remove time

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import pandas as pd
 import json
-import time
 from typing import Tuple
 from PIL import Image
 import torch
@@ -14,7 +13,6 @@ from models import (
 )
 from rembg import remove
 from io import BytesIO
-from time import time
 
 # Load environment variables (optional for local dev; Spaces use web UI for env vars)
 if os.path.exists('.env'):
@@ -117,14 +115,12 @@ def run_inference(image_tensor: torch.Tensor, description: str, category: str, m
     device = model_components['device']
     
     pil_img = transforms.ToPILImage()(image_tensor.squeeze(0).cpu())
-    start_time = time.time()
     
     results = get_predicated_values(
         model, category, pil_img, description,
         image_processor, bert_tokenizer, roberta_tokenizer, device
     )
     
-    end_time = time.time()
     
     total_attributes = len([a for a in results if a["value"] and a["value"] != "N/A"])
     avg_confidence = np.mean([a["confidence"] for a in results if a["value"] 
@@ -134,7 +130,6 @@ def run_inference(image_tensor: torch.Tensor, description: str, category: str, m
         "attributes": results,
         "total_attributes": total_attributes,
         "avg_confidence": avg_confidence,
-        "processing_time": end_time - start_time
     }
 
 def get_confidence_color(confidence: float) -> str:
@@ -179,7 +174,6 @@ def format_results_html(results: dict) -> str:
         <p>
             <strong>{results["total_attributes"]}</strong> attributes extracted | 
             <strong>{results["avg_confidence"]:.1%}</strong> avg confidence | 
-            <strong>{results["processing_time"]:.2f}s</strong> processing time
         </p>
     </div>
     </div>