Create app.py

app.py

@@ -0,0 +1,48 @@
+import streamlit as st
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+import requests
+import torch
+
+def load_model():
+    processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten')
+    model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten')
+    return processor, model
+
+def recognize_text(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
+
+def main():
+    st.title("Handwritten Text Recognition with TrOCR")
+    
+    processor, model = load_model()
+    
+    uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+    image_url = st.text_input("Or enter an image URL:", "https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg")
+    
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file).convert("RGB")
+    elif image_url:
+        try:
+            image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
+        except Exception as e:
+            st.error(f"Error loading image: {e}")
+            return
+    else:
+        st.warning("Please upload an image or provide a URL.")
+        return
+    
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    if st.button("Recognize Text"):
+        with st.spinner("Processing..."):
+            text = recognize_text(image, processor, model)
+        
+        st.success("Recognized Text:")
+        st.write(text)
+
+if __name__ == "__main__":
+    main()