**Add media tabs, detection button, and report/metrics tools to app UI**

app.py

@@ -802,54 +802,62 @@ with gr.Blocks(
         btn_enter = gr.Button("Entrar")
 
     with gr.Group(visible=False) as app_group:
+        # Input section: tabs for different media types
+        with gr.Tabs() as media_tabs:
+            # Video tab: only video input
+            with gr.TabItem("Vídeo"):
+                video_input = gr.Video(label="Sube tu vídeo de inspección")
+            # Imagen tab: only image input
+            with gr.TabItem("Imagen"):
+                image_input = gr.Image(type="filepath", label="Sube una imagen de inspección")
+            # Configuración tab: only classes tools
+            with gr.TabItem("Configuración"):
+                btn_classes = gr.Button("Mostrar clases del modelo")
+                txt_classes = gr.Textbox(label="Clases cargadas", interactive=False)
+                btn_classes.click(fn=show_classes, outputs=txt_classes)
+            # Reportes tab: only report tools
+            with gr.TabItem("Reportes"):
+                btn_report = gr.Button("Generar analisis fuerte")
+                status = gr.Textbox(label="Estado", interactive=False)
+                pdf_out = gr.File(label="Reporte PDF")
+                md_out = gr.File(label="Reporte Markdown")
+                json_out = gr.File(label="Reporte JSON")
+                def _on_report(vid, img):
+                    path = None
+                    if vid:
+                        path = vid
+                    elif img:
+                        path = img if isinstance(img, str) else getattr(img, "name", None)
+                    if not path:
+                        return "No media provided", None, None, None
+                    res = generar_analisis_fuerte(path)
+                    return res.get("status", "error"), (res.get("report_pdf") if res.get("report_pdf") else None), (res.get("report_md") if res.get("report_md") else None), (res.get("report_json") if res.get("report_json") else None)
+                btn_report.click(fn=_on_report, inputs=[video_input, image_input], outputs=[status, pdf_out, md_out, json_out])
+            # Métricas tab: only metrics tools
+            with gr.TabItem("Métricas"):
+                btn_metrics = gr.Button("Ver métricas")
+                out_metrics = gr.JSON(label="Métricas", visible=True)
+                btn_metrics.click(fn=get_metrics, outputs=out_metrics, api_name="metrics")
+
+        # Detection button (always visible after token)
+        btn_detect = gr.Button("Detectar defectos", variant="primary")
+
+        # Output section: results appear here after detection
         with gr.Row():
-            with gr.Tabs() as media_tabs:
-                pass
-                # Video tab: only video input
-                with gr.TabItem("Vídeo"):
-                    video_input = gr.Video(label="Sube tu vídeo de inspección")
-                # Imagen tab: only image input
-                with gr.TabItem("Imagen"):
-                    image_input = gr.Image(type="filepath", label="Sube una imagen de inspección")
-                # Configuración tab: only classes tools
-                with gr.TabItem("Configuración"):
-                    btn_classes = gr.Button("Mostrar clases del modelo")
-                    txt_classes = gr.Textbox(label="Clases cargadas", interactive=False)
-                    btn_classes.click(fn=show_classes, outputs=txt_classes)
-                # Reportes tab: only report tools
-                with gr.TabItem("Reportes"):
-                    btn_report = gr.Button("Generar analisis fuerte")
-                    status = gr.Textbox(label="Estado", interactive=False)
-                    pdf_out = gr.File(label="Reporte PDF")
-                    md_out = gr.File(label="Reporte Markdown")
-                    json_out = gr.File(label="Reporte JSON")
-                    def _on_report(vid, img):
-                        path = None
-                        if vid:
-                            path = vid
-                        elif img:
-                            path = img if isinstance(img, str) else getattr(img, "name", None)
-                        if not path:
-                            return "No media provided", None, None, None
-                        res = generar_analisis_fuerte(path)
-                        return res.get("status", "error"), (res.get("report_pdf") if res.get("report_pdf") else None), (res.get("report_md") if res.get("report_md") else None), (res.get("report_json") if res.get("report_json") else None)
-                    btn_report.click(fn=_on_report, inputs=[video_input, image_input], outputs=[status, pdf_out, md_out, json_out])
-                # Métricas tab: only metrics tools
-                with gr.TabItem("Métricas"):
-                    btn_metrics = gr.Button("Ver métricas")
-                    out_metrics = gr.JSON(label="Métricas", visible=True)
-                    btn_metrics.click(fn=get_metrics, outputs=out_metrics, api_name="metrics")
-
-            # Shared components (must be inside app_group so they are hidden before token)
-            output_video = gr.Video(label="Vídeo anotado", visible=False)
-            output_image = gr.Image(label="Imagen anotada", visible=False)
-            json_video = gr.JSON(visible=False)
-            json_image = gr.JSON(visible=False)
-            btn_detect = gr.Button("Detectar defectos")
-            ev_video = btn_detect.click(fn=infer_media, inputs=video_input, outputs=json_video, api_name="infer_media")
-            ev_video.then(_extract_video_update, inputs=json_video, outputs=output_video)
-            ev_image = btn_detect.click(fn=infer_media, inputs=image_input, outputs=json_image, api_name="infer_media_1")
-            ev_image.then(_extract_path_update, inputs=json_image, outputs=output_image)
+            with gr.Column():
+                output_video = gr.Video(label="Vídeo anotado", visible=False)
+            with gr.Column():
+                output_image = gr.Image(label="Imagen anotada", visible=False)
+
+        # Hidden JSON components for API chaining
+        json_video = gr.JSON(visible=False)
+        json_image = gr.JSON(visible=False)
+
+        # Wire up the detection events
+        ev_video = btn_detect.click(fn=infer_media, inputs=video_input, outputs=json_video, api_name="infer_media")
+        ev_video.then(_extract_video_update, inputs=json_video, outputs=output_video)
+        ev_image = btn_detect.click(fn=infer_media, inputs=image_input, outputs=json_image, api_name="infer_media_1")
+        ev_image.then(_extract_path_update, inputs=json_image, outputs=output_image)
 
     # Wire the gate
     btn_enter.click(fn=_check_token, inputs=[gate_token], outputs=[gate_group, app_group, gate_status])