Create app.py

app.py

@@ -0,0 +1,169 @@
+from typing import Dict, Tuple
+import os
+import gradio as gr
+from docling.datamodel.base_models import InputFormat
+from docling.datamodel.pipeline_options import PdfPipelineOptions
+from docling.document_converter import DocumentConverter, PdfFormatOption
+from docling_core.types import DoclingDocument
+from docling.utils import model_downloader
+from docling.datamodel.pipeline_options import smolvlm_picture_description
+
+# Download models upon HF space initialization
+if os.getenv("IS_HF_SPACE"):
+    model_downloader.download_models()
+
+
+def parse_document(
+    file_path: str,
+    do_code_enrichment: bool,
+    do_formula_enrichment: bool,
+    do_picture_classification: bool,
+    do_picture_description: bool,
+) -> Tuple[DoclingDocument, str]:
+    yield None, f"Parsing document... ⏳"
+
+    pipeline_options = PdfPipelineOptions()
+    pipeline_options.do_code_enrichment = do_code_enrichment
+    pipeline_options.do_formula_enrichment = do_formula_enrichment
+    pipeline_options.generate_picture_images = do_picture_classification
+    pipeline_options.images_scale = 2
+    pipeline_options.do_picture_classification = do_picture_classification
+    pipeline_options.do_picture_description = do_picture_description
+    pipeline_options.picture_description_options = smolvlm_picture_description
+
+    print(f"Pipeline options defined: \n\t{pipeline_options}")
+    converter = DocumentConverter(
+        format_options={
+            InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
+        }
+    )
+
+    result = converter.convert(file_path)
+
+    yield result.document, "Done ✅"
+
+
+def to_html(docling_doc: DoclingDocument) -> str:
+    return docling_doc.export_to_html()
+
+
+def to_markdown(docling_doc: DoclingDocument) -> str:
+    return docling_doc.export_to_markdown()
+
+
+def to_json(docling_doc: DoclingDocument) -> Dict:
+    return docling_doc.export_to_dict()
+
+
+def to_text(docling_doc: DoclingDocument) -> str:
+    return docling_doc.export_to_text()
+
+
+def upload_file(file) -> str:
+    return file.name
+
+
+def setup_gradio_demo():
+    with gr.Blocks() as demo:
+        gr.Markdown(
+            """ # Docling - OCR: Parse documents, images, spreadsheets and more to markdown or other formats!
+            
+            Docling is very powerful tool, with lots of cool features and integrations to other AI frameworks (e.g. LlamaIndex, LangChain, and many more).
+
+            Model used for picture classification: [EfficientNet-B0 Document Image Classifier](https://huggingface.co/ds4sd/DocumentFigureClassifier)
+
+            Model used for picture description: [SmolVLM-256M-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-256M-Instruct)
+
+            To explore the full set of features of Docling visit: https://github.com/docling-project/docling
+            """
+        )
+
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("### 1) Upload")
+                file_output = gr.File(
+                    file_count="single",
+                    file_types=[
+                        ".pdf",
+                        ".docx",
+                        ".pptx",
+                        ".csv",
+                        ".md",
+                        ".png",
+                        ".jpg",
+                        ".tiff",
+                        ".bmp",
+                        ".html",
+                        ".xhtml",
+                        ".xlsx",
+                    ],
+                )
+
+            with gr.Column():
+                gr.Markdown("### 2) Configure engine & Parse")
+                code_understanding = gr.Checkbox(
+                    value=False, label="Enable Code understanding"
+                )
+                formula_enrichment = gr.Checkbox(
+                    value=False, label="Enable Formula understanding"
+                )
+                picture_classification = gr.Checkbox(
+                    value=False, label="Enable Picture classification"
+                )
+                picture_description = gr.Checkbox(
+                    value=False, label="Enable Picture description"
+                )
+                gr.Markdown(
+                    "_**Warning:** Enabling any of these features can potentially increase the processing time._"
+                )
+
+                parse_button = gr.Button("Parse document")
+                status = gr.Markdown()
+            with gr.Column():
+                gr.Markdown("### 3) Convert")
+
+                html_button = gr.Button("Convert to HTML")
+                markdown_button = gr.Button("Convert to markdown")
+                json_button = gr.Button("Convert to JSON")
+                text_button = gr.Button("Convert to text")
+
+        doc = gr.State()
+        output = gr.Text(label="Output")
+
+        parse_button.click(
+            fn=parse_document,
+            inputs=[
+                file_output,
+                code_understanding,
+                formula_enrichment,
+                picture_classification,
+                picture_description,
+            ],
+            outputs=[doc, status],
+        )
+        html_button.click(
+            fn=to_html,
+            inputs=doc,
+            outputs=output,
+        )
+        markdown_button.click(
+            fn=to_markdown,
+            inputs=doc,
+            outputs=output,
+        )
+        json_button.click(
+            fn=to_json,
+            inputs=doc,
+            outputs=output,
+        )
+        text_button.click(
+            fn=to_text,
+            inputs=doc,
+            outputs=output,
+        )
+
+    demo.launch()
+
+
+if __name__ == "__main__":
+    setup_gradio_demo()