save

app.py

@@ -193,37 +193,6 @@ def format_results(result, confidence_threshold: float) -> str:
     return results_text
 
 
-def process_webcam_stream(frame, model_name: str, confidence_threshold: float):
-    """
-    Process webcam stream frame by frame.
-    
-    Args:
-        frame: Camera frame as numpy array
-        model_name: Name of the model to use
-        confidence_threshold: Confidence threshold for predictions
-        
-    Returns:
-        Processed frame as PIL Image
-    """
-    if frame is None:
-        return None
-    
-    try:
-        # Load model
-        model = load_model(model_name)
-        
-        # Run inference
-        result = model(frame)
-        
-        # Visualize results
-        result_image = visualizer.render(frame, result)
-        
-        return result_image
-    except Exception as e:
-        print(f"[ERROR] Webcam stream error: {e}")
-        return frame
-
-
 def create_gradio_interface():
     """
     Create and configure the Gradio interface.
@@ -239,79 +208,53 @@ def create_gradio_interface():
     
     with gr.Blocks(title="Object Detection with model_api") as demo:
         gr.Markdown("# 🎯 Object Detection with model_api")
-        gr.Markdown("Upload an image, use your webcam, or select a model to perform object detection using OpenVINO and model_api")
+        gr.Markdown("Upload an image and select a model to perform object detection using OpenVINO and model_api")
         
-        # Model selection (shared across tabs)
         with gr.Row():
-            model_dropdown = gr.Dropdown(
-                choices=available_models,
-                value=available_models[0] if available_models else None,
-                label="Select Model",
-                info="Choose a model from the models/ folder"
-            )
-            
-            confidence_slider = gr.Slider(
-                minimum=0.0,
-                maximum=1.0,
-                value=0.3,
-                step=0.05,
-                label="Confidence Threshold",
-                info="Minimum confidence for displaying predictions"
-            )
-        
-        # Create tabs for different input sources
-        with gr.Tabs():
-            # Image Tab
-            with gr.TabItem("📷 Image Upload"):
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        input_image = gr.Image(
-                            label="Input Image",
-                            type="numpy",
-                            height=400
-                        )
-                        
-                        classify_btn = gr.Button("🚀 Run Inference", variant="primary")
-                    
-                    with gr.Column(scale=1):
-                        output_image = gr.Image(
-                            label="Detection Result",
-                            type="pil",
-                            show_label=False,
-                            height=400
-                        )
-                        
-                        detections_output = gr.Textbox(
-                            label="Detected Objects",
-                            lines=8,
-                            max_lines=15
-                        )
-                        
-                        metrics_output = gr.Textbox(
-                            label="Performance Metrics",
-                            lines=8,
-                            max_lines=15
-                        )
+            with gr.Column(scale=1):
+                input_image = gr.Image(
+                    label="Input Image",
+                    type="numpy",
+                    height=400
+                )
+                
+                model_dropdown = gr.Dropdown(
+                    choices=available_models,
+                    value=available_models[0] if available_models else None,
+                    label="Select Model",
+                    info="Choose a model from the models/ folder"
+                )
+                
+                confidence_slider = gr.Slider(
+                    minimum=0.0,
+                    maximum=1.0,
+                    value=0.3,
+                    step=0.05,
+                    label="Confidence Threshold",
+                    info="Minimum confidence for displaying predictions"
+                )
+                
+                classify_btn = gr.Button("🚀 Run Inference", variant="primary")
             
-            # Webcam Tab
-            with gr.TabItem("🎥 Webcam"):
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        webcam_input = gr.Image(
-                            sources=["webcam"],
-                            label="Webcam Feed",
-                            type="numpy",
-                            streaming=True,
-                            height=400
-                        )
-                    
-                    with gr.Column(scale=1):
-                        webcam_output = gr.Image(
-                            label="Live Detection",
-                            type="numpy",
-                            show_label=False,
-                            height=400
-                        )
+            with gr.Column(scale=1):
+                output_image = gr.Image(
+                    label="Detection Result",
+                    type="pil",
+                    show_label=False,
+                    height=400
+                )
+                
+                detections_output = gr.Textbox(
+                    label="Detected Objects",
+                    lines=8,
+                    max_lines=15
+                )
+                
+                metrics_output = gr.Textbox(
+                    label="Performance Metrics",
+                    lines=8,
+                    max_lines=15
+                )
         
         # Examples section
         gr.Markdown("## 📸 Examples")
@@ -328,21 +271,12 @@ def create_gradio_interface():
             cache_examples=False
         )
         
-        # Connect the button to the inference function for image upload
+        # Connect the button to the inference function
         classify_btn.click(
             fn=run_inference,
             inputs=[input_image, model_dropdown, confidence_slider],
             outputs=[output_image, detections_output, metrics_output]
         )
-        
-        # Connect webcam stream processing
-        webcam_input.stream(
-            fn=process_webcam_stream,
-            inputs=[webcam_input, model_dropdown, confidence_slider],
-            outputs=webcam_output,
-            stream_every=0.1,
-            time_limit=300
-        )
     
     return demo