Update app.py

app.py

@@ -15,10 +15,9 @@ import pandas as pd # Para formatear la salida en tabla
 
 # --- Configuración ---
 MODEL_REPO_ID = "google/cxr-foundation"
-MODEL_DOWNLOAD_DIR = './hf_cxr_foundation_space' # Directorio dentro del contenedor del Space
-# Umbrales
-SIMILARITY_DIFFERENCE_THRESHOLD = 0.0
-POSITIVE_SIMILARITY_THRESHOLD = 0.0
+MODEL_DOWNLOAD_DIR = './hf_cxr_foundation_space'
+SIMILARITY_DIFFERENCE_THRESHOLD = 0.1
+POSITIVE_SIMILARITY_THRESHOLD = 0.1
 print(f"Usando umbrales: Comp Δ={SIMILARITY_DIFFERENCE_THRESHOLD}, Simp τ={POSITIVE_SIMILARITY_THRESHOLD}")
 
 # --- Prompts ---
@@ -31,255 +30,158 @@ criteria_list_negative = [
     "cropped image", "scapulae overlying lungs", "blurred image", "obscuring artifact"
 ]
 
-# --- Funciones Auxiliares (Integradas o adaptadas) ---
-# @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string)]) # Puede ayudar rendimiento
-def preprocess_text(text):
-    """Función interna del preprocesador BERT."""
-    return bert_preprocessor_global(text)
+# --- Funciones Auxiliares (MISMAS que en la versión anterior de Gradio) ---
+# @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string)])
+# def preprocess_text(text):
+#     return bert_preprocessor_global(text) # Asume que bert_preprocessor_global está cargado
 
 def bert_tokenize(text, preprocessor):
-    """Tokeniza texto usando el preprocesador BERT cargado globalmente."""
-    if preprocessor is None:
-       raise ValueError("BERT preprocessor no está cargado.")
+    if preprocessor is None: raise ValueError("BERT preprocessor no está cargado.")
     if not isinstance(text, str): text = str(text)
-
-    # Ejecutar el preprocesador
     out = preprocessor(tf.constant([text.lower()]))
-
-    # Extraer y procesar IDs y máscaras
     ids = out['input_word_ids'].numpy().astype(np.int32)
     masks = out['input_mask'].numpy().astype(np.float32)
     paddings = 1.0 - masks
-
-    # Reemplazar token [SEP] (102) por 0 y marcar como padding
     end_token_idx = (ids == 102)
     ids[end_token_idx] = 0
     paddings[end_token_idx] = 1.0
-
-    # Asegurar las dimensiones (B, T, S) -> (1, 1, 128)
-    # El preprocesador puede devolver (1, 128), necesitamos (1, 1, 128)
     if ids.ndim == 2: ids = np.expand_dims(ids, axis=1)
     if paddings.ndim == 2: paddings = np.expand_dims(paddings, axis=1)
-
-    # Verificar formas finales
     expected_shape = (1, 1, 128)
     if ids.shape != expected_shape:
-         # Intentar reajustar si es necesario (puede pasar con algunas versiones)
          if ids.shape == (1,128): ids = np.expand_dims(ids, axis=1)
          else: raise ValueError(f"Shape incorrecta para ids: {ids.shape}, esperado {expected_shape}")
     if paddings.shape != expected_shape:
          if paddings.shape == (1,128): paddings = np.expand_dims(paddings, axis=1)
          else: raise ValueError(f"Shape incorrecta para paddings: {paddings.shape}, esperado {expected_shape}")
-
     return ids, paddings
 
