Update app.py

app.py

@@ -16,7 +16,6 @@ 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 = 0.1
 POSITIVE_SIMILARITY_THRESHOLD = 0.1
 print(f"Usando umbrales: Comp Δ={SIMILARITY_DIFFERENCE_THRESHOLD}, Simp τ={POSITIVE_SIMILARITY_THRESHOLD}")
@@ -31,946 +30,323 @@ 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) # Asume que bert_preprocessor_global está cargado
-
+# --- Funciones Auxiliares ---
 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 =Por supuesto! Aquí está el código completo del archivo `app.py` para out['input_mask'].numpy().astype(np.float32)
+    masks = out['input_mask'].numpy().astype(np.float32)
     paddings = 1.0 - masks
-
-    # Reemplazar token [SEP] (102) por 0 y marcar Gradio con la corrección del tema oscuro (eliminando `text_color_subdued`).
-
- como padding
-    end_token_idx = (ids == 10```python
-import gradio as gr
-import os
-import io
-import png
-import tensorflow as tf2)
+    end_token_idx = (ids == 102)
     ids[end_token_idx] = 0
-
-import tensorflow_text as tf_text
-import tensorflow_hub as tf    paddings[end_token_idx] = 1.0_hub
-import numpy as np
-from PIL import Image
-from huggingface_hub import snapshot_download,
-
-    # Asegurar las dimensiones (B, T, S) -> ( HfFolder
-from sklearn.metrics.pairwise import cosine_similarity
-import1, 1, 128)
-    # El preprocesador puede devolver (1, 128), necesitamos (1, 1, 12 traceback
-import time
-import pandas as pd # Para formatear la salida en tabla
-
-# --- Configuración ---8)
+    paddings[end_token_idx] = 1.0
     if ids.ndim == 2: ids = np.expand_dims(ids, axis=1)
-    if paddings.
-MODEL_REPO_ID = "google/cxr-foundation"
-ndim == 2: paddings = np.expand_dims(paddMODEL_DOWNLOAD_DIR = './hf_cxr_foundation_space'ings, axis=1)
-
-    # Verificar formas finales
-    expected_shape = (1 # Directorio dentro del contenedor del Space
-SIMILARITY_DIFFERENCE_THRESHOLD = , 1, 128)
+    if paddings.ndim == 2: paddings = np.expand_dims(paddings, axis=1)
+    expected_shape = (1, 1, 128)
     if ids.shape != expected_shape:
-         # Intentar reajustar si es necesario (puede0.1
-POSITIVE_SIMILARITY_THRESHOLD = 0.1 pasar con algunas versiones)
-         if ids.shape == (1,1
-print(f"Usando umbrales: Comp Δ={SIMILAR28): ids = np.expand_dims(ids, axis=1ITY_DIFFERENCE_THRESHOLD}, Simp τ={POSITIVE_SIMILARITY_THRESHOLD}")
-
-# --- Prompts ---
-criteria_list_positive)
-         else: raise ValueError(f"Shape incorrecta para ids: = [
-    "optimal centering", "optimal inspiration", "optimal penetration",
-    "complete field of view {ids.shape}, esperado {expected_shape}")
-    if paddings", "scapulae retracted", "sharp image", "artifact free"
-].shape != expected_shape:
-         if paddings.shape == (
-criteria_list_negative = [
-    "poorly centered", "1,128): paddings = np.expand_dims(paddings, axis=1)poor inspiration", "non-diagnostic exposure",
-    "cropped image", "scapulae overlying lungs
+         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
 
