Update app.py

app.py

@@ -157,46 +157,6 @@ def predict_image(model_name, image, confidence, models):
     except Exception as e:
         return f"❌ Error during prediction: {str(e)}", None, None
 
-def predict_video(model_name, video, confidence, models):
-    """
-    Perform prediction on an uploaded video using the selected YOLO model.
-
-    Args:
-        model_name (str): The name of the selected model.
-        video (str): Path to the uploaded video file.
-        confidence (float): The confidence threshold for detections.
-        models (dict): The dictionary containing models and their info.
-
-    Returns:
-        tuple: A status message, the processed video, and the path to the output video.
-    """
-    model_entry = models.get(model_name, {})
-    model = model_entry.get('model', None)
-    if not model:
-        return "Error: Model not found.", None, None
-    try:
-
-        os.makedirs(TEMP_DIR, exist_ok=True)
-        os.makedirs(OUTPUT_DIR, exist_ok=True)
-
-        input_video_path = os.path.join(TEMP_DIR, f"{model_name}_input_video.mp4")
-        shutil.copy(video, input_video_path)
-
-        results = model(input_video_path, save=True, save_txt=False, conf=confidence)
-
-        latest_run = sorted(Path("runs/detect").glob("predict*"), key=os.path.getmtime)[-1]
-        output_video_path = os.path.join(latest_run, Path(input_video_path).name)
-        if not os.path.isfile(output_video_path):
-
-            output_video_path = results[0].save()[0]
-
-        final_output_path = os.path.join(OUTPUT_DIR, f"{model_name}_output_video.mp4")
-        shutil.copy(output_video_path, final_output_path)
-
-        return "✅ Prediction completed successfully.", final_output_path, final_output_path
-    except Exception as e:
-        return f"❌ Error during prediction: {str(e)}", None, None
-
 def main():
 
     models = load_models()
@@ -208,7 +168,7 @@ def main():
         gr.Markdown("# 🧪 YOLOv11 Model Tester")
         gr.Markdown(
             """
-            Upload images or videos to test different YOLOv11 models. Select a model from the dropdown to see its details.
+            Upload images to test different YOLOv11 models. Select a model from the dropdown to see its details.
             """
         )
 
@@ -247,53 +207,29 @@ def main():
                 info="Adjust the minimum confidence required for detections to be displayed."
             )
 
-        with gr.Tabs():
-
-            with gr.Tab("🖼️ Image"):
-                with gr.Column():
-                    image_input = gr.Image(
-                        type='pil',
-                        label="Upload Image for Prediction"
-
-                    )
-                    image_predict_btn = gr.Button("🔍 Predict on Image")
-                    image_status = gr.Markdown("**Status will appear here.**")
-                    image_output = gr.Image(label="Predicted Image")
-                    image_download_btn = gr.File(label="⬇️ Download Predicted Image")
-
-                def process_image(selected_display_name, image, confidence):
-                    if not selected_display_name:
-                        return "❌ Please select a model.", None, None
-                    model_name = display_to_name.get(selected_display_name)
-                    return predict_image(model_name, image, confidence, models)
-
-                image_predict_btn.click(
-                    fn=process_image,
-                    inputs=[model_dropdown, image_input, confidence_slider],
-                    outputs=[image_status, image_output, image_download_btn]
-                )
+        with gr.Tab("🖼️ Image"):
+            with gr.Column():
+                image_input = gr.Image(
+                    type='pil',
+                    label="Upload Image for Prediction"
 
-            with gr.Tab("🎥 Video"):
-                with gr.Column():
-                    video_input = gr.Video(
-                        label="Upload Video for Prediction"
-                    )
-                    video_predict_btn = gr.Button("🔍 Predict on Video")
-                    video_status = gr.Markdown("**Status will appear here.**")
-                    video_output = gr.Video(label="Predicted Video")
-                    video_download_btn = gr.File(label="⬇️ Download Predicted Video")
-
-                def process_video(selected_display_name, video, confidence):
-                    if not selected_display_name:
-                        return "❌ Please select a model.", None, None
-                    model_name = display_to_name.get(selected_display_name)
-                    return predict_video(model_name, video, confidence, models)
-
-                video_predict_btn.click(
-                    fn=process_video,
-                    inputs=[model_dropdown, video_input, confidence_slider],
-                    outputs=[video_status, video_output, video_download_btn]
                 )
+                image_predict_btn = gr.Button("🔍 Predict on Image")
+                image_status = gr.Markdown("**Status will appear here.**")
+                image_output = gr.Image(label="Predicted Image")
+                image_download_btn = gr.File(label="⬇️ Download Predicted Image")
+
+            def process_image(selected_display_name, image, confidence):
+                if not selected_display_name:
+                    return "❌ Please select a model.", None, None
+                model_name = display_to_name.get(selected_display_name)
+                return predict_image(model_name, image, confidence, models)
+
+            image_predict_btn.click(
+                fn=process_image,
+                inputs=[model_dropdown, image_input, confidence_slider],
+                outputs=[image_status, image_output, image_download_btn]
+            )
 
         gr.Markdown(
             """
@@ -305,4 +241,4 @@ def main():
     demo.launch()
 
 if __name__ == "__main__":
-    main()
+    main()