Update app.py

app.py

@@ -55,35 +55,29 @@ for model in models:
       model["cfg"].MODEL.DEVICE = "cpu"
 
 
-def inference(image_url, image, min_score, model_name):
-    if image_url:
-        r = requests.get(image_url)
-        if r:
-            im = np.frombuffer(r.content, dtype="uint8")
-            im = cv2.imdecode(im, cv2.IMREAD_COLOR_BGR2RGB)
-    else:
+def inference(images, min_score, model_name):
+    results = []
+    for image in images:
         # Model expect BGR!
-        im = image[:,:,::-1]
+        im = image[:, :, ::-1]
 
-    model_id = model_name_to_id[model_name]
+        model_id = model_name_to_id[model_name]
 
-    models[model_id]["cfg"].MODEL.ROI_HEADS.SCORE_THRESH_TEST = min_score
-    predictor = DefaultPredictor(models[model_id]["cfg"])
+        models[model_id]["cfg"].MODEL.ROI_HEADS.SCORE_THRESH_TEST = min_score
+        predictor = DefaultPredictor(models[model_id]["cfg"])
 
-    outputs = predictor(im)
+        outputs = predictor(im)
 
-    v = Visualizer(im, models[model_id]["metadata"], scale=1.2)
-    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-
-    return out.get_image()
+        v = Visualizer(im, models[model_id]["metadata"], scale=1.2)
+        out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
 
+        results.append(out.get_image())
+    return results
 
 title = "# DBMDZ Detectron2 Model Demo"
 description = """
 This demo introduces an interactive playground for our trained Detectron2 model.
-
 Currently, two models are supported that were trained on manually annotated segments from digitized books:
-
 * [Version 1 (2-class)](https://huggingface.co/dbmdz/detectron2-model): This model can detect *Illustration* or *Illumination* segments on a given page.
 * [Version 2 (4-class)](https://huggingface.co/dbmdz/detectron2-v2-model): This model is more powerful and can detect *Illustration*, *Stamp*, *Initial* or *Other* segments on a given page.
 """
@@ -93,21 +87,18 @@ with gr.Blocks() as demo:
     gr.Markdown(title)
     gr.Markdown(description)
     
-    with gr.Tab("From URL"):
-        url_input = gr.Textbox(label="Image URL", placeholder="https://api.digitale-sammlungen.de/iiif/image/v2/bsb10483966_00008/full/500,/0/default.jpg")
-    
     with gr.Tab("From Image"):
-        image_input = gr.Image(type="numpy", label="Input Image")
+        image_input = gr.Gallery(label="Input Images", type="numpy")
 
     min_score = gr.Slider(minimum=0.0, maximum=1.0, value=0.5, label="Minimum score")
 
     model_name = gr.Radio(choices=[model["name"] for model in models], value=models[0]["name"], label="Select Detectron2 model")
 
-    output_image = gr.Image(type="pil", label="Output")
+    output_gallery = gr.Gallery(label="Output Images")
 
     inference_button = gr.Button("Submit")
     
-    inference_button.click(fn=inference, inputs=[url_input, image_input, min_score, model_name], outputs=output_image)
+    inference_button.click(fn=inference, inputs=[image_input, min_score, model_name], outputs=output_gallery)
 
     gr.Markdown(footer)