reverting to initial code

app.py

@@ -1,71 +1,64 @@
 import spaces
-from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
+from transformers import Owlv2Processor, Owlv2ForObjectDetection, AutoProcessor, AutoModelForZeroShotObjectDetection
 import torch
 import gradio as gr
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 dino_processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-base")
-dino_model = AutoModelForZeroShotObjectDetection.from_pretrained("IDEA-Research/grounding-dino-base").to(device)
-
-def generate_colors(labels):
-    import random
-    random.seed(42)
-    colors = {}
-    for label in labels:
-        colors[label] = "#{:06x}".format(random.randint(0, 0xFFFFFF))
-    return colors
+dino_model = AutoModelForZeroShotObjectDetection.from_pretrained("IDEA-Research/grounding-dino-base").to("cuda")
 
 @spaces.GPU
 def infer(img, text_queries, score_threshold, model):
-    if model == "dino":
-        queries = ""
-        for query in text_queries:
-            queries += f"{query}. "
+  
+  if model == "dino":
+    queries=""
+    for query in text_queries:
+      queries += f"{query}. "
+
+    width, height = img.shape[:2]
 
-        height, width = img.shape[:2]
-        target_sizes = [(height, width)]
-        inputs = dino_processor(text=queries, images=img, return_tensors="pt").to(device)
+    target_sizes=[(width, height)]
+    inputs = dino_processor(text=queries, images=img, return_tensors="pt").to(device)
 
-        with torch.no_grad():
-            outputs = dino_model(**inputs)
-            outputs.logits = outputs.logits.cpu()
-            outputs.pred_boxes = outputs.pred_boxes.cpu()
-            results = dino_processor.post_process_grounded_object_detection(outputs=outputs, input_ids=inputs.input_ids,
-                                                                            box_threshold=score_threshold,
-                                                                            target_sizes=target_sizes)
+    with torch.no_grad():
+      outputs = dino_model(**inputs)
+      outputs.logits = outputs.logits.cpu()
+      outputs.pred_boxes = outputs.pred_boxes.cpu()
+      results = dino_processor.post_process_grounded_object_detection(outputs=outputs, input_ids=inputs.input_ids,
+                                                                    box_threshold=score_threshold,
+                                                                    target_sizes=target_sizes)
       
-        boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
-        result_labels = []
+  boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
+  result_labels = []
 
-        for box, score, label in zip(boxes, scores, labels):
-            box = [int(i) for i in box.tolist()]
-            if score < score_threshold:
-                continue
+  for box, score, label in zip(boxes, scores, labels):
+      box = [int(i) for i in box.tolist()]
+      if score < score_threshold:
+          continue
 
-            # Only include the labels that are part of the input text_queries
-            if model == "dino" and label in text_queries:
-                result_labels.append((box, label))
-        return result_labels
+      if model == "dino":
+        if label != "":
+            result_labels.append((box, label))
+  return result_labels
 
 def query_image(img, text_queries, dino_threshold):
-    text_queries = [query.strip() for query in text_queries.split(",")]
+    text_queries = text_queries
+    text_queries = text_queries.split(",")
     dino_output = infer(img, text_queries, dino_threshold, "dino")
-    annotations = []
-    for box, label in dino_output:
-        annotations.append({"label": label, "coordinates": {"x": box[0], "y": box[1], "width": box[2] - box[0], "height": box[3] - box[1]}})
-    
-    colors = generate_colors(text_queries)
-    return (img, {"boxes": annotations, "colors": colors})
+
+
+    return (img, dino_output)
+
 
 dino_threshold = gr.Slider(0, 1, value=0.12, label="Grounding DINO Threshold")
 dino_output = gr.AnnotatedImage(label="Grounding DINO Output")
 demo = gr.Interface(
     query_image,
     inputs=[gr.Image(label="Input Image"), gr.Textbox(label="Candidate Labels"), dino_threshold],
-    outputs=[dino_output],
+    outputs=[ dino_output],
     title="OWLv2 ⚔ Grounding DINO",
-    description="Evaluate state-of-the-art [Grounding DINO](https://huggingface.co/IDEA-Research/grounding-dino-base) zero-shot object detection models. Simply enter an image and the objects you want to find with comma, or try one of the examples. Play with the threshold to filter out low confidence predictions in the model.",
+    description="Evaluate  state-of-the-art [Grounding DINO](https://huggingface.co/IDEA-Research/grounding-dino-base) zero-shot object detection models. Simply enter an image and the objects you want to find with comma, or try one of the examples. Play with the threshold to filter out low confidence predictions in the model.",
     examples=[["./warthog.jpg", "zebra, warthog", 0.16], ["./zebra.jpg", "zebra, lion", 0.16]]
 )
 demo.launch(debug=True)