Create app.py

app.py

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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:
+    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=image_to_text, 
+                    inputs= gr.Image(type="pil"), 
+                    outputs=["dataframe"],
+                    title="Amazon_invoice_to_text",
+                    description= "Upload invoice image here")
+demo.launch(share=False)