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import pytesseract
from PIL import Image
import pandas as pd
import os
import gradio as gr
import numpy as np
import gradio as gr
import re
import pandas as pd
def ocr_df_using_pytesseract(image):
#pytesseract.pytesseract.tesseract_cmd =r"C:\Users\amold\Desktop\Upwork\pdf to image and pytesseract\tesseact_exe\Tesseract-OCR\tesseract.exe"
pytesseract.pytesseract.tesseract_cmd = r'/usr/bin/tesseract'
#image = Image.open(example['image_path'])
width, height = image.size
# apply ocr to the image
ocr_df = pytesseract.image_to_data(image, output_type='data.frame')
float_cols = ocr_df.select_dtypes('float').columns
ocr_df = ocr_df.dropna().reset_index(drop=True)
ocr_df[float_cols] = ocr_df[float_cols].round(0).astype(int)
ocr_df = ocr_df.replace(r'^\s*$', np.nan, regex=True)
ocr_df = ocr_df.dropna().reset_index(drop=True)
ocr_df
ocr_df['X1']=ocr_df['left']
ocr_df['Y1']=ocr_df['top']
ocr_df['X2']= ocr_df['left'] + ocr_df['width']
ocr_df['Y2']= ocr_df['top'] + ocr_df['height']
return ocr_df
def image_to_text(image):
ocr_df= ocr_df_using_pytesseract(image)
grouped_text = ocr_df.groupby(['block_num', 'line_num'])['text'].agg(' '.join).reset_index()
# sort the text by line numbers within each block
grouped_text = grouped_text.sort_values(['block_num', 'line_num'])
# join the text by blocks and add newlines
result = ''
for i, row in grouped_text.iterrows():
if i > 0 and row['block_num'] != grouped_text.loc[i-1, 'block_num']:
result += '\n\n'
result += row['text'].rstrip() + '\n'
return result
def getting_extractions(image):
text= image_to_text(image)
item_pattern = r"(\d+)\s*of:(.*?)\$(\d+\.\d{2})"
# Extracting the matches using regex
item_matches = re.findall(item_pattern, text, re.DOTALL)
items = []
for match in item_matches:
quantity, description, price = match
quantity = int(quantity)
description = description.strip()
price = float(price)
item = {
"quantity": quantity,
"description": description,
"price": price
}
items.append(item)
# Creating a pandas DataFrame
df = pd.DataFrame(items, columns=["quantity", "description", "price"])
return df
demo = gr.Interface(fn=getting_extractions,
inputs= gr.Image(type="pil"),
outputs=["dataframe"],
title="Amazon_invoice_to_text",
description= "Upload invoice image here")
demo.launch(share=False) |