Create app.py

app.py

@@ -0,0 +1,40 @@
+import streamlit as st
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+
+# Load the model and processor
+@st.cache_resource
+def load_model():
+    processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
+    model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
+    return processor, model
+
+processor, model = load_model()
+
+# Streamlit app
+st.title("OCR API Service")
+
+uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    try:
+        # Load and display the uploaded image
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Perform OCR
+        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]
+
+        # Display extracted text
+        st.subheader("Extracted Text:")
+        st.text(generated_text)
+
+        # Simulate API-like JSON response
+        json_response = {"extracted_text": generated_text}
+        st.write("API Response:")
+        st.json(json_response)
+
+    except Exception as e:
+        st.error(f"An error occurred: {e}")