Update app.py

app.py

@@ -1,22 +1,74 @@
-from flair.data import Sentence
-from flair.models import SequenceTagger
-import streamlit as st
+# from flair.data import Sentence
+# from flair.models import SequenceTagger
+# import streamlit as st
 
-# load tagger
-tagger = SequenceTagger.load("flair/ner-english-large")
+# # load tagger
+# tagger = SequenceTagger.load("flair/ner-english-large")
 
-# make example sentence
-text=st.text_area("Enter the text to detect it's named entities")
-sentence = Sentence(text)
+# # make example sentence
+# text=st.text_area("Enter the text to detect it's named entities")
+# sentence = Sentence(text)
 
-# predict NER tags
-tagger.predict(sentence)
+# # predict NER tags
+# tagger.predict(sentence)
 
-# print sentence
-print(sentence)
+# # print sentence
+# print(sentence)
 
-# print predicted NER spans
-print('The following NER tags are found:')
-# iterate over entities and printx
-for entity in sentence.get_spans('ner'):
-    print(entity)
+# # print predicted NER spans
+# print('The following NER tags are found:')
+# # iterate over entities and printx
+# for entity in sentence.get_spans('ner'):
+#     print(entity)
+
+
+
+import easyocr
+import cv2
+import requests
+import re
+from PIL import Image
+
+## Image uploading function ##
+def image_upload_and_ocr(reader):
+    uploaded_file=st.file_uploader(label=':red[**please upload a busines card** :sunglasses:]',type=['jpeg','jpg','png','webp'])
+    if uploaded_file is not None:
+        image=Image.open(uploaded_file)
+        image=image.resize((640,480))
+        result = reader.readtext(image)
+        result2=result
+        texts = [item[1] for item in result]
+        result=' '.join(texts)
+    return result,result2
+
+
+        
+    ###  DRAWING DETECTION FUNCTION  ###
+def drawing_detection(image):
+    # Draw bounding boxes around the detected text regions
+    for detection in image:
+        # Extract the bounding box coordinates
+        points = detection[0]  # List of points defining the bounding box
+        x1, y1 = int(points[0][0]), int(points[0][1])  # Top-left corner
+        x2, y2 = int(points[2][0]), int(points[2][1])  # Bottom-right corner
+        
+        # Draw the bounding box
+        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+    
+        # Add the detected text
+        text = detection[1]
+        cv2.putText(image, text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+        st.image(image,caption='Detected text on the card ',width=710)
+    return image
+
+# Load the EasyOCR reader
+reader = easyocr.Reader(['en'])
+
+st.title("_Business_ card data extractor using opencv and streamlit :sunglasses:")
+result,result2=image_upload_and_ocr(reader)
+darwing_image=drawing_detection(result2)
+
+
+
+    
+