Update app.py

app.py

@@ -1,573 +1,3 @@
-# import gradio as gr
-# import numpy as np
-# import torch
-# from PIL import Image
-# import matplotlib.pyplot as plt
-# from transformers import pipeline
-# import warnings
-# from io import BytesIO
-# import importlib.util
-# import os
-# import openai
-
-# # Suppress warnings
-# warnings.filterwarnings("ignore")
-
-# # Set up OpenAI API key
-# api_key = os.getenv('OPENAI_API_KEY')
-# if not api_key:
-#     print("No OpenAI API key found - will use simple keyword extraction")
-# elif not api_key.startswith("sk-proj-") and not api_key.startswith("sk-"):
-#     print("API key found but doesn't look correct")
-# elif api_key.strip() != api_key:
-#     print("API key has leading or trailing whitespace - please fix it.")
-# else:
-#     print("OpenAI API key found and looks good!")
-#     openai.api_key = api_key
-
-# # Global variables for models
-# detector = None
-# sam_predictor = None
-
-# def load_detector():
-#     """Load the OWL-ViT detector once and cache it."""
-#     global detector
-#     if detector is None:
-#         print("Loading OWL-ViT model...")
-#         detector = pipeline(
-#             model="google/owlv2-base-patch16-ensemble", 
-#             task="zero-shot-object-detection",
-#             device=0 if torch.cuda.is_available() else -1
-#         )
-#         print("OWL-ViT model loaded successfully!")
-
-# def install_sam2_if_needed():
-#     """Check if SAM2 is installed, and install it if needed."""
-#     if importlib.util.find_spec("sam2") is not None:
-#         print("SAM2 is already installed.")
-#         return True
-    
-#     try:
-#         import subprocess
-#         import sys
-#         print("Installing SAM2 from GitHub...")
-#         subprocess.check_call([sys.executable, "-m", "pip", "install", "git+https://github.com/facebookresearch/sam2.git"])
-#         print("SAM2 installed successfully.")
-#         return True
-#     except Exception as e:
-#         print(f"Error installing SAM2: {e}")
-#         return False
-
-# def load_sam_predictor():
-#     """Load SAM2 predictor if available."""
-#     global sam_predictor
-#     if sam_predictor is None:
-#         if install_sam2_if_needed():
-#             try:
-#                 from sam2.sam2_image_predictor import SAM2ImagePredictor
-#                 print("Loading SAM2 model...")
-#                 sam_predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-large")
-#                 device = "cuda" if torch.cuda.is_available() else "cpu"
-#                 sam_predictor.model.to(device)
-#                 print(f"SAM2 model loaded successfully on {device}!")
-#                 return True
-#             except Exception as e:
-#                 print(f"Error loading SAM2: {e}")
-#                 return False
-#     return sam_predictor is not None
-
-# def detect_objects_owlv2(text_query, image, threshold=0.1):
-#     """Detect objects using OWL-ViT."""
-#     try:
-#         load_detector()
-        
-#         if isinstance(image, np.ndarray):
-#             image = Image.fromarray(image)
-        
-#         # Clean up the text query
-#         query_terms = [term.strip() for term in text_query.split(',') if term.strip()]
-#         if not query_terms:
-#             query_terms = ["object"]
-        
-#         print(f"Detecting: {query_terms}")
-#         predictions = detector(image, candidate_labels=query_terms)
-        
-#         detections = []
-#         for pred in predictions:
-#             if pred['score'] >= threshold:
-#                 bbox = pred['box']
-#                 width, height = image.size
-#                 normalized_bbox = [
-#                     bbox['xmin'] / width,
-#                     bbox['ymin'] / height, 
-#                     bbox['xmax'] / width,
-#                     bbox['ymax'] / height
-#                 ]
-                
-#                 detection = {
-#                     'label': pred['label'],
-#                     'bbox': normalized_bbox,
-#                     'score': pred['score']
-#                 }
-#                 detections.append(detection)
-        
-#         return detections, image
-#     except Exception as e:
-#         print(f"Detection error: {e}")
-#         return [], image
-
-# def generate_masks_sam2(detections, image):
-#     """Generate segmentation masks using SAM2."""
-#     try:
-#         if not load_sam_predictor():