 def png_to_tfexample(image_array: np.ndarray) -> tf.train.Example:
-    """Crea tf.train.Example desde NumPy array (escala de grises)."""
     if image_array.ndim == 3 and image_array.shape[2] == 1:
-        image_array = np.squeeze(image_array, axis=2) # Asegurar 2D
+        image_array = np.squeeze(image_array, axis=2)
     elif image_array.ndim != 2:
-        raise ValueError(f'Array debe ser 2-D (escala de grises). Dimensiones actuales: {image_array.ndim}')
-
+        raise ValueError(f'Array debe ser 2-D. Dimensiones: {image_array.ndim}')
     image = image_array.astype(np.float32)
-    min_val = image.min()
-    max_val = image.max()
-
-    # Evitar división por cero si la imagen es constante
+    min_val, max_val = image.min(), image.max()
     if max_val <= min_val:
-        # Si es constante, tratar como uint8 si el rango original lo permitía,
-        # o simplemente ponerla a 0 si es float.
         if image_array.dtype == np.uint8 or (min_val >= 0 and max_val <= 255):
-             pixel_array = image.astype(np.uint8)
-             bitdepth = 8
-        else: # Caso flotante constante o fuera de rango uint8
-             pixel_array = np.zeros_like(image, dtype=np.uint16)
-             bitdepth = 16
+             pixel_array = image.astype(np.uint8); bitdepth = 8
+        else:
+             pixel_array = np.zeros_like(image, dtype=np.uint16); bitdepth = 16
     else:
-        image -= min_val # Mover mínimo a cero
+        image -= min_val
         current_max = max_val - min_val
-        # Escalar a 16-bit para mayor precisión si no era uint8 originalmente
         if image_array.dtype != np.uint8:
             image *= 65535 / current_max
-            pixel_array = image.astype(np.uint16)
-            bitdepth = 16
+            pixel_array = image.astype(np.uint16); bitdepth = 16
         else:
-            # Si era uint8, mantener el rango y tipo
-            # La resta del min ya la dejó en [0, current_max]
-            # Escalar a 255 si es necesario
             image *= 255 / current_max
-            pixel_array = image.astype(np.uint8)
-            bitdepth = 8
-
-    # Codificar como PNG
+            pixel_array = image.astype(np.uint8); bitdepth = 8
     output = io.BytesIO()
-    png.Writer(
-        width=pixel_array.shape[1],
-        height=pixel_array.shape[0],
-        greyscale=True,
-        bitdepth=bitdepth
-    ).write(output, pixel_array.tolist())
-    png_bytes = output.getvalue()
-
-    # Crear tf.train.Example
+    png.Writer(width=pixel_array.shape[1], height=pixel_array.shape[0], greyscale=True, bitdepth=bitdepth).write(output, pixel_array.tolist())
     example = tf.train.Example()
     features = example.features.feature
-    features['image/encoded'].bytes_list.value.append(png_bytes)
+    features['image/encoded'].bytes_list.value.append(output.getvalue())
     features['image/format'].bytes_list.value.append(b'png')
     return example
 
 def generate_image_embedding(img_np, elixrc_infer, qformer_infer):
-    """Genera embedding final de imagen."""
-    if elixrc_infer is None or qformer_infer is None:
-        raise ValueError("Modelos ELIXR-C o QFormer no cargados.")
-
+    if elixrc_infer is None or qformer_infer is None: raise ValueError("Modelos ELIXR-C o QFormer no cargados.")
     try:
-        # 1. ELIXR-C
         serialized_img_tf_example = png_to_tfexample(img_np).SerializeToString()
         elixrc_output = elixrc_infer(input_example=tf.constant([serialized_img_tf_example]))
         elixrc_embedding = elixrc_output['feature_maps_0'].numpy()
-        print(f"  Embedding ELIXR-C shape: {elixrc_embedding.shape}")
-
-        # 2. QFormer (Imagen)
         qformer_input_img = {
             'image_feature': elixrc_embedding.tolist(),
-            'ids': np.zeros((1, 1, 128), dtype=np.int32).tolist(), # Texto vacío
-            'paddings': np.ones((1, 1, 128), dtype=np.float32).tolist(), # Todo padding
+            'ids': np.zeros((1, 1, 128), dtype=np.int32).tolist(),
+            'paddings': np.ones((1, 1, 128), dtype=np.float32).tolist(),
         }
         qformer_output_img = qformer_infer(**qformer_input_img)
         image_embedding = qformer_output_img['all_contrastive_img_emb'].numpy()
-
-        # Ajustar dimensiones si es necesario
         if image_embedding.ndim > 2:
-            print(f"  Ajustando dimensiones embedding imagen (original: {image_embedding.shape})")
-            image_embedding = np.mean(
-                image_embedding,
-                axis=tuple(range(1, image_embedding.ndim - 1))
-            )
-            if image_embedding.ndim == 1:
-                image_embedding = np.expand_dims(image_embedding, axis=0)
-        elif image_embedding.ndim == 1:
-             image_embedding = np.expand_dims(image_embedding, axis=0) # Asegurar 2D
-
-        print(f"  Embedding final imagen shape: {image_embedding.shape}")
-        if image_embedding.ndim != 2:
-            raise ValueError(f"Embedding final de imagen no tiene 2 dimensiones: {image_embedding.shape}")
+            image_embedding = np.mean(image_embedding, axis=tuple(range(1, image_embedding.ndim - 1)))
+        if image_embedding.ndim == 1: image_embedding = np.expand_dims(image_embedding, axis=0)
+        if image_embedding.ndim != 2: raise ValueError(f"Embedding final no tiene 2 dims: {image_embedding.shape}")
         return image_embedding
-
     except Exception as e:
-        print(f"Error generando embedding de imagen: {e}")
-        traceback.print_exc()
-        raise # Re-lanzar la excepción para que Gradio la maneje
+        print(f"Error generando embedding imagen: {e}"); traceback.print_exc(); raise
 
