Update app.py

app.py

@@ -362,12 +362,12 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
                 formatted_output = gr.Markdown(label="Formatted Result (Result.Md)")
             
             model_choice = gr.Radio(
-                choices=["SmolDocling-256M-preview", "Nanonets-OCR-s", "MonkeyOCR-Recognition", "Typhoon-OCR-7B"],
+                choices=["Nanonets-OCR-s", "MonkeyOCR-Recognition", "SmolDocling-256M-preview", "Typhoon-OCR-7B"],
                 label="Select Model",
                 value="Nanonets-OCR-s"
             )
             
-            gr.Markdown("**Model Info 💻**")
+            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR2/discussions)")
             gr.Markdown("> [SmolDocling-256M](https://huggingface.co/ds4sd/SmolDocling-256M-preview): SmolDocling is a multimodal Image-Text-to-Text model designed for efficient document conversion. It retains Docling's most popular features while ensuring full compatibility with Docling through seamless support for DoclingDocuments.")
             gr.Markdown("> [Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s): nanonets-ocr-s is a powerful, state-of-the-art image-to-markdown ocr model that goes far beyond traditional text extraction. it transforms documents into structured markdown with intelligent content recognition and semantic tagging.")
             gr.Markdown("> [MonkeyOCR-Recognition](https://huggingface.co/echo840/MonkeyOCR): MonkeyOCR adopts a Structure-Recognition-Relation (SRR) triplet paradigm, which simplifies the multi-tool pipeline of modular approaches while avoiding the inefficiency of using large multimodal models for full-page document processing.")