convert("RGB")
-    return png_image
-
-
-@st.cache_data()
-def get_uploaded_image(in_file):
-    return Image.open(in_file).convert("RGB")
-
-
-@st.cache_data()
-def page_count(pdf_file):
-    doc = open_pdf(pdf_file)
-    return len(doc)
-
-
-def get_canvas_hash(pil_image):
-    return hashlib.md5(pil_image.tobytes()).hexdigest()
-
-
-@st.cache_data()
-def get_image_size(pil_image):
-    if pil_image is None:
-        return 800, 600
-    height, width = pil_image.height, pil_image.width
-    if width > MAX_WIDTH:
-        scale = MAX_WIDTH / width
-        height = int(height * scale)
-        width = MAX_WIDTH
-    return height, width
-
-
-st.set_page_config(layout="wide")
-
-top_message = """### Texify
-
-After the model loads, upload an image or a pdf, then draw a box around the equation or text you want to OCR by clicking and dragging. Texify will convert it to Markdown with LaTeX math on the right.
-
-If you have already cropped your image, select "OCR image" in the sidebar instead.
-"""
-
-st.markdown(top_message)
-col1, col2 = st.columns([.7, .3])
-
-model = load_model_cached()
-processor = load_processor_cached()
-
-in_file = st.sidebar.file_uploader("PDF file or image:", type=["pdf", "png", "jpg", "jpeg", "gif", "webp"])
-if in_file is None:
-    st.stop()
-
-filetype = in_file.type
-whole_image = False
-if "pdf" in filetype:
-    page_count = page_count(in_file)
-    page_number = st.sidebar.number_input(f"Page number out of {page_count}:", min_value=1, value=1, max_value=page_count)
-
-    pil_image = get_page_image(in_file, page_number)
-else:
-    pil_image = get_uploaded_image(in_file)
-    whole_image = st.sidebar.button("OCR image")
-
-temperature = st.sidebar.slider("Generation temperature:", min_value=0.0, max_value=1.0, value=0.0, step=0.05)
-
-canvas_hash = get_canvas_hash(pil_image) if pil_image else "canvas"
-
-with col1:
-    # Create a canvas component
-    canvas_result = st_canvas(
-        fill_color="rgba(255, 165, 0, 0.1)",  # Fixed fill color with some opacity
-        stroke_width=1,
-        stroke_color="#FFAA00",
-        background_color="#FFF",
-        background_image=pil_image,
-        update_streamlit=True,
-        height=get_image_size(pil_image)[0],
-        width=get_image_size(pil_image)[1],
-        drawing_mode="rect",
-        point_display_radius=0,
-        key=canvas_hash,
-    )
-
-if canvas_result.json_data is not None or whole_image:
-    objects = pd.json_normalize(canvas_result.json_data["objects"])  # need to convert obj to str because PyArrow
-    bbox_list = None
-    if objects.shape[0] > 0:
-        boxes = objects[objects["type"] == "rect"][["left", "top", "width", "height"]]
-        boxes["right"] = boxes["left"] + boxes["width"]
-        boxes["bottom"] = boxes["top"] + boxes["height"]
-        bbox_list = boxes[["left", "top", "right", "bottom"]].values.tolist()
-    if whole_image:
-        bbox_list = [(0, 0, pil_image.width, pil_image.height)]
-
-    if bbox_list:
-        with col2:
-            inferences = [infer_image(pil_image, bbox, temperature) for bbox in bbox_list]
-            for idx, inference in enumerate(reversed(inferences)):
-                st.markdown(f"### {len(inferences) - idx}")
-                katex_markdown = replace_katex_invalid(inference)
-                st.markdown(katex_markdown)
-                st.code(inference)
-                st.divider()
-
-with col2:
-    tips = """
-    ### Usage tips
-    - Don't make your boxes too small or too large.  See the examples and the video in the [README](https://github.com/vikParuchuri/texify) for more info.
-    - Texify is sensitive to how you draw the box around the text you want to OCR. If you get bad results, try selecting a slightly different box, or splitting the box into multiple.
-    - You can try changing the temperature value on the left if you don't get good results.  This controls how "creative" the model is.
-    - Sometimes KaTeX won't be able to render an equation (red error text), but it will still be valid LaTeX.  You can copy the LaTeX and render it elsewhere.
-    """
-    st.markdown(tips)