-", "blurred image", "obscuring artifact"
-]
-
-# --- Funciones Auxiliadef png_to_tfexample(image_array: np.ndarray) -> tf.train.Example:
-    """Crea tf.train.Example desde NumPy array (res (Integradas o adaptadas) ---
-def bert_tokenize(text, preprocessor):
-escala de grises)."""
-    if image_array.ndim ==    """Tokeniza texto usando el preprocesador BERT cargado globalmente."""
-    if 3 and image_array.shape[2] == 1:
- preprocessor is None: raise ValueError("BERT preprocessor no está cargado.")        image_array = np.squeeze(image_array, axis=2) # Asegurar 2D
+def png_to_tfexample(image_array: np.ndarray) -> tf.train.Example:
+    if image_array.ndim == 3 and image_array.shape[2] == 1:
+        image_array = np.squeeze(image_array, axis=2)
     elif image_array.ndim != 2:
-        raise ValueError(f'Array debe ser 2-D (
-    if not isinstance(text, str): text = str(text)escala de grises). Dimensiones actuales: {image_array.ndim
-
-    out = preprocessor(tf.constant([text.lower()]))}')
-
+        raise ValueError(f'Array debe ser 2-D. Dimensiones: {image_array.ndim}')
     image = image_array.astype(np.float32)
-    min
-    ids = out['input_word_ids'].numpy().astype(_val = image.min()
-    max_val = image.max()
-
-    np.int32)
-    masks = out['input_mask'].# Evitar división por cero si la imagen es constante
-    if max_val <= min_val:numpy().astype(np.float32)
-    paddings =
-        # Si es constante, tratar como uint8 si el rango original lo permitía,
- 1.0 - masks
-    end_token_idx = (ids == 102)
-        # o simplemente ponerla a 0 si es float.
-        if image_array.    ids[end_token_idx] = 0
-    paddings[end_token_idx] = 1.0
-
-    if ids.ndim == 2dtype == np.uint8 or (min_val >= 0 and max: ids = np.expand_dims(ids, axis=1)
-    if paddings.ndim == 2: paddings = np.expand_val <= 255):
-             pixel_array = image._dims(paddings, axis=1)
-
-    expected_shape = (1,astype(np.uint8)
-             bitdepth = 8
-         1, 128)
-    if ids.shape != expectedelse: # Caso flotante constante o fuera de rango uint8
-             pixel__shape:
-         if ids.shape == (1,128): ids = np.expand_dims(ids, axis=1)
-         else: raise ValueErrorarray = np.zeros_like(image, dtype=np.uint1(f"Shape incorrecta para ids: {ids.shape}, esperado {6)
-             bitdepth = 16
+    min_val, max_val = image.min(), image.max()
+    if max_val <= min_val:
+        if image_array.dtype == np.uint8 or (min_val >= 0 and max_val <= 255):
+             pixel_array = image.astype(np.uint8); bitdepth = 8
+        else:
+             pixel_array = np.zeros_like(image, dtype=np.uint16); bitdepth = 16
     else:
-        expected_shape}")
-    if paddings.shape != expected_shape:image -= min_val # Mover mínimo a cero
-        current_max = max_val -
-         if paddings.shape == (1,128): padd min_val
-        # Escalar a 16-bit para mayor precisión si noings = np.expand_dims(paddings, axis=1) era uint8 originalmente
+        image -= min_val
+        current_max = max_val - min_val
         if image_array.dtype != np.uint8:
-         else: raise ValueError(f"Shape incorrecta para paddings:
             image *= 65535 / current_max
-            pixel_array = {paddings.shape}, esperado {expected_shape}")
-
-    return ids, paddings
-
- image.astype(np.uint16)
-            bitdepth = def png_to_tfexample(image_array: np.ndarray)16
-        else:
-            # Si era uint8, mantener el rango y tipo
-            # La resta del min ya la dejó en [0, current_max]
- -> tf.train.Example:
-    """Crea tf.train.Example desde NumPy array (            # Escalar a 255 si es necesario
-            image *= 255 / current_escala de grises)."""
-    if image_array.ndim ==max
-            pixel_array = image.astype(np.uint8) 3 and image_array.shape[2] == 1:
-
-            bitdepth = 8
-
-    # Codificar como PNG
-    output = io.Bytes        image_array = np.squeeze(image_array, axis=2IO()
-    png.Writer(
-        width=pixel_array.) # Asegurar 2D
-    elif image_array.ndim != 2shape[1],
-        height=pixel_array.shape[0],:
-        raise ValueError(f'Array debe ser 2-D (
-        greyscale=True,
-        bitdepth=bitdepth
-    escala de grises). Dimensiones actuales: {image_array.ndim).write(output, pixel_array.tolist())
-    png_bytes = output.getvalue()
-
-}')
-
-    image = image_array.astype(np.float32)
-    min_val    # Crear tf.train.Example
-    example = tf.train.Example()
-, max_val = image.min(), image.max()
-
-    if    features = example.features.feature
-    features['image/encoded']. max_val <= min_val: # Imagen constante
-        if image_array.dtype == np.uint8 or (min_val >= 0 and max_bytes_list.value.append(png_bytes)
-    features['image/format'].bytes_list.value.append(b'png')
-    return example
-
-def generate_image_embedding(img_np,val <= 255):
-             pixel_array = image.astype(np.uint8); bitdepth = 8
+            pixel_array = image.astype(np.uint16); bitdepth = 16
         else:
-             pixel_array = np.zeros_like(image elixrc_infer, qformer_infer):
-    """Genera embedding final, dtype=np.uint16); bitdepth = 16
-    else: # Imagen con rango
-        image -= min_val
-        current_max = max_val - min de imagen."""
-    if elixrc_infer is None or qformer_infer is None:
-        raise ValueError("Modelos ELIXR-C o Q_val
-        if image_array.dtype != np.uint8: #Former no cargados.")
-
-    try:
-        # 1. EL Escalar a 16-bit si no era uint8
-            image *= 6IXR-C
-        serialized_img_tf_example = png_5535 / current_max
-            pixel_array = image.to_tfexample(img_np).SerializeToString()
-        elixrc_output = elixrcastype(np.uint16); bitdepth = 16
-        _infer(input_example=tf.constant([serialized_img_tf_example]))else: # Mantener rango uint8
             image *= 255 / current_max
-            pixel_array = image.astype(np.uint
-        elixrc_embedding = elixrc_output['feature_maps_0'].numpy()
-8); bitdepth = 8
-
+            pixel_array = image.astype(np.uint8); bitdepth = 8
     output = io.BytesIO()
-    png.Writer(width=pixel_array.shape[1], height=pixel_array.shape        print(f"  Embedding ELIXR-C shape: {elixrc_embedding.[0], greyscale=True, bitdepth=bitdepth).write(shape}")
-
-        # 2. QFormer (Imagen)
-        qformer_input_output, pixel_array.tolist())
-    png_bytes = output.getvalue()
-
+    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.img = {
-            'image_feature': elixrc_embedding.tolist(),
-append(png_bytes)
-    features['image/format'].bytes_            'ids': np.zeros((1, 1, 12list.value.append(b'png')
+    features['image/encoded'].bytes_list.value.append(output.getvalue())
+    features['image/format'].bytes_list.value.append(b'png')
     return example
 
