Update app.py

app.py

@@ -0,0 +1,50 @@
+import streamlit as st
+from PIL import Image
+import requests
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+import torch
+
+# Load pre-trained TrOCR model and processor
+processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
+model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
+
+# Function to perform OCR and extract text
+def extract_text_from_image(image):
+    # Preprocess the image
+    pixel_values = processor(images=image, return_tensors="pt").pixel_values
+
+    # Ensure the model is in evaluation mode
+    model.eval()
+
+    # Perform OCR
+    with torch.no_grad():
+        generated_ids = model.generate(pixel_values)
+    
+    # Decode the generated IDs to text
+    text = processor.decode(generated_ids[0], skip_special_tokens=True)
+    return text
+
+# Streamlit UI
+st.title("OCR Text Extraction from Image")
+
+st.write("""
+Upload an image containing text, and this app will extract and display the text from the image using the powerful TrOCR model!
+""")
+
+# File uploader to upload the image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Open the uploaded image
+    image = Image.open(uploaded_file)
+
+    # Display the uploaded image
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Button to extract text
+    if st.button("Extract Text"):
+        with st.spinner('Extracting text...'):
+            extracted_text = extract_text_from_image(image)
+            st.subheader("Extracted Text:")
+            st.write(extracted_text)
+