Update app.py

app.py

@@ -403,15 +403,34 @@ with gr.Blocks(css=css) as demo:
             # )
         with gr.Column(scale=2):
             output_label = gr.Label(label="Overall Quality Estimate", elem_id="quality-label")
-            # ***** LÍNEA MODIFICADA: Se quitó height=350 *****
             output_dataframe = gr.DataFrame(
                 headers=["Criterion", "Sim (+)", "Sim (-)", "Difference", "Assessment (Comp)", "Assessment (Simp)"],
                 label="Detailed Quality Assessment",
-                wrap=True
-                # height=350 # <- Eliminado
+                wrap=True,
+                height=350
                 )
             output_json = gr.JSON(label="Raw Results (for debugging)")
 
+
+    gr.Markdown(
+        f"""
+        **Explanation:**
+        *   **Criterion:** The quality aspect being evaluated (using the positive prompt text).
+        *   **Sim (+):** Cosine similarity between the image and the *positive* text prompt (e.g., "optimal centering"). Higher is better.
+        *   **Sim (-):** Cosine similarity between the image and the *negative* text prompt (e.g., "poorly centered"). Lower is better.
+        *   **Difference:** Sim (+) - Sim (-). A large positive difference indicates the image is much closer to the positive description.
+        *   **Assessment (Comp):** PASS if Difference > {SIMILARITY_DIFFERENCE_THRESHOLD}, otherwise FAIL. This is the main comparative assessment.
+        *   **Assessment (Simp):** PASS if Sim (+) > {POSITIVE_SIMILARITY_THRESHOLD}, otherwise FAIL. A simpler check based only on positive similarity.
+        """
+    )
+
+    # Conectar el botón a la función de procesamiento
+    submit_button.click(
+        fn=assess_quality,
+        inputs=input_image,
+        outputs=[output_dataframe, output_label, output_json]
+    )
+
 # --- Iniciar la Aplicación Gradio ---
 # Al desplegar en Spaces, Gradio se encarga de esto automáticamente.
 # Para ejecutar localmente: demo.launch()