-def8), dtype=np.int32).tolist(), # Texto vacío
-            'paddings': generate_image_embedding(img_np, elixrc_infer, np.ones((1, 1, 128), dtype= qformer_infer):
-    """Genera embedding final de imagen."""
-    if elixnp.float32).tolist(), # Todo padding
-        }
-        qformer_output_img = qformer_infer(**qformer_input_imgrc_infer is None or qformer_infer is None: raise ValueError(")
-        image_embedding = qformer_output_img['all_contrastive_imgModelos ELIXR-C o QFormer no cargados.")
-    _emb'].numpy()
-
-        # Ajustar dimensiones si es necesario
-        if image_try:
-        # 1. ELIXR-C
-        serialized_embedding.ndim > 2:
-            print(f"  Ajustimg_tf_example = png_to_tfexample(img_npando dimensiones embedding imagen (original: {image_embedding.shape})")
-).SerializeToString()
-        elixrc_output = elixrc_infer(            image_embedding = np.mean(
-                image_embedding,
-input_example=tf.constant([serialized_img_tf_example]))                axis=tuple(range(1, image_embedding.ndim - 
-        elixrc_embedding = elixrc_output['feature_maps_0'].numpy1))
-            )
-            if image_embedding.ndim == 1()
-        print(f"  Embedding ELIXR-C shape: {elixrc_embedding.:
-                image_embedding = np.expand_dims(image_embedding, axis=0)
-        elif image_embedding.ndim == 1:
-shape}")
-
-        # 2. QFormer (Imagen)
-        qformer_input_             image_embedding = np.expand_dims(image_embedding, axis=0) # Asegurar 2D
-
-        print(f"  Embedding final imagen shape: {image_embedding.shape}")
-        if image_embedding.ndimimg = {
+def generate_image_embedding(img_np, elixrc_infer, qformer_infer):
+    if elixrc_infer is None or qformer_infer is None: raise ValueError("Modelos ELIXR-C o QFormer no cargados.")
+    try:
+        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()
+        qformer_input_img = {
             'image_feature': elixrc_embedding.tolist(),
- != 2:
-            raise ValueError(f"Embedding final de imagen no tiene 2 dimensiones: {            'ids': np.zeros((1, 1, 12image_embedding.shape}")
-        return image_embedding
-
-    except Exception8), dtype=np.int32).tolist(), # Texto vacío
-            'paddings as e:
-        print(f"Error generando embedding de imagen: {e}")
-        ': np.ones((1, 1, 128), dtype=np.floattraceback.print_exc()
-        raise # Re-lanzar32).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_ la excepción para que Gradio la maneje
-
-def calculate_similarities_and_classify(infer(**qformer_input_img)
-        image_embedding = qformer_output_image_embedding, bert_preprocessor, qformer_infer):
-    img['all_contrastive_img_emb'].numpy()
-
-        # Ajustar dimensiones
-        if"""Calcula similitudes y clasifica."""
-    if image_embedding is None: raise ValueError("Embedding image_embedding.ndim > 2:
-            print(f"  Ajustando de imagen es None.")
-    if bert_preprocessor is None: raise ValueError("Preprocesador BERT es dimensiones embedding imagen (original: {image_embedding.shape})")
-            image_embedding = np.mean(image_embedding, axis=tuple( None.")
-    if qformer_infer is None: raise ValueError("Qrange(1, image_embedding.ndim - 1)))
-        if image_embedding.ndim == Former es None.")
-    detailed_results = {}
-    print("\n--- Calculando similitudes y clasific1: image_embedding = np.expand_dims(image_embedding,ando ---")
-
-    for i in range(len(criteria_list_positive)):
-         axis=0) # Asegurar 2D
-        print(f"  Embedding final imagen shapepositive_text = criteria_list_positive[i]
-        negative_: {image_embedding.shape}")
-        if image_embedding.ndimtext = criteria_list_negative[i]
-        criterion_name = != 2: raise ValueError(f"Embedding final imagen no tiene 2 dims positive_text # Usar prompt positivo como clave
-
-        print(f": {image_embedding.shape}")
+        qformer_output_img = qformer_infer(**qformer_input_img)
+        image_embedding = qformer_output_img['all_contrastive_img_emb'].numpy()
+        if image_embedding.ndim > 2:
+            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:
-Procesando criterio: \"{criterion_name}\"")
-        similarity_positive, similarity        print(f"Error generando embedding imagen: {e}"); traceback.print_exc(); raise
-
-def calculate_similarities_and_classify(image_embedding, bert_preprocessor_negative, difference = None, None, None
-        classification_comp, classification_simp = "ERROR", "ERROR"
+        print(f"Error generando embedding imagen: {e}"); traceback.print_exc(); raise
 
-        try:
-            #, qformer_infer):
-    """Calcula similitudes y clasifica."""
+def calculate_similarities_and_classify(image_embedding, bert_preprocessor, qformer_infer):
     if image_embedding is None: raise ValueError("Embedding imagen es None.")
-    if bert_ 1. Embedding Texto Positivo
-            tokens_pos, paddings_pos = bert_tokenize(preprocessor is None: raise ValueError("Preprocesador BERT es None.")
-    if qformer_positive_text, bert_preprocessor)
-            qformer_input_infer is None: raise ValueError("QFormer 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 ---")
-    for i intext_pos = {
-                'image_feature': np.zeros([ range(len(criteria_list_positive)):
-        positive_text,1, 8, 8, 1376], dtype= negative_text = criteria_list_positive[i], criteria_list_np.float32).tolist(), # Dummy
-                'ids': tokensnegative[i]
-        criterion_name = positive_text # Usar prompt positivo_pos.tolist(), 'paddings': paddings_pos.tolist(),
-            }
-            text como clave
-        print(f"Procesando criterio: \"{criterion_name}\"_embedding_pos = qformer_infer(**qformer_input_text")
+    print("\n--- Calculando similitudes ---")
+    for i in range(len(criteria_list_positive)):
+        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__pos)['contrastive_txt_emb'].numpy()
-            if text_embedding_poscomp, classification_simp = "ERROR", "ERROR"
-        try:.ndim == 1: text_embedding_pos = np.expand_
-            # 1. Embeddings de Texto
-            tokens_pos, paddings_pos = bert_tokenize(positive_text, bert_preprocessordims(text_embedding_pos, axis=0)
-
-            # )
-            qformer_input_pos = {'image_feature': np2. Embedding Texto Negativo
-            tokens_neg, paddings_neg.zeros([1, 8, 8, 1376 = bert_tokenize(negative_text, bert_preprocessor)
-            qformer_input_text], dtype=np.float32).tolist(), 'ids': tokens_pos.tolist(), 'padd_neg = {
-                'image_feature': np.zeros([1ings': paddings_pos.tolist()}
-            text_embedding_pos = qformer_infer(**qformer_input_pos)['contrastive_txt_emb'].numpy(), 8, 8, 1376], dtype=np
-            if text_embedding_pos.ndim == 1: text_embedding_pos = np.expand_dims(text_embedding_pos, axis=0).float32).tolist(), # Dummy
-                'ids': tokens_neg.tolist(), 'paddings': paddings_neg.tolist(),
+        classification_comp, classification_simp = "ERROR", "ERROR"
+        try:
+            tokens_pos, paddings_pos = bert_tokenize(positive_text, bert_preprocessor)
+            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)
 
