Update app.py

app.py

@@ -2,49 +2,32 @@ import cv2
 import easyocr
 import gradio as gr
 import base64
-import json
 
-def text_extraction(image):
-    # Convert base64 image to OpenCV format
-    image = base64.b64decode(image.split(",")[1])
-    nparr = np.frombuffer(image, np.uint8)
-    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-    # Instance text detector
-    reader = easyocr.Reader(['en'], gpu=False)
+# Instance text detector
+reader = easyocr.Reader(['en'], gpu=False)
 
-    # Detect text on image
-    text_ = reader.readtext(img)
+def text_extraction(image):
+    text_ = reader.readtext(image)
 
     threshold = 0.25
-    # Draw bbox and text
+    # draw bbox and text
     for t_, t in enumerate(text_):
         bbox, text, score = t
 
         if score > threshold:
-            cv2.rectangle(img, tuple(map(int, bbox[0])), tuple(map(int, bbox[2])), (255, 0, 0), 2)
+            cv2.rectangle(image, tuple(map(int, bbox[0])), tuple(map(int, bbox[2])), (255, 0, 0), 2)
 
-    # Encode image to base64
-    retval, buffer = cv2.imencode('.jpg', img)
+    retval, buffer = cv2.imencode('.jpg', image)
     img_base64 = base64.b64encode(buffer).decode('utf-8')
 
-    # Create JSON response
-    response_json = {
-        'annotated_image_base64': img_base64,
-        'text_data': text_
-    }
-
-    # Convert the dictionary to a JSON string
-    response_json_str = json.dumps(response_json, default=str)
-
-    return response_json_str
+    return img_base64
 
 # Define Gradio interface
 iface = gr.Interface(
     fn=text_extraction,
     inputs=gr.Image(),
-    outputs=["image", "json"]
+    outputs=["image"]
 )
 
 # Launch the Gradio interface
-iface.launch()
+iface.launch()