Update app.py

app.py

@@ -50,23 +50,19 @@ def analyze_image(image_path):
         with torch.no_grad():
             outputs = model(**inputs)
 
-        # Set a threshold for filtering low-confidence detections (e.g., 0.8)
-        threshold = 0.8
+        # Set a target size for post-processing
         target_sizes = torch.tensor([image.size[::-1]])  # (height, width)
-        results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=threshold)[0]
-
-        # Filter detections by confidence threshold
-        if len(results["labels"]) > 0:
-            detected_objects = []
-            for idx, label in enumerate(results["labels"]):
-                if results["scores"][idx] >= threshold:  # Check if confidence score is high enough
-                    # Get the object label based on label index
-                    object_name = COCO_CLASSES[label.item()]  # Assuming COCO_CLASSES is available
-                    detected_objects.append(object_name)
-            if detected_objects:
-                bot_response = f"Objects detected: {', '.join(detected_objects)}."
-            else:
-                bot_response = "No objects detected above the confidence threshold."
+        results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)[0]
+
+        # Collect detected objects
+        detected_objects = []
+        for idx, label in enumerate(results["labels"]):
+            # Get the object label based on label index
+            object_name = COCO_CLASSES[label.item()]  # Assuming COCO_CLASSES is available
+            detected_objects.append(object_name)
+
+        if detected_objects:
+            bot_response = f"Objects detected: {', '.join(detected_objects)}."
         else:
             bot_response = "No objects detected."
 
@@ -78,7 +74,6 @@ def analyze_image(image_path):
         return chat_history
 
 
-
 # Build the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# Ken Chatbot")