             tokens_neg, paddings_neg = bert_tokenize(negative_text, bert_preprocessor)
-            qformer_input_neg
-            }
-            text_embedding_neg = qformer_infer(** = {'image_feature': np.zeros([1, 8, qformer_input_text_neg)['contrastive_txt_emb'].numpy()
-            if text_embedding_neg.ndim == 1: text_embedding_neg8, 1376], dtype=np.float32). = np.expand_dims(text_embedding_neg, axis=0tolist(), 'ids': tokens_neg.tolist(), 'paddings':)
-
-            # Verificar compatibilidad de dimensiones para similitud
-            if image_embedding paddings_neg.tolist()}
-            text_embedding_neg = qformer_infer(**qformer_input_neg)['contrastive_txt_.shape[1] != text_embedding_pos.shape[1]:emb'].numpy()
-            if text_embedding_neg.ndim == 
-                 raise ValueError(f"Dimensión incompatible: Imagen ({image_embedding.shape[11: text_embedding_neg = np.expand_dims(text_embedding_neg, axis=0)
-
-            # Verificar dimensiones
-            if image_embedding.shape]}) vs Texto Pos ({text_embedding_pos.shape[1]})")
-            if[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 image_embedding.shape[1] != text_embedding_neg.shape.shape[1] != text_embedding_neg.shape[1]: raise ValueError(f"Dim mismatch: Img ({image_embedding.shape[1]:
-                 raise ValueError(f"Dimensión incompatible: Imagen ({image_embedding.shape[1[1]}) vs Neg ({text_embedding_neg.shape[1]})")
+            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)
 
-            # 2. Calcular Similitudes
-            similarity_positive = cosine_similarity(image]}) vs Texto Neg ({text_embedding_neg.shape[1]})")_embedding, text_embedding_pos)[0][0]
-            similarity_negative =
+            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 cosine_similarity(image_embedding, text_embedding_neg)[0][, text_embedding_pos)[0][0]
-            similarity_negative0]
-
-            # 3. Clasificar
-            difference = similarity_positive - similarity = cosine_similarity(image_embedding, text_embedding_neg)[0_negative
-            classification_comp = "PASS" if difference > SIMILARITY_DIFFERENCE][0]
-            print(f"  Sim (+)={similarity_positive_THRESHOLD else "FAIL"
-            classification_simp = "PASS" if:.4f}, Sim (-)={similarity_negative:.4f}")
-
-             similarity_positive > POSITIVE_SIMILARITY_THRESHOLD else "FAIL"# 4. Clasificar
-            difference = similarity_positive - similarity_
-            print(f"  Sim(+)={similarity_positive:.4f},negative
-            classification_comp = "PASS" if difference > SIMILARITY_DIFFERENCE Sim(-)={similarity_negative:.4f}, Diff={difference:.4f_THRESHOLD else "FAIL"
-            classification_simp = "PASS" if} -> Comp:{classification_comp}, Simp:{classification_simp}")
-        except Exception as similarity_positive > POSITIVE_SIMILARITY_THRESHOLD else "FAIL" e:
-            print(f"  ERROR procesando criterio '{criterion_name}': {e}"); traceback.print_exc()
-            # Mantener clasificaciones como "ERROR
-            print(f"  Diff={difference:.4f} -> Comp: {classification_comp},"
-        detailed_results[criterion_name] = {
-            'positive_prompt': Simp: {classification_simp}")
+            similarity_positive = cosine_similarity(image_embedding, text_embedding_pos)[0][0]
+            similarity_negative = cosine_similarity(image_embedding, text_embedding_neg)[0][0]
 
+            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"  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" positive_text, 'negative_prompt': negative_text,
-            'similarity_positive': float(similarity_positive) if similarity_positive is not None else None,
-
-
-        # Guardar resultados
+            print(f"  ERROR criterio '{criterion_name}': {e}"); traceback.print_exc()
         detailed_results[criterion_name] = {
-                        'similarity_negative': float(similarity_negative) if similarity_negative'positive_prompt': positive_text,
-            'negative_prompt': is not None else None,
-            'difference': float(difference) if negative_text,
-            'similarity_positive': float(similarity_positive difference is not None else None,
-            'classification_comparative': classification) if similarity_positive is not None else None,
-            'similarity__comp, 'classification_simplified': classification_simp
+            '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
         }
-    return detailed_resultsnegative': float(similarity_negative) if similarity_negative is not None else None,
-            'difference': float(difference) if difference is not None
+    return detailed_results
 
 # --- Carga Global de Modelos ---
-print("--- Iniciando carga global de modelos else None,
-            'classification_comparative': classification_comp,
- ---")
+print("--- Iniciando carga global de modelos ---")
 start_time = time.time()
 models_loaded = False
 bert_preprocessor_global = None
-elixrc_infer            'classification_simplified': classification_simp
-        }
-    return detailed_results
-
-# ---_global = None
-qformer_infer_global = None
-try: Carga Global de Modelos ---
-# Se ejecuta UNA VEZ al iniciar la
-    hf_token = os.environ.get("HF_TOKEN") # Leer aplicación Gradio/Space
-print("--- Iniciando carga global de modelos ---")
-start_ token desde secretos del Space
-    if hf_token: print("HFtime = time.time()
-models_loaded = False
-bert_pre_TOKEN encontrado, usando para autenticación.")
-
-    os.makedirs(MODEL_DOWNLOADprocessor_global = None
 elixrc_infer_global = None
-_DIR, exist_ok=True)
-    print(f"Descargando/verificando modelos en: {MODEL_DOWNLOAD_DIR}")qformer_infer_global = None
-
+qformer_infer_global = None
 try:
-    # Añadir token si
+    hf_token = os.environ.get("HF_TOKEN") # Leer token desde secretos del Space
+    if hf_token: print("Usando HF_TOKEN para autenticación.")
+
+    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 es necesario (para repos privados o gated)
-    hf_token = os.environ.get("-c-v2-pooled/*', 'pax-elixr-b-text/*'],
-                      local_dir_use_symlinks=False, token=hf_token) # Pasar token aquí
+                      allow_patterns=['elixr-c-v2-pooled/*', 'pax-elixr-b-text/*'],
+                      local_dir_use_symlinks=False, token=hf_token) # Pasar token
     print("Modelos descargados/verificados.")
 