-#             print("SAM2 not available, skipping mask generation")
-#             return detections
-        
-#         if isinstance(image, np.ndarray):
-#             image = Image.fromarray(image)
-        
-#         image_np = np.array(image.convert("RGB"))
-#         H, W = image_np.shape[:2]
-        
-#         # Set image for SAM2
-#         sam_predictor.set_image(image_np)
-        
-#         # Convert normalized bboxes to pixel coordinates
-#         input_boxes = []
-#         for det in detections:
-#             x1, y1, x2, y2 = det['bbox']
-#             input_boxes.append([int(x1 * W), int(y1 * H), int(x2 * W), int(y2 * H)])
-        
-#         if not input_boxes:
-#             return detections
-        
-#         input_boxes = np.array(input_boxes)
-        
-#         print(f"Generating masks for {len(input_boxes)} detections...")
-        
-#         with torch.inference_mode():
-#             device = "cuda" if torch.cuda.is_available() else "cpu"
-#             if device == "cuda":
-#                 with torch.autocast("cuda", dtype=torch.bfloat16):
-#                     masks, scores, _ = sam_predictor.predict(
-#                         point_coords=None, 
-#                         point_labels=None,
-#                         box=input_boxes, 
-#                         multimask_output=False
-#                     )
-#             else:
-#                 masks, scores, _ = sam_predictor.predict(
-#                     point_coords=None, 
-#                     point_labels=None,
-#                     box=input_boxes, 
-#                     multimask_output=False
-#                 )
-        
-#         # Add masks to detections
-#         results = []
-#         for i, det in enumerate(detections):
-#             new_det = det.copy()
-#             mask = masks[i]
-#             if mask.ndim == 3:
-#                 mask = mask[0]  # Remove batch dimension if present
-#             new_det['mask'] = mask.astype(np.uint8)
-#             results.append(new_det)
-        
-#         print(f"Successfully generated {len(results)} masks")
-#         return results
-        
-#     except Exception as e:
-#         print(f"SAM2 mask generation error: {e}")
-#         return detections
-
-# def visualize_detections_with_masks(image, detections_with_masks, show_labels=True, show_boxes=True):
-#     """
-#     Visualize the detections with their segmentation masks.
-#     Returns PIL Image instead of showing plot.
-#     """
-#     # Load the image
-#     if isinstance(image, np.ndarray):
-#         image = Image.fromarray(image)
-#     image_np = np.array(image.convert("RGB"))
-    
-#     # Get image dimensions
-#     height, width = image_np.shape[:2]
-    
-#     # Create figure
-#     fig = plt.figure(figsize=(12, 8))
-#     plt.imshow(image_np)
-    
-#     # Define colors for different instances
-#     colors = plt.cm.tab10(np.linspace(0, 1, 10))
-    
-#     # Plot each detection
-#     for i, detection in enumerate(detections_with_masks):
-#         # Get bbox, mask, label, and score
-#         bbox = detection['bbox']
-#         label = detection['label']
-#         score = detection['score']
-        
-#         # Convert normalized bbox to pixel coordinates
-#         x1, y1, x2, y2 = bbox
-#         x1_px, y1_px = int(x1 * width), int(y1 * height)
-#         x2_px, y2_px = int(x2 * width), int(y2 * height)
-        
-#         # Color for this instance
-#         color = colors[i % len(colors)]
-        
-#         # Display mask with transparency if available
-#         if 'mask' in detection:
-#             mask = detection['mask']
-#             mask_color = np.zeros((height, width, 4), dtype=np.float32)
-#             mask_color[mask > 0] = [color[0], color[1], color[2], 0.5]
-#             plt.imshow(mask_color)
-        
-#         # Draw bounding box if requested
-#         if show_boxes:
-#             rect = plt.Rectangle((x1_px, y1_px), x2_px - x1_px, y2_px - y1_px,
-#                                 fill=False, edgecolor=color, linewidth=2)
-#             plt.gca().add_patch(rect)
-        
-#         # Add label and score if requested
-#         if show_labels:
-#             plt.text(x1_px, y1_px - 5, f"{label}: {score:.2f}",
-#                     color='white', bbox=dict(facecolor=color, alpha=0.8), fontsize=10)
-    
-#     plt.axis('off')
-#     plt.tight_layout()
-    
-#     # Convert to PIL Image using the correct method
-#     buf = BytesIO()
