Update app.py

app.py

@@ -1,11 +1,10 @@
 #run the app
 #python -m streamlit run d:/NSFW/Project/test1.py
 import torch
-import tensorflow as tf
 from transformers import BertTokenizer, BertForSequenceClassification
 import math, keras_ocr
 # Initialize pipeline
-pipeline = keras_ocr.pipeline.Pipeline()
+pipeline = None
 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
 model_2 = BertForSequenceClassification.from_pretrained("CustomModel")
 
@@ -70,14 +69,19 @@ st.title("NSFW Content Detector")
 # File uploader widget
 uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
 
+def initialize():
+    global pipeline
+    if pipeline==None:
+        pipeline=keras_ocr.pipeline.Pipeline()
+
 if uploaded_file is not None:
     st.image(uploaded_file, caption='Uploaded Image', width=200)
     #st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
-
-# Read in image
+    initialize()
+    # Read in image
     read_image = keras_ocr.tools.read(uploaded_file)
 
-# prediction_groups is a list of (word, box) tuples
+    # prediction_groups is a list of (word, box) tuples
     prediction_groups = pipeline.recognize([read_image])
     predictions = prediction_groups[0] # extract text list
     predictions = get_distance(predictions)
@@ -111,5 +115,4 @@ if uploaded_file is not None:
     else:
         print('Not safe')
         st.write('Not Safe for Work')
-    pipeline = keras_ocr.pipeline.Pipeline()