-HF_TOKEN") # Leer token desde secretos del Space
-    # if hf_token:
-        print("Cargando Preprocesador BERT...")
-    bert_preprocess#     print("Usando HF_TOKEN para autenticación.")
-    #     # HfFolder.save_token(hf_token) # Esto no siempre funciona bien en entornos server_handle = "https://tfhub.dev/tensorflow/bert_enless
-
-    # Crear directorio si no existe
-    os.makedirs(MODEL_DOWNLOAD_DIR_uncased_preprocess/3"
-    bert_preprocessor_global, exist_ok=True)
-    print(f"Descargando/verificando modelos en = tf_hub.KerasLayer(bert_preprocess_handle)
-    print("Preprocesador BERT: {MODEL_DOWNLOAD_DIR}")
-    snapshot_download(repo_id=MODEL cargado.")
-
-    print("Cargando ELIXR-C...")_REPO_ID, local_dir=MODEL_DOWNLOAD_DIR,
+    print("Cargando Preprocesador BERT...")
+    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.")
 
-    elixrc_model_path = os.path.join(                      allow_patterns=['elixr-c-v2-pooled/*', 'pax-elixrMODEL_DOWNLOAD_DIR, 'elixr-c-v2--b-text/*'],
-                      local_dir_use_symlinkspooled')
-    elixrc_model = tf.saved_model.=False, # Evitar symlinks
-                      token=hf_token) # Pasar tokenload(elixrc_model_path)
+    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 aquí
-    print("Modelos descargados/verificados.")
+    print("Modelo ELIXR-C cargado.")
 
-    # C ELIXR-C cargado.")
-
-    print("Cargando Qargar Preprocesador BERT desde TF Hub
-    print("Cargando Preprocesador BERT...")
-    Former (ELIXR-B Text)...")
-    qformer_model_path = os.path.join(MODEL_DOWNLOAD_DIR, '# Usar handle explícito puede ser más robusto en algunos entornos
-    bert_preprocess_pax-elixr-b-text')
-    qformer_handle = "https://tfhub.dev/tensorflow/bert_en_model = tf.saved_model.load(qformer_model_pathuncased_preprocess/3"
-    bert_preprocessor_global =)
+    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)
     qformer_infer_global = qformer_model.signatures['serving_default']
-     tf_hub.KerasLayer(bert_preprocess_handle)
     print("Modelo QFormer cargado.")
 
     models_loaded = True
-    end_print("Preprocesador BERT cargado.")
-
-    # Cargar ELIXR-C
-    print("Cargando ELIXR-C...")
-    elixrctime = time.time()
-    print(f"--- Modelos cargados global_model_path = os.path.join(MODEL_DOWNLOAD_DIRmente con éxito en {end_time - start_time:.2f}, 'elixr-c-v2-pooled')
-    el segundos ---")
+    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(ixrc_model = tf.saved_model.load(elixrcf"--- ERROR CRÍTICO DURANTE LA CARGA GLOBAL DE MODELOS_model_path)
-    elixrc_infer_global = el ---"); print(e); traceback.print_exc()
-
-# --- Función Principal de Procesamiento paraixrc_model.signatures['serving_default']
-    print("Modelo Gradio ---
-def assess_quality_and_update_ui(image ELIXR-C cargado.")
+    print(f"--- ERROR CRÍTICO DURANTE LA CARGA GLOBAL DE MODELOS ---"); print(e); traceback.print_exc()
 
-    # Cargar QFormer (_pil):
-    """Procesa la imagen y devuelve actualizaciones para la UI."""ELIXR-B Text)
-    print("Cargando QFormer
+# --- Función Principal de Procesamiento para Gradio ---
+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 (ELIXR-B Text)...")
-    qformer_model_ modelos no se pudieron cargar. La aplicación no puede procesar imágenes.")
-    if image_pil is Nonepath = os.path.join(MODEL_DOWNLOAD_DIR, 'p:
-        # Devuelve valores por defecto/vacíos y controla la visibilidad
+        raise gr.Error("Error: Los modelos no se pudieron cargar. La aplicación no puede procesar imágenes.")
+    if image_pil is None:
         return (
-ax-elixr-b-text')
-    qformer_model            gr.update(visible=True),  # Muestra bienvenida
-            gr.update(visible= = tf.saved_model.load(qformer_model_path)
-    qformer_infer_global = qformer_model.signatures['False), # Oculta resultados
-            None,                     # Borra imagen de salidaserving_default']
-    print("Modelo QFormer cargado.")
-
-    
+            gr.update(visible=True),  # Muestra bienvenida
+            gr.update(visible=False), # Oculta resultados
+            None,                     # Borra imagen de salida
             gr.update(value="N/A"),   # Borra etiqueta
-            pdmodels_loaded = True
-    end_time = time.time()
-.DataFrame(),           # Borra dataframe
+            pd.DataFrame(),           # Borra dataframe
             None                      # Borra JSON
         )
 