-#     plt.savefig(buf, format='png', bbox_inches='tight', dpi=150)
-#     plt.close(fig)
-#     buf.seek(0)
-    
-#     result_image = Image.open(buf)
-#     return result_image
-
-# def visualize_detections(image, detections, show_labels=True):
-#     """
-#     Visualize object detections with bounding boxes only.
-#     Returns PIL Image instead of showing plot.
-#     """
-#     # Load the image
-#     if isinstance(image, np.ndarray):
-#         image = Image.fromarray(image)
-#     image_np = np.array(image.convert("RGB"))
-    
-#     # Get image dimensions
-#     height, width = image_np.shape[:2]
-    
-#     # Create figure
-#     fig = plt.figure(figsize=(12, 8))
-#     plt.imshow(image_np)
-    
-#     # If we have detections, draw them
-#     if detections:
-#         # Define colors for different instances
-#         colors = plt.cm.tab10(np.linspace(0, 1, 10))
-        
-#         # Plot each detection
-#         for i, detection in enumerate(detections):
-#             # Get bbox, label, and score
-#             bbox = detection['bbox']
-#             label = detection['label']
-#             score = detection['score']
-            
-#             # Convert normalized bbox to pixel coordinates
-#             x1, y1, x2, y2 = bbox
-#             x1_px, y1_px = int(x1 * width), int(y1 * height)
-#             x2_px, y2_px = int(x2 * width), int(y2 * height)
-            
-#             # Color for this instance
-#             color = colors[i % len(colors)]
-            
-#             # Draw bounding box
-#             rect = plt.Rectangle((x1_px, y1_px), x2_px - x1_px, y2_px - y1_px,
-#                                 fill=False, edgecolor=color, linewidth=2)
-#             plt.gca().add_patch(rect)
-            
-#             # Add label and score if requested
-#             if show_labels:
-#                 plt.text(x1_px, y1_px - 5, f"{label}: {score:.2f}",
-#                         color='white', bbox=dict(facecolor=color, alpha=0.8), fontsize=10)
-    
-#     # Set title
-#     plt.title(f'Object Detection Results ({len(detections)} objects found)', fontsize=14, pad=20)
-#     plt.axis('off')
-#     plt.tight_layout()
-    
-#     # Convert to PIL Image
-#     buf = BytesIO()
-#     plt.savefig(buf, format='png', bbox_inches='tight', dpi=150)
-#     plt.close(fig)
-#     buf.seek(0)
-    
-#     result_image = Image.open(buf)
-#     return result_image
-
-# def get_optimized_prompt(query_text):
-#     """
-#     Use OpenAI to convert natural language query into optimal detection prompt.
-#     Falls back to simple extraction if OpenAI is not available.
-#     """
-#     if not query_text.strip():
-#         return "object"
-    
-#     # Try OpenAI first if API key is available
-#     if hasattr(openai, 'api_key') and openai.api_key:
-#         try:
-#             response = openai.chat.completions.create(
-#                 model="gpt-3.5-turbo",
-#                 messages=[{
-#                     "role": "system", 
-#                     "content": """You are an expert at converting natural language queries into precise object detection terms. 
-
-# RULES:
-# 1. Return ONLY 1-2 words maximum that describe the object to detect
-# 2. Use the exact object name from the user's query
-# 3. For people: use "person" 
-# 4. For vehicles: use "car", "truck", "bicycle"
-# 5. Do NOT include counting words, articles, or explanations
-# 6. Examples:
-#    - "How many cacao fruits are there?" → "cacao fruit"
-#    - "Count the corn in the field" → "corn"
-#    - "Find all people" → "person"
-#    - "How many cacao pods?" → "cacao pod" 
-#    - "Detect cars" → "car"
-#    - "Count bananas" → "banana"
-#    - "How many apples?" → "apple"
-
-# Return ONLY the object name, nothing else."""
-#                 }, {
-#                     "role": "user", 
-#                     "content": query_text
-#                 }],
-#                 temperature=0.0,  # Make it deterministic
-#                 max_tokens=5      # Force brevity
-#             )
-            
-#             llm_result = response.choices[0].message.content.strip().lower()
-#             # Extra safety: take only first 2 words
-#             words = llm_result.split()[:2]
-#             final_result = " ".join(words)
-            
-#             print(f"🤖 OpenAI suggested prompt: '{final_result}'")
-#             return final_result
-            
-#         except Exception as e:
-#             print(f"OpenAI error: {e}, falling back to keyword extraction")
-    
-#     # Fallback to simple keyword extraction (no hardcoded fruits)
-#     print("🔤 Using keyword extraction (no OpenAI)")
-#     query_lower = query_text.lower().replace("?", "").strip()
-    
-#     # Look for common patterns and extract object names
-#     if "how many" in query_lower:
-#         parts = query_lower.split("how many")
-#         if len(parts) > 1:
-#             remaining = parts[1].strip()
-#             remaining = remaining.replace("are", "").replace("in", "").replace("the", "").replace("image", "").replace("there", "").strip()
-#             # Take first meaningful word(s)
-#             words = remaining.split()[:2]
-#             search_terms = " ".join(words) if words else "object"
-#         else:
-#             search_terms = "object"
-#     elif "count" in query_lower:
-#         parts = query_lower.split("count")
-#         if len(parts) > 1:
-#             remaining = parts[1].strip()
-#             remaining = remaining.replace("the", "").replace("in", "").replace("image", "").strip()
-#             words = remaining.split()[:2]
-#             search_terms = " ".join(words) if words else "object"
-#         else:
-#             search_terms = "object"
-#     elif "find" in query_lower:
-#         parts = query_lower.split("find")
-#         if len(parts) > 1:
-#             remaining = parts[1].strip()
-#             remaining = remaining.replace("all", "").replace("the", "").replace("in", "").replace("image", "").strip()
-#             words = remaining.split()[:2]
-#             search_terms = " ".join(words) if words else "object"
-#         else:
-#             search_terms = "object"
-#     else:
-#         # Extract first 1-2 meaningful words from the query
-#         words = query_lower.split()
-#         meaningful_words = [w for w in words if w not in ["how", "many", "are", "in", "the", "image", "find", "count", "detect", "there", "this", "that", "a", "an"]]
-#         search_terms = " ".join(meaningful_words[:2]) if meaningful_words else "object"
-    
-#     return search_terms
-
-# def is_count_query(text):
-#     """Check if the query is asking for counting."""
-#     count_keywords = ["how many", "count", "number of", "total"]
-#     return any(keyword in text.lower() for keyword in count_keywords)
-
-# def detection_pipeline(query_text, image, threshold, use_sam):
-#     """Main detection pipeline."""
-#     if image is None:
-#         return None, "⚠️ Please upload an image first!"
-    
-#     try:
-#         # Use OpenAI or fallback to get optimized search terms
-#         search_terms = get_optimized_prompt(query_text)
-        
-#         print(f"Processing query: '{query_text}' -> searching for: '{search_terms}'")
-        
-#         # Run object detection
-#         detections, processed_image = detect_objects_owlv2(search_terms, image, threshold)
-        
-#         print(f"Found {len(detections)} detections")
-#         for i, det in enumerate(detections):
-#             print(f"Detection {i+1}: {det['label']} (score: {det['score']:.3f})")
-        
-#         # Generate masks if requested
-#         if use_sam and detections:
-#             print("Generating SAM2 masks...")
-#             detections = generate_masks_sam2(detections, processed_image)
-        
-#         # Create visualization using your proven functions
-#         print("Creating visualization...")
-#         if use_sam and detections and 'mask' in detections[0]:
-#             result_image = visualize_detections_with_masks(
-#                 processed_image, 
-#                 detections, 
-#                 show_labels=True, 
-#                 show_boxes=True
-#             )
-#             print("Created visualization with masks")
-#         else:
-#             result_image = visualize_detections(
-#                 processed_image, 
-#                 detections, 
-#                 show_labels=True
-#             )
-#             print("Created visualization with bounding boxes only")
-        
-#         # Make sure we have a valid result image
-#         if result_image is None:
-#             print("Warning: result_image is None, returning original image")
-#             result_image = processed_image
-        
-#         # Generate summary
-#         count = len(detections)
-        
-#         summary_parts = []
-#         summary_parts.append(f"🗣️ **Original Query**: '{query_text}'")
