Show inference time for both models

Added an extra output that shows inference time for both models. I removed the `@GPU` annotation on the 'detect' function because it was causing a ~400ms hit to the first model's inference time (presumably because of ZeroGPU initialization that the second model is able to take advantage of). With the annotation removed both models get the performance hit resulting in a more apples-to-apples comparison.

Here's what it looks like:


app.py

@@ -2,6 +2,7 @@ import random
 import requests
 import json
 import ast
+import time
 
 import matplotlib.pyplot as plt
 from PIL import Image, ImageDraw, ImageFont
@@ -156,6 +157,7 @@ def detect_qwen(image, prompt):
         }
     ]
 
+    t0 = time.perf_counter()
     text = processor_qwen.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
     image_inputs, video_inputs = process_vision_info(messages)
     inputs = processor_qwen(
@@ -173,37 +175,41 @@ def detect_qwen(image, prompt):
     output_text = processor_qwen.batch_decode(
         generated_ids_trimmed, do_sample=True, skip_special_tokens=True, clean_up_tokenization_spaces=False
     )[0]
+    elapsed_ms = (time.perf_counter() - t0) * 1_000
 
     input_height = inputs['image_grid_thw'][0][1] * 14
     input_width = inputs['image_grid_thw'][0][2] * 14
 
     annotated_image = create_annotated_image(image, output_text, input_height, input_width)
 
-    return annotated_image, output_text
+    time_taken = f"**Inference time ({model_qwen_name}):** {elapsed_ms:.0f} ms"
+    return annotated_image, output_text, time_taken
 
 
 @GPU
 def detect_moondream(image, prompt, category_input):
+    t0 = time.perf_counter()
     if category_input in ["Object Detection", "Visual Grounding + Object Detection"]:
         output_text = model_moondream.detect(image=image, object=prompt)
     elif category_input == "Visual Grounding + Keypoint Detection":
         output_text = model_moondream.point(image=image, object=prompt)
     else:
         output_text = model_moondream.query(image=image, question=prompt, reasoning=True)
+    elapsed_ms = (time.perf_counter() - t0) * 1_000
 
     annotated_image = create_annotated_image_normalized(image=image, json_data=output_text, label="object", explicit_color=None)
-    
-    return annotated_image, output_text
 
-@GPU
+    time_taken = f"**Inference time ({model_moondream_name}):** {elapsed_ms:.0f} ms"
+    return annotated_image, output_text, time_taken
+
 def detect(image, prompt_model_1, prompt_model_2, category_input):
     STANDARD_SIZE = (1024, 1024)
     image.thumbnail(STANDARD_SIZE)
     
-    annotated_image_model_1, output_text_model_1 = detect_qwen(image, prompt_model_1)
-    annotated_image_model_2, output_text_model_2 = detect_moondream(image, prompt_model_2, category_input)
+    annotated_image_model_1, output_text_model_1, timing_1 = detect_qwen(image, prompt_model_1)
+    annotated_image_model_2, output_text_model_2, timing_2 = detect_moondream(image, prompt_model_2, category_input)
 
-    return annotated_image_model_1, output_text_model_1, annotated_image_model_2, output_text_model_2
+    return annotated_image_model_1, output_text_model_1, timing_1, annotated_image_model_2, output_text_model_2, timing_2
 
 css_hide_share = """
 button#gradio-share-link-button-0 {
@@ -253,10 +259,12 @@ with gr.Blocks(theme=Ocean(), css=css_hide_share) as demo:
         with gr.Column(scale=1):
             output_image_model_1 = gr.Image(type="pil", label=f"Annotated image for {model_qwen_name}", height=400)
             output_textbox_model_1 = gr.Textbox(label=f"Model response for {model_qwen_name}", lines=10)
+            output_time_model_1 = gr.Markdown()
         
         with gr.Column(scale=1):
             output_image_model_2 = gr.Image(type="pil", label=f"Annotated image for {model_moondream_name}", height=400)
             output_textbox_model_2 = gr.Textbox(label=f"Model response for {model_moondream_name}", lines=10)
+            output_time_model_2 = gr.Markdown()
 
     gr.Markdown("### Examples")
     example_prompts = [
@@ -276,8 +284,15 @@ with gr.Blocks(theme=Ocean(), css=css_hide_share) as demo:
         label="Click an example to populate the input"
     )
 
-    generate_btn.click(fn=detect, inputs=[image_input, prompt_input_model_1, prompt_input_model_2, category_input], outputs=[output_image_model_1, output_textbox_model_1, output_image_model_2, output_textbox_model_2])
-
+    generate_btn.click(
+        fn=detect,
+        inputs=[image_input, prompt_input_model_1, prompt_input_model_2, category_input],
+        outputs=[
+            output_image_model_1, output_textbox_model_1, output_time_model_1,
+            output_image_model_2, output_textbox_model_2, output_time_model_2
+        ]
+    )
+    
 if __name__ == "__main__":
     demo.launch()