Update app.py

app.py

@@ -8,23 +8,23 @@ from transformers import TrOCRProcessor, VisionEncoderDecoderModel
 file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 
 # Display the uploaded image
-if file is not None:
+#if file is not None:
     
     # Read the image data
-    image = Image.open(file)
+    #image = Image.open(file)
     #image=image.convert("RGB")
 
     #resized_image = image.resize((384, 384))
 
-    processor = TrOCRProcessor.from_pretrained('microsoft/trocr-small-handwritten')
-    model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-small-handwritten')
-    pixel_values = processor(images=resized_image, return_tensors="pt").pixel_values
-    generated_ids = model.generate(pixel_values)
-    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    #processor = TrOCRProcessor.from_pretrained('microsoft/trocr-small-handwritten')
+    #model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-small-handwritten')
+    #pixel_values = processor(images=resized_image, return_tensors="pt").pixel_values
+    #generated_ids = model.generate(pixel_values)
+    #generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
     
     # Display the image
-    st.image(image, caption='Uploaded Image', use_column_width=True)
+    #st.image(image, caption='Uploaded Image', use_column_width=True)
     
     # Print the shape of the image
-    st.write(f"Text: {generated_text}")
+    #st.write(f"Text: {generated_text}")