 def calculate_similarities_and_classify(image_embedding, bert_preprocessor, qformer_infer):
-    """Calcula similitudes y clasifica."""
-    if image_embedding is None: raise ValueError("Embedding de imagen es None.")
+    if image_embedding is None: raise ValueError("Embedding imagen es None.")
     if bert_preprocessor is None: raise ValueError("Preprocesador BERT es None.")
     if qformer_infer is None: raise ValueError("QFormer es None.")
-
     detailed_results = {}
-    print("\n--- Calculando similitudes y clasificando ---")
-
+    print("\n--- Calculando similitudes ---")
     for i in range(len(criteria_list_positive)):
-        positive_text = criteria_list_positive[i]
-        negative_text = criteria_list_negative[i]
-        criterion_name = positive_text # Usar prompt positivo como clave
-
-        print(f"Procesando criterio: \"{criterion_name}\"")
+        positive_text, negative_text = criteria_list_positive[i], criteria_list_negative[i]
+        criterion_name = positive_text
+        print(f"Procesando: \"{criterion_name}\"")
         similarity_positive, similarity_negative, difference = None, None, None
         classification_comp, classification_simp = "ERROR", "ERROR"
-
         try:
-            # 1. Embedding Texto Positivo
             tokens_pos, paddings_pos = bert_tokenize(positive_text, bert_preprocessor)
-            qformer_input_text_pos = {
-                'image_feature': np.zeros([1, 8, 8, 1376], dtype=np.float32).tolist(), # Dummy
-                'ids': tokens_pos.tolist(), 'paddings': paddings_pos.tolist(),
-            }
-            text_embedding_pos = qformer_infer(**qformer_input_text_pos)['contrastive_txt_emb'].numpy()
+            qformer_input_pos = {'image_feature': np.zeros([1, 8, 8, 1376], dtype=np.float32).tolist(), 'ids': tokens_pos.tolist(), 'paddings': paddings_pos.tolist()}
+            text_embedding_pos = qformer_infer(**qformer_input_pos)['contrastive_txt_emb'].numpy()
             if text_embedding_pos.ndim == 1: text_embedding_pos = np.expand_dims(text_embedding_pos, axis=0)
 
-            # 2. Embedding Texto Negativo
             tokens_neg, paddings_neg = bert_tokenize(negative_text, bert_preprocessor)
-            qformer_input_text_neg = {
-                'image_feature': np.zeros([1, 8, 8, 1376], dtype=np.float32).tolist(), # Dummy
-                'ids': tokens_neg.tolist(), 'paddings': paddings_neg.tolist(),
-            }
-            text_embedding_neg = qformer_infer(**qformer_input_text_neg)['contrastive_txt_emb'].numpy()
+            qformer_input_neg = {'image_feature': np.zeros([1, 8, 8, 1376], dtype=np.float32).tolist(), 'ids': tokens_neg.tolist(), 'paddings': paddings_neg.tolist()}
+            text_embedding_neg = qformer_infer(**qformer_input_neg)['contrastive_txt_emb'].numpy()
             if text_embedding_neg.ndim == 1: text_embedding_neg = np.expand_dims(text_embedding_neg, axis=0)
 
-            # Verificar compatibilidad de dimensiones para similitud
-            if image_embedding.shape[1] != text_embedding_pos.shape[1]:
-                 raise ValueError(f"Dimensión incompatible: Imagen ({image_embedding.shape[1]}) vs Texto Pos ({text_embedding_pos.shape[1]})")
-            if image_embedding.shape[1] != text_embedding_neg.shape[1]:
-                 raise ValueError(f"Dimensión incompatible: Imagen ({image_embedding.shape[1]}) vs Texto Neg ({text_embedding_neg.shape[1]})")
+            if image_embedding.shape[1] != text_embedding_pos.shape[1]: raise ValueError(f"Dim mismatch: Img ({image_embedding.shape[1]}) vs Pos ({text_embedding_pos.shape[1]})")
+            if image_embedding.shape[1] != text_embedding_neg.shape[1]: raise ValueError(f"Dim mismatch: Img ({image_embedding.shape[1]}) vs Neg ({text_embedding_neg.shape[1]})")
 