     print("\n--- Iniciando evaluación para nueva imagen ---")
-    start    print(f"--- Modelos cargados globalmente con éxito en {end_time_process_time = time.time()
+    start_process_time = time.time()
     try:
-        # - start_time:.2f} segundos ---")
-
-except Exception as e:
-    models_loaded = False
-    print(f"--- ERROR CRÍTICO DUR 1. Convertir a NumPy
-        img_np = np.arrayANTE LA CARGA GLOBAL DE MODELOS ---")
-    print(e)
-    traceback.print_(image_pil.convert('L'))
-        print(f"Imagenexc()
-    # Gradio se iniciará, pero la función de análisis fallará. convertida a NumPy. Shape: {img_np.shape}, Tipo:
-
-# --- Función Principal de Procesamiento para Gradio ---
-def assess_quality_and_ {img_np.dtype}")
-        # 2. Generar Embeddingupdate_ui(image_pil):
-    """Procesa la imagen y devuelve actualizaciones
-        print("Generando embedding de imagen...")
-        image_embedding = generate_image_embedding(img_np, elixrc_infer_global, q para la UI."""
-    if not models_loaded:
-        raise grformer_infer_global)
-        print("Embedding de imagen generado.")
-.Error("Error: Los modelos no se pudieron cargar. La aplicación no puede procesar imágenes.")
-        # 3. Clasificar
-        print("Calculando similitudes y clasificando criterios    if image_pil is None:
-        # Devuelve valores por defecto/vacíos...")
-        detailed_results = calculate_similarities_and_classify(image_embedding, bert_preprocessor_global, qformer_infer_ y controla la visibilidad
-        return (
-            gr.update(visible=Trueglobal)
-        print("Clasificación completada.")
-        # ),  # Muestra bienvenida
-            gr.update(visible=False), # Oculta resultados
-            4. Formatear Resultados
-        output_data, passed_count,None,                     # Borra imagen de salida
-            gr.update(value="N/A total_count = [], 0, 0
-        for criterion, details in detailed_results.items"),   # Borra etiqueta
-            pd.DataFrame(),           # Borra dataframe():
+        img_np = np.array(image_pil.convert('L'))
+        image_embedding = generate_image_embedding(img_np, elixrc_infer_global, qformer_infer_global)
+        detailed_results = calculate_similarities_and_classify(image_embedding, bert_preprocessor_global, qformer_infer_global)
+        output_data, passed_count, total_count = [], 0, 0
+        for criterion, details in detailed_results.items():
             total_count += 1
-            sim_pos = details
-            None                      # Borra JSON
-        )
-
-    print("\n--- Iniciando evaluación['similarity_positive']
-            sim_neg = details['similarity_negative para nueva imagen ---")
-    start_process_time = time.time']
+            sim_pos = details['similarity_positive']
+            sim_neg = details['similarity_negative']
             diff = details['difference']
             comp = details['classification_comparative']
             simp = details['classification_simplified']
-            ()
-    try:
-        # 1. Convertir a NumPy
-        img_np = np.array(image_pil.convert('Loutput_data.append([ criterion, f"{sim_pos:.4f}"'))
-        print(f"Imagen convertida a NumPy. Shape: {img_np.shape}, Tipo: {img_np.dtype}")
-
- if sim_pos is not None else "N/A",
-                f"{sim_neg:.        # 2. Generar Embedding de Imagen
-        print("Generando embedding4f}" if sim_neg is not None else "N/A", de imagen...")
-        image_embedding = generate_image_embedding(img f"{diff:.4f}" if diff is not None else "N/_np, elixrc_infer_global, qformer_infer_A", comp, simp ])
+            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
-global)
-        print("Embedding de imagen generado.")
-
-        # 3        df_results = pd.DataFrame(output_data, columns=[ "Criterion", "Sim (+)", "Sim (-)", "Difference", "Assessment (Comp)", "Assessment (Simp)" ])
-        overall_quality = "Error"; pass_. Calcular Similitudes y Clasificar
-        print("Calculando similitudesrate = 0
+        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:
-             y clasificando criterios...")
-        detailed_results = calculate_similarities_and_classify(pass_rate = passed_count / total_count
-            if pass_image_embedding, bert_preprocessor_global, qformer_infer_rate >= 0.85: overall_quality = "Excellent"
-            elif pass_rate >= global)
-        print("Clasificación completada.")
-
-        # 0.70: overall_quality = "Good"
-            elif pass4. Formatear Resultados para Gradio
-        output_data = []
-        passed_count = _rate >= 0.50: overall_quality = "Fair"0
-        total_count = 0
-        for criterion, details in detailed_results.items
+            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():
-            total_count += 1
-            sim_pos = details['similarity_positive']
-            sim_neg = details['similarity_negative = f"{overall_quality} ({passed_count}/{total_count}']
-            diff = details['difference']
-            comp = details['classification 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} seg_comparative']
-            simp = details['classification_simplified']
-             ---")
-        # Devolver resultados y actualizar visibilidad
+        print(f"--- Evaluación completada en {end_process_time - start_process_time:.2f} seg ---")
         return (
-            output_data.append([
-                criterion,
-                f"{sim_pos:.4f}"gr.update(visible=False), # Oculta bienvenida
+            gr.update(visible=False), # Oculta bienvenida
             gr.update(visible=True),  # Muestra resultados
-            image_pil,                # Muestra imagen if sim_pos is not None else "N/A",
-                f procesada
+            image_pil,                # Muestra imagen procesada
             gr.update(value=quality_label), # Actualiza etiqueta
             df_results,               # Actualiza dataframe
-            detailed"{sim_neg:.4f}" if sim_neg is not None else_results          # Actualiza JSON
-        )
-    except Exception as e "N/A",
-                f"{diff:.4f}" if diff:
-        print(f"Error durante procesamiento Gradio: {e}"); is not None else "N/A",
-                comp,
-                simp
-            ])
- traceback.print_exc()
-        raise gr.Error(f"Error procesando imagen: {str            if comp == "PASS":
-                passed_count += 1
-
-(e)}")
-
-# --- Función para Resetear la UI ---
-def reset_ui        # Crear DataFrame
-        df_results = pd.DataFrame(output_data, columns():
-    print("Reseteando UI...")
-    return (
-        gr.update(visible==[
-            "Criterion", "Sim (+)", "Sim (-)", "Difference", "Assessment (CompTrue),  # Muestra bienvenida
-        gr.update(visible=False), # Oculta resultados
-        None,                     # Borra imagen de)", "Assessment (Simp)"
-        ])
-
-        # Calcular etiqueta de calidad general
-        overall_quality entrada
-        None,                     # Borra imagen de salida
-        gr.update(value="N/A"),   # Borra etiqueta
-        pd = "Error"
-        pass_rate = 0
-        if total_count > 0:
-.DataFrame(),           # Borra dataframe
-        None                      # Borra JSON
-    )
-
-# --- Definir Tema Oscuro Personalizado ---
-# Inspirado en los colores del HTML original y            pass_rate = passed_count / total_count
-            if pass Tailwind dark grays/blues
-dark_theme = gr.themes.Default_rate >= 0.85: overall_quality = "Excellent"
-            elif pass_rate >=(
-    primary_hue=gr.themes.colors.blue,       # Azul como color primario
-    secondary_hue=gr.themes.colors.blue, 0.70: overall_quality = "Good"
-            elif     # Azul secundario
-    neutral_hue=gr.themes.colors pass_rate >= 0.50: overall_quality = "Fair.gray,       # Gris neutro
-    font=[gr.themes.GoogleFont("Inter"
-            else: overall_quality = "Poor"
-        quality_"), "ui-sans-serif", "system-ui", "sans-label = f"{overall_quality} ({passed_count}/{total_countserif"],
-    font_mono=[gr.themes.GoogleFont("Jet} passed)"
-
-        end_process_time = time.time()
-        print(f"---Brains Mono"), "ui-monospace", "Consolas", "monospace"],
- Evaluación completada en {end_process_time - start_process_time:.2f} segundos ---).set(
-    # Fondos
-    body_background_fill="#111827",          # Fondo principal muy oscuro (gray-900)
-    background_fill_primary="#1f2937",")
-
-        # Devolver resultados y actualizar visibilidad
-        return (
-                   # Fondo de componentes (gray-800)
-    background_fill_secondary="#3gr.update(visible=False), # Oculta bienvenida
-            gr.update(visible=74151",     # Fondo secundario (gray-700)
-    block_background_fill="#1f2937",         True),  # Muestra resultados
-            image_pil,                # Muestra imagen# Fondo de bloques (gray-800)
-
-    # Texto
- procesada
-            gr.update(value=quality_label), # Actualiza etiqueta
-            df    body_text_color="#d1d5db",               # Texto_results,               # Actualiza dataframe
             detailed_results          # Actualiza JSON
         )
     except Exception as e:
-        print(f"Error durante principal claro (gray-300)
-    # text_color_subdued="# procesamiento Gradio: {e}")
-        traceback.print_exc()
-        9ca3af",          # <-- LÍNEA PROBLEMÁTICA EL# Lanzar un gr.Error para mostrarlo en la UI de Gradio
+        print(f"Error durante procesamiento Gradio: {e}"); traceback.print_exc()
         raise gr.Error(f"Error procesando imagen: {str(e)}")
 
