examples

app.py

@@ -140,7 +140,7 @@ To train or fine-tune a ColPali model, we need a dataset of image-text pairs whi
 To make the ColPali models work even better we might want a dataset of query/image document pairs related to our domain or task. 
 
 One way in which we might go about generating such a dataset is to use an VLM to generate synthetic queries for us. 
-This space uses the [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) to generate queries for a document, based on an input document image. 
+This space uses the [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) VLM model to generate queries for a document, based on an input document image. 
 
 
 This [blog post](https://danielvanstrien.xyz/posts/post-with-code/colpali/2024-09-23-generate_colpali_dataset.html) gives an overview of how you can use this kind of approach to generate a full dataset for fine-tuning ColPali models. 
@@ -149,11 +149,17 @@ If you want to convert a PDF(s) to a dataset of page images you can try out the
 
 """
 
+examples = [
+    "examples/Approche_no_13_1977.pdf_page_22.jpg",
+    "examples/SRCCL_Technical-Summary.pdf_page_7.jpg",
+]
+
 demo = gr.Interface(
     fn=generate_response,
     inputs=gr.Image(type="pil"),
     outputs=gr.Json(),
     title=title,
     description=description,
+    examples=examples,
 )
 demo.launch()