-            # 3. Calcular Similitudes
             similarity_positive = cosine_similarity(image_embedding, text_embedding_pos)[0][0]
             similarity_negative = cosine_similarity(image_embedding, text_embedding_neg)[0][0]
-            print(f"  Sim (+)={similarity_positive:.4f}, Sim (-)={similarity_negative:.4f}")
 
-            # 4. Clasificar
             difference = similarity_positive - similarity_negative
             classification_comp = "PASS" if difference > SIMILARITY_DIFFERENCE_THRESHOLD else "FAIL"
             classification_simp = "PASS" if similarity_positive > POSITIVE_SIMILARITY_THRESHOLD else "FAIL"
-            print(f"  Diff={difference:.4f} -> Comp: {classification_comp}, Simp: {classification_simp}")
-
+            print(f"  Sim(+)={similarity_positive:.4f}, Sim(-)={similarity_negative:.4f}, Diff={difference:.4f} -> Comp:{classification_comp}, Simp:{classification_simp}")
         except Exception as e:
-            print(f"  ERROR procesando criterio '{criterion_name}': {e}")
-            traceback.print_exc()
-            # Mantener clasificaciones como "ERROR"
-
-        # Guardar resultados
+            print(f"  ERROR criterio '{criterion_name}': {e}"); traceback.print_exc()
         detailed_results[criterion_name] = {
-            'positive_prompt': positive_text,
-            'negative_prompt': negative_text,
+            'positive_prompt': positive_text, 'negative_prompt': negative_text,
             'similarity_positive': float(similarity_positive) if similarity_positive is not None else None,
             'similarity_negative': float(similarity_negative) if similarity_negative is not None else None,
             'difference': float(difference) if difference is not None else None,
-            'classification_comparative': classification_comp,
-            'classification_simplified': classification_simp
+            'classification_comparative': classification_comp, 'classification_simplified': classification_simp
         }
     return detailed_results
 
 # --- Carga Global de Modelos ---
-# Se ejecuta UNA VEZ al iniciar la aplicación Gradio/Space
 print("--- Iniciando carga global de modelos ---")
 start_time = time.time()
 models_loaded = False
 bert_preprocessor_global = None
 elixrc_infer_global = None
 qformer_infer_global = None
-
 try:
-    # Verificar autenticación HF (útil si se usan modelos privados, aunque no es el caso aquí)
-    # if HfFolder.get_token() is None:
-    #     print("Advertencia: No se encontró token de Hugging Face.")
-    # else:
-    #     print("Token de Hugging Face encontrado.")
+    # Añadir token si es necesario (para repos privados o gated)
+    hf_token = os.environ.get("HF_TOKEN") # Leer token desde secretos del Space
+    # if hf_token:
+    #     print("Usando HF_TOKEN para autenticación.")
+    #     HfFolder.save_token(hf_token)
 
-    # Crear directorio si no existe
     os.makedirs(MODEL_DOWNLOAD_DIR, exist_ok=True)
     print(f"Descargando/verificando modelos en: {MODEL_DOWNLOAD_DIR}")
     snapshot_download(repo_id=MODEL_REPO_ID, local_dir=MODEL_DOWNLOAD_DIR,
                       allow_patterns=['elixr-c-v2-pooled/*', 'pax-elixr-b-text/*'],
-                      local_dir_use_symlinks=False) # Evitar symlinks
+                      local_dir_use_symlinks=False, token=hf_token) # Pasar token aquí
     print("Modelos descargados/verificados.")
 
-    # Cargar Preprocesador BERT desde TF Hub
     print("Cargando Preprocesador BERT...")
-    # Usar handle explícito puede ser más robusto en algunos entornos
     bert_preprocess_handle = "https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3"
     bert_preprocessor_global = tf_hub.KerasLayer(bert_preprocess_handle)
     print("Preprocesador BERT cargado.")
 
