Try with pollen-vision demo app now that the image builds properly.

app.py

@@ -1,7 +1,118 @@
+# import gradio as gr
+
+# def greet(name):
+#     return "Hello " + name + "!!"
+
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface.launch()
+
+"""
+Gradio app for pollen-vision
+
+This script creates a Gradio app for pollen-vision. The app allows users to perform object detection and object segmentation using the OWL-ViT and MobileSAM models.
+"""
+
+from datasets import load_dataset
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+import numpy as np
+import numpy.typing as npt
+from typing import Any, Dict, List
+
+from pollen_vision.vision_models.object_detection import OwlVitWrapper
+from pollen_vision.vision_models.object_segmentation import MobileSamWrapper
+from pollen_vision.vision_models.utils import Annotator, get_bboxes
+
+
+owl_vit = OwlVitWrapper()
+mobile_sam = MobileSamWrapper()
+annotator = Annotator()
+
+
+def object_detection(
+    img: npt.NDArray[np.uint8], text_queries: List[str], score_threshold: float
+) -> List[Dict[str, Any]]:
+    predictions: List[Dict[str, Any]] = owl_vit.infer(
+        im=img, candidate_labels=text_queries, detection_threshold=score_threshold
+    )
+    return predictions
+
+
+def object_segmentation(
+    img: npt.NDArray[np.uint8], object_detection_predictions: List[Dict[str, Any]]
+) -> List[npt.NDArray[np.uint8]]:
+    bboxes = get_bboxes(predictions=object_detection_predictions)
+    masks: List[npt.NDArray[np.uint8]] = mobile_sam.infer(im=img, bboxes=bboxes)
+    return masks
+
+
+def query(
+    task: str,
+    img: npt.NDArray[np.uint8],
+    text_queries: List[str],
+    score_threshold: float,
+) -> npt.NDArray[np.uint8]:
+    object_detection_predictions = object_detection(
+        img=img, text_queries=text_queries, score_threshold=score_threshold
+    )
+
+    if task == "Object detection + segmentation (OWL-ViT + MobileSAM)":
+        masks = object_segmentation(
+            img=img, object_detection_predictions=object_detection_predictions
+        )
+        img = annotator.annotate(
+            im=img, detection_predictions=object_detection_predictions, masks=masks
+        )
+        return img
+
+    img = annotator.annotate(im=img, detection_predictions=object_detection_predictions)
+    return img
+
+
+description = """
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam nec purus et nunc tincidunt tincidunt.
+"""
+
+demo_inputs = [
+    gr.Dropdown(
+        [
+            "Object detection (OWL-ViT)",
+            "Object detection + segmentation (OWL-ViT + MobileSAM)",
+        ],
+        label="Choose a task",
+        value="Object detection (OWL-ViT)",
+    ),
+    gr.Image(),
+    "text",
+    gr.Slider(0, 1, value=0.1),
+]
+
+rdt_dataset = load_dataset("pollen-robotics/reachy-doing-things", split="train")
+
+img_kitchen_detection = rdt_dataset[11]["image"]
+img_kitchen_segmentation = rdt_dataset[12]["image"]
+
+demo_examples = [
+    [
+        "Object detection (OWL-ViT)",
+        img_kitchen_detection,
+        ["kettle", "black mug", "sink", "blue mug", "sponge", "bag of chips"],
+        0.15,
+    ],
+    [
+        "Object detection + segmentation (OWL-ViT + MobileSAM)",
+        img_kitchen_segmentation,
+        ["blue mug", "paper cup", "kettle", "sponge"],
+        0.12,
+    ],
+]
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+demo = gr.Interface(
+    fn=query,
+    inputs=demo_inputs,
+    outputs="image",
+    title="pollen-vision",
+    description=description,
+    examples=demo_examples,
+)
+demo.launch()