-#         summary_parts.append(f"🤖 **AI-Optimized Search**: '{search_terms}'")
-#         summary_parts.append(f"⚙️ **Threshold**: {threshold}")
-#         summary_parts.append(f"🎭 **SAM2 Segmentation**: {'Enabled' if use_sam else 'Disabled'}")
-        
-#         if count > 0:
-#             if is_count_query(query_text):
-#                 summary_parts.append(f"🔢 **Answer: {count} {search_terms}(s) found**")
-#             else:
-#                 summary_parts.append(f"✅ **Found {count} {search_terms}(s)**")
-            
-#             # Show detection details
-#             for i, det in enumerate(detections[:5]):  # Show first 5
-#                 summary_parts.append(f"   • Detection {i+1}: {det['score']:.3f} confidence")
-#             if count > 5:
-#                 summary_parts.append(f"   • ... and {count-5} more detections")
-#         else:
-#             summary_parts.append(f"❌ **No {search_terms}(s) detected**")
-#             summary_parts.append("💡 Try lowering the threshold or using different terms")
-        
-#         summary_text = "\n".join(summary_parts)
-        
-#         return result_image, summary_text
-        
-#     except Exception as e:
-#         error_msg = f"❌ **Error**: {str(e)}"
-#         return image, error_msg
-
-# # ----------------
-# # GRADIO INTERFACE
-# # ----------------
-# with gr.Blocks(title="🔍 Object Detection & Segmentation") as demo:
-#     gr.Markdown("""
-#     # 🔍 Object Detection & Segmentation App
-    
-#     **Simple and powerful object detection using OWL-ViT + SAM2**
-    
-#     1. **Enter your query** (e.g., "How many people?", "Find cars", "Count apples")
-#     2. **Upload an image**
-#     3. **Adjust detection sensitivity**
-#     4. **Toggle SAM2 segmentation** for precise masks
-#     5. **Click Detect!**
-#     """)
-    
-#     with gr.Row():
-#         with gr.Column(scale=1):
-#             query_input = gr.Textbox(
-#                 label="🗣️ What do you want to detect?",
-#                 placeholder="e.g., 'How many people are in the image?'",
-#                 value="How many people are in the image?",
-#                 lines=2
-#             )
-            
-#             image_input = gr.Image(
-#                 label="📸 Upload your image",
-#                 type="numpy"
-#             )
-            
-#             with gr.Row():
-#                 threshold_slider = gr.Slider(
-#                     minimum=0.01,
-#                     maximum=0.9,
-#                     value=0.1,
-#                     step=0.01,
-#                     label="🎚️ Detection Sensitivity"
-#                 )
-                
-#                 sam_checkbox = gr.Checkbox(
-#                     label="🎭 Enable SAM2 Segmentation",
-#                     value=False,
-#                     info="Generate precise pixel masks"
-#                 )
-            
-#             detect_button = gr.Button("🔍 Detect Objects!", variant="primary", size="lg")
-    
-#         with gr.Column(scale=1):
-#             output_image = gr.Image(label="🎯 Detection Results")
-#             output_text = gr.Textbox(
-#                 label="📊 Detection Summary", 
-#                 lines=12,
-#                 show_copy_button=True
-#             )
-    
-#     # Event handlers
-#     detect_button.click(
-#         fn=detection_pipeline,
-#         inputs=[query_input, image_input, threshold_slider, sam_checkbox],
-#         outputs=[output_image, output_text]
-#     )
-    
-#     # Also trigger on Enter in text box
-#     query_input.submit(
-#         fn=detection_pipeline,
-#         inputs=[query_input, image_input, threshold_slider, sam_checkbox],
-#         outputs=[output_image, output_text]
-#     )
-    
-#     # Examples section
-#     gr.Examples(
-#         examples=[
-#             ["How many people are in the image?", None, 0.1, False],
-#             ["Find all cars", None, 0.15, True],
-#             ["Count the bottles", None, 0.1, True],
-#             ["Detect dogs", None, 0.2, False],
-#             ["How many phones?", None, 0.15, True],
-#         ],
-#         inputs=[query_input, image_input, threshold_slider, sam_checkbox],
-#     )
-
-# # Launch
-# if __name__ == "__main__":
-#     demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
-
-
 import gradio as gr
 import numpy as np
 import torch
@@ -579,7 +9,6 @@ from io import BytesIO
 import importlib.util
 import os
 import openai
-from typing import List, Dict
 
