"""
Gradio interface for TextLens OCR application.
"""
import gradio as gr
from .styles import get_custom_css
from .handlers import extract_text_from_image, get_model_status
def create_interface():
"""Create and configure the Gradio interface."""
with gr.Blocks(css=get_custom_css(), title="TextLens - AI OCR", theme=gr.themes.Soft()) as interface:
# Header
with gr.Row():
gr.HTML("""
""")
# Model status
with gr.Row():
with gr.Column():
model_status = gr.Markdown(
value=get_model_status(),
elem_classes=["status-box"]
)
refresh_status_btn = gr.Button("š Refresh Status", size="sm")
# Main interface
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### š Upload Image", elem_classes=["markdown-text"])
image_input = gr.Image(
label="Drop image here or click to upload",
type="pil",
sources=["upload", "webcam", "clipboard"],
elem_classes=["upload-box"]
)
extract_btn = gr.Button(
"š Extract Text",
variant="primary",
size="lg"
)
gr.Markdown("### š Try with examples:", elem_classes=["markdown-text"])
gr.Markdown("""
**Try uploading an image with text:**
⢠Screenshots of documents
⢠Photos of signs or billboards
⢠Handwritten notes
⢠Menu cards or receipts
⢠Book pages or articles
""", elem_classes=["markdown-text"])
with gr.Column(scale=1):
gr.Markdown("### š Extracted Text", elem_classes=["markdown-text"])
text_output = gr.Textbox(
label="Text Output",
lines=15,
max_lines=25,
placeholder="Extracted text will appear here...\n\n⢠Upload an image to get started\n⢠The first run may take a few minutes to download the model\n⢠Subsequent runs will be much faster",
show_copy_button=True
)
gr.Markdown("""
**š” Tips:**
- Higher resolution images generally give better results
- Ensure text is clearly visible and not blurry
- The model works best with printed text but also supports handwriting
- First-time model loading may take 2-3 minutes
""",
elem_classes=["tips-section"]
)
# # Usage instructions
# with gr.Row():
# gr.Markdown("""
# ### š§ How to Use
# 1. **Upload an Image**: Drag and drop, use webcam, or paste from clipboard
# 2. **Extract Text**: Click the "Extract Text" button or text extraction will start automatically
# 3. **Copy Results**: Use the copy button to copy extracted text
# 4. **Try Different Images**: Upload multiple images to test various scenarios
# ### ā” Features
# - **Vision-Language Model**: Uses Microsoft Florence-2 for accurate text recognition
# - **Multiple Input Methods**: Upload files, use webcam, or paste from clipboard
# - **Auto-Processing**: Text extraction starts automatically when you upload an image
# - **GPU Acceleration**: Automatically uses GPU if available for faster processing
# - **Copy Functionality**: Easy one-click copying of extracted text
# """, elem_classes=["instructions-section"])
# Event handlers
image_input.upload(
fn=extract_text_from_image,
inputs=image_input,
outputs=text_output
)
extract_btn.click(
fn=extract_text_from_image,
inputs=image_input,
outputs=text_output
)
refresh_status_btn.click(
fn=get_model_status,
outputs=model_status
)
return interface