-    # Cargar ELIXR-C
     print("Cargando ELIXR-C...")
     elixrc_model_path = os.path.join(MODEL_DOWNLOAD_DIR, 'elixr-c-v2-pooled')
     elixrc_model = tf.saved_model.load(elixrc_model_path)
     elixrc_infer_global = elixrc_model.signatures['serving_default']
     print("Modelo ELIXR-C cargado.")
 
-    # Cargar QFormer (ELIXR-B Text)
     print("Cargando QFormer (ELIXR-B Text)...")
     qformer_model_path = os.path.join(MODEL_DOWNLOAD_DIR, 'pax-elixr-b-text')
     qformer_model = tf.saved_model.load(qformer_model_path)
@@ -289,143 +191,255 @@ try:
     models_loaded = True
     end_time = time.time()
     print(f"--- Modelos cargados globalmente con éxito en {end_time - start_time:.2f} segundos ---")
-
 except Exception as e:
     models_loaded = False
-    print(f"--- ERROR CRÍTICO DURANTE LA CARGA GLOBAL DE MODELOS ---")
-    print(e)
-    traceback.print_exc()
-    # Gradio se iniciará, pero la función de análisis fallará.
+    print(f"--- ERROR CRÍTICO DURANTE LA CARGA GLOBAL DE MODELOS ---"); print(e); traceback.print_exc()
 
 # --- Función Principal de Procesamiento para Gradio ---
-def assess_quality(image_pil):
-    """Función que Gradio llamará con la imagen de entrada."""
+def assess_quality_and_update_ui(image_pil):
+    """Procesa la imagen y devuelve actualizaciones para la UI."""
     if not models_loaded:
         raise gr.Error("Error: Los modelos no se pudieron cargar. La aplicación no puede procesar imágenes.")
     if image_pil is None:
-        # Devolver resultados vacíos o un mensaje de error si no hay imagen
-         return pd.DataFrame(), "N/A", None # Dataframe vacío, Label vacío, JSON vacío
+        # Devuelve valores por defecto/vacíos y controla la visibilidad
+        return (
+            gr.update(visible=True),  # Muestra bienvenida
+            gr.update(visible=False), # Oculta resultados
+            None,                     # Borra imagen de salida
+            gr.update(value="N/A"),   # Borra etiqueta
+            pd.DataFrame(),           # Borra dataframe
+            None                      # Borra JSON
+        )
 
