Create app.py

app.py

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+# Streamlit app for extracting text from an image using the General OCR Theory (GOT) 2.0 model
+import streamlit as st
+from transformers import AutoTokenizer, AutoModel
+import torch
+from PIL import Image
+import requests
+
+# Load the pre-trained GOT OCR 2.0 model and tokenizer
+@st.cache_resource(show_spinner=True)
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
+    model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
+    return tokenizer, model.eval().cuda()
+
+# Streamlit interface
+st.title("OCR Application using General OCR Theory (GOT) 2.0")
+st.write("Upload an image to extract text using the GOT OCR 2.0 model.")
+
+# File upload handler
+uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+    
+    # Load model
+    tokenizer, model = load_model()
+
+    # Load the image into the model
+    with open(uploaded_file.name, 'wb') as f:
+        f.write(uploaded_file.getbuffer())
+    
+    image_file = uploaded_file.name
+
+    # Perform OCR
+    with st.spinner("Extracting text..."):
+        res = model.chat(tokenizer, image_file, ocr_type='ocr')
+
+    # Display the result
+    st.write("Extracted Text:")
+    st.text(res)