 # Suppress warnings
 warnings.filterwarnings("ignore")
@@ -600,75 +29,7 @@ else:
 detector = None
 sam_predictor = None
 
-def calculate_bbox_area(bbox):
-    """Calculate the area of a normalized bounding box."""
-    x1, y1, x2, y2 = bbox
-    width = abs(x2 - x1)
-    height = abs(y2 - y1)
-    return width * height
-
-def filter_bbox_outliers(detections: List[Dict], 
-                        method: str = 'zscore', 
-                        threshold: float = 2.0,
-                        min_score: float = 0.0) -> List[Dict]:
-    """
-    Filter out outlier bounding boxes based on their area.
-    
-    Args:
-        detections: List of detection dictionaries with 'bbox', 'label', 'score'
-        method: 'iqr' (Interquartile Range) or 'zscore' (Z-score) 
-        threshold: Multiplier for IQR method or Z-score threshold
-        min_score: Minimum confidence score to keep detection
-        
-    Returns:
-        Filtered list of detections
-    """
-    if not detections:
-        return detections
-    
-    # Filter by minimum score first
-    detections = [det for det in detections if det['score'] >= min_score]
-    
-    if len(detections) <= 2:  # Need at least 3 detections for meaningful outlier removal
-        return detections
-    
-    # Calculate areas for all bounding boxes
-    areas = [calculate_bbox_area(det['bbox']) for det in detections]
-    areas = np.array(areas)
-    
-    if method == 'iqr':
-        # IQR method
-        q1 = np.percentile(areas, 25)
-        q3 = np.percentile(areas, 75)
-        iqr = q3 - q1
-        
-        lower_bound = q1 - threshold * iqr
-        upper_bound = q3 + threshold * iqr
-        
-        valid_indices = np.where((areas >= lower_bound) & (areas <= upper_bound))[0]
-        
-    elif method == 'zscore':
-        # Z-score method
-        if np.std(areas) == 0:  # All areas are the same
-            return detections
-            
-        mean_area = np.mean(areas)
-        std_area = np.std(areas)
-        
-        z_scores = np.abs((areas - mean_area) / std_area)
-        valid_indices = np.where(z_scores <= threshold)[0]
-        
-    else:
-        raise ValueError("Method must be 'iqr' or 'zscore'")
-    
-    # Return filtered detections
-    filtered_detections = [detections[i] for i in valid_indices]
-    
-    print(f"Original detections: {len(detections)}")
-    print(f"Filtered detections: {len(filtered_detections)}")
-    print(f"Removed {len(detections) - len(filtered_detections)} outliers")
-    
-    return filtered_detections
+def load_detector():
     """Load the OWL-ViT detector once and cache it."""
     global detector
     if detector is None:
@@ -715,6 +76,54 @@ def load_sam_predictor():
                 return False
     return sam_predictor is not None
 