     print("\n--- Iniciando evaluación para nueva imagen ---")
     start_process_time = time.time()
-
     try:
-        # 1. Convertir PIL Image a NumPy array (escala de grises)
-        # Gradio con type="pil" ya la entrega como objeto PIL
+        # 1. Convertir a NumPy
         img_np = np.array(image_pil.convert('L'))
-        print(f"Imagen convertida a NumPy. Shape: {img_np.shape}, Tipo: {img_np.dtype}")
-
-        # 2. Generar Embedding de Imagen
-        print("Generando embedding de imagen...")
+        # 2. Generar Embedding
         image_embedding = generate_image_embedding(img_np, elixrc_infer_global, qformer_infer_global)
-        print("Embedding de imagen generado.")
-
-        # 3. Calcular Similitudes y Clasificar
-        print("Calculando similitudes y clasificando criterios...")
+        # 3. Clasificar
         detailed_results = calculate_similarities_and_classify(image_embedding, bert_preprocessor_global, qformer_infer_global)
-        print("Clasificación completada.")
-
-        # 4. Formatear Resultados para Gradio
-        output_data = []
-        passed_count = 0
-        total_count = 0
+        # 4. Formatear Resultados
+        output_data, passed_count, total_count = [], 0, 0
         for criterion, details in detailed_results.items():
             total_count += 1
-            sim_pos_str = f"{details['similarity_positive']:.4f}" if details['similarity_positive'] is not None else "N/A"
-            sim_neg_str = f"{details['similarity_negative']:.4f}" if details['similarity_negative'] is not None else "N/A"
-            diff_str = f"{details['difference']:.4f}" if details['difference'] is not None else "N/A"
-            assessment_comp = details['classification_comparative']
-            assessment_simp = details['classification_simplified']
-            output_data.append([
-                criterion,
-                sim_pos_str,
-                sim_neg_str,
-                diff_str,
-                assessment_comp,
-                assessment_simp
-            ])
-            if assessment_comp == "PASS":
-                passed_count += 1
-
-        # Crear DataFrame
-        df_results = pd.DataFrame(output_data, columns=[
-            "Criterion", "Sim (+)", "Sim (-)", "Difference", "Assessment (Comp)", "Assessment (Simp)"
-        ])
-
-        # Calcular etiqueta de calidad general
-        overall_quality = "Error"
+            sim_pos = details['similarity_positive']
+            sim_neg = details['similarity_negative']
+            diff = details['difference']
+            comp = details['classification_comparative']
+            simp = details['classification_simplified']
+            output_data.append([ criterion, f"{sim_pos:.4f}" if sim_pos else "N/A",
+                f"{sim_neg:.4f}" if sim_neg else "N/A", f"{diff:.4f}" if diff else "N/A", comp, simp ])
+            if comp == "PASS": passed_count += 1
+        df_results = pd.DataFrame(output_data, columns=[ "Criterion", "Sim (+)", "Sim (-)", "Difference", "Assessment (Comp)", "Assessment (Simp)" ])
+        overall_quality = "Error"; pass_rate = 0
         if total_count > 0:
             pass_rate = passed_count / total_count
             if pass_rate >= 0.85: overall_quality = "Excellent"
             elif pass_rate >= 0.70: overall_quality = "Good"
             elif pass_rate >= 0.50: overall_quality = "Fair"
             else: overall_quality = "Poor"
-        quality_label = f"{overall_quality} ({passed_count}/{total_count} criteria passed)"
-
+        quality_label = f"{overall_quality} ({passed_count}/{total_count} passed)"
         end_process_time = time.time()
-        print(f"--- Evaluación completada en {end_process_time - start_process_time:.2f} segundos ---")
-
-        # Devolver DataFrame, Etiqueta y JSON
-        return df_results, quality_label, detailed_results
-
+        print(f"--- Evaluación completada en {end_process_time - start_process_time:.2f} seg ---")
+        # Devolver resultados y actualizar visibilidad
+        return (
+            gr.update(visible=False), # Oculta bienvenida
+            gr.update(visible=True),  # Muestra resultados
+            image_pil,                # Muestra imagen procesada
+            gr.update(value=quality_label), # Actualiza etiqueta
+            df_results,               # Actualiza dataframe
+            detailed_results          # Actualiza JSON
+        )
     except Exception as e:
-        print(f"Error durante el procesamiento de la imagen en Gradio: {e}")
-        traceback.print_exc()
-        # Lanzar un gr.Error para mostrarlo en la UI de Gradio
-        raise gr.Error(f"Error procesando la imagen: {str(e)}")
-
-
-# --- Definir la Interfaz Gradio ---
-css = """
-#quality-label label {
-    font-size: 1.1em;
-    font-weight: bold;
-}
-"""
-with gr.Blocks(css=css) as demo:
-    gr.Markdown(
-        """
-        #  Chest X-ray Technical Quality Assessment
-        Upload a chest X-ray image (PNG, JPG, etc.) to evaluate its technical quality based on 7 standard criteria
-        using the ELIXR model family (comparative strategy: Positive vs Negative prompts).