-
-# --- Función para ResetearIMINADA
-    block_label_text_color="#d1d5db",        # Etiquetas de bloque (gray-300)
-    block_title_text la UI ---
+# --- Función para Resetear la UI ---
 def reset_ui():
     print("Reseteando UI...")
     return (
         gr.update(visible=True),  # Muestra bienvenida
-_color="#ffffff",        # Títulos de bloque (blanco)
-
         gr.update(visible=False), # Oculta resultados
-            # Bordes
-    border_color_accent="#374151",None,                     # Borra imagen de entrada
-        None,                     # Bor           # Borde (gray-700)
-    border_colorra imagen de salida
+        None,                     # Borra imagen de entrada
+        None,                     # Borra imagen de salida
         gr.update(value="N/A"),   # Borra etiqueta
-        _primary="#4b5563",          # Borde primario (gray-pd.DataFrame(),           # Borra dataframe
+        pd.DataFrame(),           # Borra dataframe
         None                      # Borra JSON
     )
 
-600)
-
+# --- Definir Tema Oscuro Personalizado (CORREGIDO) ---
+dark_theme = gr.themes.Default(
+    primary_hue=gr.themes.colors.blue,
+    secondary_hue=gr.themes.colors.blue,
+    neutral_hue=gr.themes.colors.gray,
+    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",
+    background_fill_primary="#1f2937",
+    background_fill_secondary="#374151",
+    block_background_fill="#1f2937",
+    # Texto
+    body_text_color="#d1d5db",
+    # text_color_subdued="#9ca3af", # <-- Línea eliminada que causaba el error
+    block_label_text_color="#d1d5db",
+    block_title_text_color="#ffffff",
+    # Bordes
+    border_color_accent="#374151",
+    border_color_primary="#4b5563",
     # Botones y Elementos Interactivos
-    # --- Definir Tema Oscuro Personalizado (CORREGIDO) ---
-#button_primary_background_fill="*primary_600", # Usa color primario (azul)
+    button_primary_background_fill="*primary_600",
     button_primary_text_color="#ffffff",
-     Inspirado en los colores del HTML original y Tailwind dark grays/blues
-dark_button_secondary_background_fill="*neutral_700",
+    button_secondary_background_fill="*neutral_700",
     button_secondary_text_color="#ffffff",
-    input_background_fill="#3theme = gr.themes.Default(
-    primary_hue=gr.74151",         # Fondo de inputs (gray-700)
-    input_borderthemes.colors.blue,       # Azul como color primario
-    secondary_hue=gr.themes.colors.blue,     # Azul secundario
-    neutral_hue=gr_color="#4b5563",            # Borde de inputs (gray-.themes.colors.gray,       # Gris neutro
-    font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui", "sans600)
-    input_text_color="#ffffff",              # Texto en inputs
-
+    input_background_fill="#374151",
+    input_border_color="#4b5563",
+    input_text_color="#ffffff",
     # Sombras y Radios
-    shadow_drop="rgba(0,0,0,0-serif"],
-    font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "ui.2) 0px 2px 4px",
-    block-monospace", "Consolas", "monospace"],
-).set(
-    _shadow="rgba(0,0,0,0.2) # Fondos
-    body_background_fill="#111827",          0px 2px 5px",
-    radius_size="*# Fondo principal muy oscuro (gray-900)
-    background_fill_primaryradius_lg",                # Bordes redondeados
+    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",
 )
 
