Update app.py

app.py

@@ -1,71 +1,68 @@
-import gradio as gr
-from PIL import Image
-import base64
-import io
-import numpy as np
-from typing import List
-from main import segmenter  # Import the segmenter instance
-
-def process_image(image: Image.Image, objects_text: str) -> dict:
-    """Process image and return results"""
-    try:
-        # Parse objects
-        objects = [obj.strip() for obj in objects_text.split('.') if obj.strip()]
-        
-        # Use the segmenter to process the image
-        results = segmenter.segment_objects(image, objects)
-        
-        # Create visualization of results
-        # For now, just returning the original image
-        buffered = io.BytesIO()
-        image.save(buffered, format="PNG")
-        img_str = base64.b64encode(buffered.getvalue()).decode()
-        
-        # Format results for response
-        return {
-            "success": True,
-            "message": f"Processed image with objects: {objects}",
-            "image": img_str,
-            "results": [
-                {
-                    "label": r.label,
-                    "confidence": float(r.confidence),
-                    "bounding_box": r.bounding_box
-                }
-                for r in results
-            ]
-        }
-    except Exception as e:
-        return {
-            "success": False,
-            "message": str(e),
-            "image": None,
-            "results": []
-        }
-
-# Create Gradio interface with API mode enabled
-demo = gr.Interface(
-    fn=process_image,
-    inputs=[
-        gr.Image(type="pil", label="Input Image"),
-        gr.Textbox(label="Objects (separate with dots)", placeholder="cat. dog. chair")
-    ],
-    outputs=gr.JSON(label="API Response"),
-    title="Zero Shot Segmentation",
-    description="Upload an image and specify objects to detect.",
-    allow_flagging="never",
-    examples=[
-        ["path/to/example.jpg", "cat. dog"]
-    ]
-)
-
-# Enable API access
-demo.queue()
-
-if __name__ == "__main__":
-    demo.launch(
-        share=True,
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_api=True
+import gradio as gr
+from PIL import Image
+import base64
+import io
+import numpy as np
+from typing import List
+from main import segmenter  # Import the segmenter instance
+
+def process_image(image: Image.Image, objects_text: str) -> dict:
+    """Process image and return results"""
+    try:
+        # Parse objects
+        objects = [obj.strip() for obj in objects_text.split('.') if obj.strip()]
+        
+        # Use the segmenter to process the image
+        results = segmenter.segment_objects(image, objects)
+        
+        # Create visualization of results
+        # For now, just returning the original image
+        buffered = io.BytesIO()
+        image.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode()
+        
+        # Format results for response
+        return {
+            "success": True,
+            "message": f"Processed image with objects: {objects}",
+            "image": img_str,
+            "results": [
+                {
+                    "label": r.label,
+                    "confidence": float(r.confidence),
+                    "bounding_box": r.bounding_box
+                }
+                for r in results
+            ]
+        }
+    except Exception as e:
+        return {
+            "success": False,
+            "message": str(e),
+            "image": None,
+            "results": []
+        }
+
+# Create Gradio interface with API mode enabled
+demo = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(type="pil", label="Input Image"),
+        gr.Textbox(label="Objects (separate with dots)", placeholder="cat. dog. chair")
+    ],
+    outputs=gr.JSON(label="API Response"),
+    title="Zero Shot Segmentation",
+    description="Upload an image and specify objects to detect.",
+    allow_flagging="never"
+)
+
+# Enable API access
+demo.queue()
+
+if __name__ == "__main__":
+    demo.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_api=True
     )