-        **Note:** Model loading on startup might take a minute. Processing an image can take 10-30 seconds depending on server load.
-        """
+        print(f"Error durante procesamiento Gradio: {e}"); traceback.print_exc()
+        raise gr.Error(f"Error procesando imagen: {str(e)}")
+
+# --- Función para Resetear la UI ---
+def reset_ui():
+    print("Reseteando UI...")
+    return (
+        gr.update(visible=True),  # Muestra bienvenida
+        gr.update(visible=False), # Oculta resultados
+        None,                     # Borra imagen de entrada
+        None,                     # Borra imagen de salida
+        gr.update(value="N/A"),   # Borra etiqueta
+        pd.DataFrame(),           # Borra dataframe
+        None                      # Borra JSON
     )
+
+# --- Definir Tema Oscuro Personalizado ---
+# Inspirado en los colores del HTML original y Tailwind dark grays/blues
+dark_theme = gr.themes.Default(
+    primary_hue=gr.themes.colors.blue,       # Azul como color primario
+    secondary_hue=gr.themes.colors.blue,     # Azul secundario
+    neutral_hue=gr.themes.colors.gray,       # Gris neutro
+    font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans-serif"],
+    font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "ui-monospace", "Consolas", "monospace"],
+).set(
+    # Fondos
+    body_background_fill="#111827",          # Fondo principal muy oscuro (gray-900)
+    background_fill_primary="#1f2937",       # Fondo de componentes (gray-800)
+    background_fill_secondary="#374151",     # Fondo secundario (gray-700)
+    block_background_fill="#1f2937",         # Fondo de bloques (gray-800)
+
+    # Texto
+    body_text_color="#d1d5db",               # Texto principal claro (gray-300)
+    text_color_subdued="#9ca3af",            # Texto secundario (gray-400)
+    block_label_text_color="#d1d5db",        # Etiquetas de bloque (gray-300)
+    block_title_text_color="#ffffff",        # Títulos de bloque (blanco)
+
+    # Bordes
+    border_color_accent="#374151",           # Borde (gray-700)
+    border_color_primary="#4b5563",          # Borde primario (gray-600)
+
+    # Botones y Elementos Interactivos
+    button_primary_background_fill="*primary_600", # Usa color primario (azul)
+    button_primary_text_color="#ffffff",
+    button_secondary_background_fill="*neutral_700",
+    button_secondary_text_color="#ffffff",
+    input_background_fill="#374151",         # Fondo de inputs (gray-700)
+    input_border_color="#4b5563",            # Borde de inputs (gray-600)
+    input_text_color="#ffffff",              # Texto en inputs
+
+    # Sombras y Radios
+    shadow_drop="rgba(0,0,0,0.2) 0px 2px 4px",
+    block_shadow="rgba(0,0,0,0.2) 0px 2px 5px",
+    radius_size="*radius_lg",                # Bordes redondeados
+)
+
+
+# --- Definir la Interfaz Gradio con Bloques y Tema ---
+with gr.Blocks(theme=dark_theme, title="CXR Quality Assessment") as demo:
+    # --- Cabecera ---
     with gr.Row():
-        with gr.Column(scale=1):
-            input_image = gr.Image(type="pil", label="Upload Chest X-ray")
-            submit_button = gr.Button("Assess Quality", variant="primary")
+        gr.Markdown(
+            """
+            # <span style="color: #e5e7eb;">CXR Quality Assessment</span>
+            <p style="color: #9ca3af;">Evaluate chest X-ray technical quality using AI (ELIXR family)</p>
+            """, # Usar blanco/gris claro para texto cabecera
+            elem_id="app-header"
+        )
+
+    # --- Contenido Principal (Dos Columnas) ---
+    with gr.Row(equal_height=False): # Permitir alturas diferentes
+
+        # --- Columna Izquierda (Carga) ---
+        with gr.Column(scale=1, min_width=350):
+            gr.Markdown("### 1. Upload Image", elem_id="upload-title")
+            input_image = gr.Image(type="pil", label="Upload Chest X-ray", height=300) # Altura fija para imagen entrada
+            with gr.Row():
+                 analyze_btn = gr.Button("Analyze Image", variant="primary", scale=2)
+                 reset_btn = gr.Button("Reset", variant="secondary", scale=1)
             # Añadir ejemplos si tienes imágenes de ejemplo
-            # Asegúrate de que la carpeta 'examples' exista y contenga las imágenes
             # gr.Examples(
-            #     examples=[os.path.join("examples", "sample_cxr.png")], # Lista de rutas a ejemplos
-            #     inputs=input_image
+            #     examples=[os.path.join("examples", "sample_cxr.png")],
+            #     inputs=input_image, label="Example CXR"
             # )
+            gr.Markdown(
+                "<p style='color:#9ca3af; font-size:0.9em;'>Model loading on startup takes ~1 min. Analysis takes ~15-40 sec.</p>"
+            )
+
+
+        # --- Columna Derecha (Bienvenida / Resultados) ---
         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
+
+            # --- Bloque de Bienvenida (Visible Inicialmente) ---
+            with gr.Column(visible=True, elem_id="welcome-section") as welcome_block:
+                gr.Markdown(
+                    """
+                    ### Welcome!
+                    Upload a chest X-ray image (PNG, JPG, etc.) on the left panel and click "Analyze Image".
