Create app.py

app.py

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+import streamlit as st
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+import requests
+
+def load_model():
+    processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-printed')
+    model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-printed')
+    return processor, model
+
+def process_image(image, processor, model):
+    pixel_values = processor(images=image, return_tensors="pt").pixel_values
+    generated_ids = model.generate(pixel_values)
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return generated_text
+
+st.title("Print OCR with TrOCR")
+
+# Load model and processor
+processor, model = load_model()
+
+# File uploader
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    with st.spinner("Extracting text..."):
+        extracted_text = process_image(image, processor, model)
+    
+    st.subheader("Extracted Text:")
+    st.write(extracted_text)
+
+# Example URL processing
+st.write("Or try with an example image:")
+default_url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"
+if st.button("Process Example Image"):
+    image = Image.open(requests.get(default_url, stream=True).raw).convert("RGB")
+    st.image(image, caption="Example Image", use_column_width=True)
+    with st.spinner("Extracting text..."):
+        extracted_text = process_image(image, processor, model)
+    st.subheader("Extracted Text:")
+    st.write(extracted_text)