+def filter_bbox_outliers(detections: List[Dict], 
+                        method: str = 'iqr', 
+                        threshold: float = 1.5,
+                        min_score: float = 0.0) -> List[Dict]:
+    """
+    Filter out outlier bounding boxes based on their area.
+    
+    Args:
+        detections: List of detection dictionaries with 'bbox', 'label', 'score'
+        method: 'iqr' (Interquartile Range) or 'zscore' (Z-score) or 'percentile'
+        threshold: Multiplier for IQR method or Z-score threshold
+        min_score: Minimum confidence score to keep detection
+        
+    Returns:
+        Filtered list of detections
+    """
+    if not detections:
+        return detections
+    
+    # Filter by minimum score first
+    detections = [det for det in detections if det['score'] >= min_score]
+    
+    # Calculate areas for all bounding boxes
+    areas = [calculate_bbox_area(det['bbox']) for det in detections]
+    areas = np.array(areas)
+    
+        
+    if method == 'zscore':
+        # Z-score method
+        mean_area = np.mean(areas)
+        std_area = np.std(areas)
+        
+        z_scores = np.abs((areas - mean_area) / std_area)
+        valid_indices = np.where(z_scores <= threshold)[0]
+        
+    
+    else:
+        raise ValueError("Method must be 'iqr', 'zscore', or 'percentile'")
+    
+    # Return filtered detections
+    filtered_detections = [detections[i] for i in valid_indices]
+    
+    print(f"Original detections: {len(detections)}")
+    print(f"Filtered detections: {len(filtered_detections)}")
+    print(f"Removed {len(detections) - len(filtered_detections)} outliers")
+    
+    return filtered_detections
+
 def detect_objects_owlv2(text_query, image, threshold=0.1):
     """Detect objects using OWL-ViT."""
     try:
@@ -749,8 +158,10 @@ def detect_objects_owlv2(text_query, image, threshold=0.1):
                     'score': pred['score']
                 }
                 detections.append(detection)
+
+        print(detections)
+        return filter_bbox_outliers(detections),image
         
-        return detections, image
     except Exception as e:
         print(f"Detection error: {e}")
         return [], image
@@ -819,7 +230,7 @@ def generate_masks_sam2(detections, image):
         print(f"SAM2 mask generation error: {e}")
         return detections
 
-def visualize_detections_with_masks(image, detections_with_masks, show_labels=True, show_boxes=True):
+def visualize_detections_with_masks(image, detections_with_masks, show_labels=False, show_boxes=True):
     """
     Visualize the detections with their segmentation masks.
     Returns PIL Image instead of showing plot.
@@ -884,7 +295,7 @@ def visualize_detections_with_masks(image, detections_with_masks, show_labels=Tr
     result_image = Image.open(buf)
     return result_image
 
-def visualize_detections(image, detections, show_labels=True):
+def visualize_detections(image, detections, show_labels=False):
     """
     Visualize object detections with bounding boxes only.
     Returns PIL Image instead of showing plot.
@@ -1057,27 +468,22 @@ def detection_pipeline(query_text, image, threshold, use_sam):
         # Run object detection
         detections, processed_image = detect_objects_owlv2(search_terms, image, threshold)
         
-        print(f"Found {len(detections)} initial detections")
+        print(f"Found {len(detections)} detections")
         for i, det in enumerate(detections):
-            print(f"Detection {i+1}: {det['label']} (score: {det['score']:.3f}, area: {calculate_bbox_area(det['bbox']):.6f})")
-        
-        # Filter outliers before SAM2
-        if len(detections) > 2:  # Only filter if we have enough detections
-            detections = filter_bbox_outliers(detections, method='zscore', threshold=2.0)
-            print(f"After outlier filtering: {len(detections)} detections remain")
+            print(f"Detection {i+1}: {det['label']} (score: {det['score']:.3f})")
         
         # Generate masks if requested
         if use_sam and detections:
             print("Generating SAM2 masks...")
             detections = generate_masks_sam2(detections, processed_image)
         
-        # Create visualization using your proven functions (labels OFF)
+        # Create visualization using your proven functions
         print("Creating visualization...")
         if use_sam and detections and 'mask' in detections[0]:
             result_image = visualize_detections_with_masks(
                 processed_image, 
                 detections, 
-                show_labels=False,  # Labels OFF
+                show_labels=True, 
                 show_boxes=True
             )
             print("Created visualization with masks")
@@ -1085,7 +491,7 @@ def detection_pipeline(query_text, image, threshold, use_sam):
             result_image = visualize_detections(
                 processed_image, 
                 detections, 
-                show_labels=False  # Labels OFF
+                show_labels=True
             )
             print("Created visualization with bounding boxes only")
         
@@ -1209,4 +615,5 @@ with gr.Blocks(title="🔍 Object Detection & Segmentation") as demo:
 
 # Launch
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
+