+
+                    The system will evaluate its technical quality based on 7 standard criteria using the ELIXR model family.
+                    The results will appear here once the analysis is complete.
+                    """, elem_id="welcome-text"
                 )
-            output_json = gr.JSON(label="Raw Results (for debugging)")
-            
-    # Conectar el botón a la función de procesamiento
-    submit_button.click(
-        fn=assess_quality,
-        inputs=input_image,
-        outputs=[output_dataframe, output_label, output_json]
+                # Podrías añadir un icono o imagen aquí si quieres
+                # gr.Image("path/to/welcome_icon.png", interactive=False, show_label=False, show_download_button=False)
+
+
+            # --- Bloque de Resultados (Oculto Inicialmente) ---
+            with gr.Column(visible=False, elem_id="results-section") as results_block:
+                gr.Markdown("### 2. Quality Assessment Results", elem_id="results-title")
+                with gr.Row(): # Fila para imagen de salida y resumen
+                     with gr.Column(scale=1):
+                          output_image = gr.Image(type="pil", label="Analyzed Image", interactive=False)
+                     with gr.Column(scale=1):
+                          gr.Markdown("#### Summary", elem_id="summary-title")
+                          output_label = gr.Label(value="N/A", label="Overall Quality Estimate", elem_id="quality-label")
+                          # Podríamos añadir más texto de resumen aquí si quisiéramos
+
+                gr.Markdown("#### Detailed Criteria Evaluation", elem_id="detailed-title")
+                output_dataframe = gr.DataFrame(
+                    headers=["Criterion", "Sim (+)", "Sim (-)", "Difference", "Assessment (Comp)", "Assessment (Simp)"],
+                    label=None, # Quitar etiqueta redundante
+                    wrap=True,
+                    # La altura ahora se maneja mejor automáticamente o con CSS
+                    # row_count=(7, "dynamic") # Mostrar 7 filas, permitir scroll si hay más
+                    max_rows=10, # Limitar filas visibles con scroll
+                    overflow_row_behaviour="show_ends", # Muestra inicio/fin al hacer scroll
+                    interactive=False, # No editable
+                    elem_id="results-dataframe"
+                )
+                with gr.Accordion("Raw JSON Output (for debugging)", open=False):
+                      output_json = gr.JSON(label=None)
+
+                gr.Markdown(
+                    f"""
+                    #### Technical Notes
+                    *   **Criterion:** Quality aspect evaluated.
+                    *   **Sim (+/-):** Cosine similarity with positive/negative prompt.
+                    *   **Difference:** Sim (+) - Sim (-).
+                    *   **Assessment (Comp):** PASS if Difference > {SIMILARITY_DIFFERENCE_THRESHOLD}. (Main Result)
+                    *   **Assessment (Simp):** PASS if Sim (+) > {POSITIVE_SIMILARITY_THRESHOLD}.
+                    """, elem_id="notes-text"
+                )
+
+    # --- Pie de página ---
+    gr.Markdown(
+        """
+        ----
+        <p style='text-align:center; color:#9ca3af; font-size:0.8em;'>
+        CXR Quality Assessment Tool | Model: google/cxr-foundation | Interface: Gradio
+        </p>
+        """, elem_id="app-footer"
+        )
+
+
+    # --- Conexiones de Eventos ---
+    analyze_btn.click(
+        fn=assess_quality_and_update_ui,
+        inputs=[input_image],
+        outputs=[
+            welcome_block,      # -> actualiza visibilidad bienvenida
+            results_block,      # -> actualiza visibilidad resultados
+            output_image,       # -> muestra imagen analizada
+            output_label,       # -> actualiza etiqueta resumen
+            output_dataframe,   # -> actualiza tabla
+            output_json         # -> actualiza JSON
+        ]
     )
 
+    reset_btn.click(
+        fn=reset_ui,
+        inputs=None, # No necesita inputs
+        outputs=[
+            welcome_block,
+            results_block,
+            input_image,        # -> limpia imagen entrada
+            output_image,
+            output_label,
+            output_dataframe,
+            output_json
+        ]
+    )
+
+
 # --- Iniciar la Aplicación Gradio ---
-# Al desplegar en Spaces, Gradio se encarga de esto automáticamente.
-# Para ejecutar localmente: demo.launch()
-# Para Spaces, es mejor dejar que HF maneje el launch.
-# demo.launch(share=True) # Para obtener un link público temporal si corres localmente
 if __name__ == "__main__":
-     # share=True solo si quieres un enlace público temporal desde local
-     # server_name="0.0.0.0" para permitir conexiones de red local
+     # server_name="0.0.0.0" para accesibilidad en red local
      # server_port=7860 es el puerto estándar de HF Spaces
-     demo.launch(server_name="0.0.0.0", server_port=7860)
+     # auth=("user", "password") # Si quieres añadir autenticación básica localmente
+     demo.launch(server_name="0.0.0.0", server_port=7860) #, share=True) # Quita share=True para despliegue normal