-
-# --- Definir la Interfaz Gradio con="#1f2937",       # Fondo de componentes (gray-800)
- Bloques y Tema ---
+# --- Definir la Interfaz Gradio con Bloques y Tema ---
 with gr.Blocks(theme=dark_theme, title="CXR Quality Assessment") as demo:
     # --- Cabecera ---
     with gr.Row():
         gr.Markdown(
             """
-            # <span style="color: #    background_fill_secondary="#374151",     #e5e7eb;">CXR Quality Assessment</span>
-            <p style Fondo secundario (gray-700)
-    block_background_="color: #9ca3af;">Evaluate chest X-ray technical quality usingfill="#1f2937",         # Fondo de bloques (gray-8 AI (ELIXR family)</p>
-            """,
-            elem_id="app-header00)
-
-    # Texto
-    body_text_color="#d1d5db",               #"
-        )
-
-    # --- Contenido Principal (Dos Columnas) ---
-    with gr Texto principal claro (gray-300)
-    # text_color_subdued.Row(equal_height=False): # Permitir alturas diferentes
-
-        # --- Columna Iz="#9ca3af",          # <--- ESTA LÍNEA CAUSABA EL ERROR Y FUE ELIMINADA/COMENTADA
-    block_label_text_color="#d1d5db",        # Etiquetas de bloque (gray-300quierda (Carga) ---
-        with gr.Column(scale=1,)
-    block_title_text_color="#ffffff",        # T min_width=350):
-            gr.Markdown("### ítulos de bloque (blanco)
-
-    # Bordes
-    border_1. Upload Image", elem_id="upload-title")
-            inputcolor_accent="#374151",           # Borde (gray-70_image = gr.Image(type="pil", label="Upload Chest X-ray", height=300)
-    border_color_primary="#4b55630) # Altura fija para imagen entrada
-            with gr.Row():
-                 ",          # Borde primario (gray-600)
-
-    analyze_btn = gr.Button("Analyze Image", variant="primary", scale=2)
-                 reset_btn = gr.Button("Reset", variant="secondary", scale=1)
-            ## Botones y Elementos Interactivos
-    button_primary_background_fill="*primary_600", # Usa color primario (azul)
-    button_primary_ Añadir ejemplos si tienes imágenes de ejemplo
-            # gr.Examples(
-            text_color="#ffffff",
-    button_secondary_background_fill="*neutral_700",#     examples=[os.path.join("examples", "sample_cx
-    button_secondary_text_color="#ffffff",
-    input_background_fill="#3r.png")],
-            #     inputs=input_image, label="Example CXR"
-            # )
-            gr.Markdown(
-                74151",         # Fondo de inputs (gray-700)
-    input_border_color="#4b5563",            # Borde de inputs (gray-"<p style='color:#9ca3af; font-size:0600)
-    input_text_color="#ffffff",              #.9em;'>Model loading on startup takes ~1 min. Analysis takes ~15-40 sec.</p>"
-            )
-
-
-        # --- Columna Derecha (Bienvenida / Resultados) ---
-         Texto en inputs
-
-    # Sombras y Radios
-    shadow_dropwith gr.Column(scale=2):
-
-            # --- Bloque de Bienvenida (Visible Inicialmente="rgba(0,0,0,0.2) 0px) ---
-            with gr.Column(visible=True, elem_id 2px 4px",
-    block_shadow="rgba(0,0="welcome-section") as welcome_block:
-                gr.Markdown(,0,0.2) 0px 2px 5px",
-    radius_size="*radius_lg",                # Bordes redondeados
-)
-
-
-
-                    """
-                    ### Welcome!
-                    Upload a chest X-ray image (# --- Definir la Interfaz Gradio con Bloques y Tema ---
-with gr.Blocks(themePNG, JPG, etc.) on the left panel and click "Analyze Image".=dark_theme, title="CXR Quality Assessment") as demo:
-
-
-                    The system will evaluate its technical quality based on 7 standard criteria using the ELIXR model family.    # --- Cabecera ---
-    with gr.Row():
-        gr.Markdown
-                    The results will appear here once the analysis is complete.
-                    """,(
-            """
-            # <span style="color: #e5e7eb;">CXR elem_id="welcome-text"
-                )
-
-
-            # --- Blo Quality Assessment</span>
+            # <span style="color: #e5e7eb;">CXR Quality Assessment</span>
             <p style="color: #9ca3af;">Evaluate chest X-ray technical quality using AI (ELIXR family)</p>
-            que de Resultados (Oculto Inicialmente) ---
-            with gr.""", # Usar blanco/gris claro para texto cabecera
+            """,
             elem_id="app-header"
         )
 
-    # --- Contenido Principal (DosColumn(visible=False, elem_id="results-section") as results Columnas) ---
-    with gr.Row(equal_height=False): # Permitir alturas diferentes
+    # --- Contenido Principal (Dos Columnas) ---
+    with gr.Row(equal_height=False):
 
         # --- Columna Izquierda (Carga) ---
-        with gr.Column(scale=1, min_width=_block:
-                gr.Markdown("### 2. Quality Assessment Results350):
-            gr.Markdown("### 1. Upload Image", elem_id="results-title")
-                with gr.Row(): # Fila para imagen de salida", elem_id="upload-title")
-            input_image = gr.Image(type y resumen
-                     with gr.Column(scale=1):
-                          output_image = gr.Image(type="pil", label="Analyzed Image", interactive=False)
-                     with gr.Column(scale="pil", label="Upload Chest X-ray", height=300) # Altura fija para imagen entrada
+        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)
             with gr.Row():
-                 analyze_btn = gr=1):
-                          gr.Markdown("#### Summary", elem_id=".Button("Analyze Image", variant="primary", scale=2)
+                 analyze_btn = gr.Button("Analyze Image", variant="primary", scale=2)
                  reset_btn = gr.Button("Reset", variant="secondary", scale=1)
-            #summary-title")
-                          output_label = gr.Label(value="N/A", label="Overall Quality Estimate", elem_id="quality-label")
-
-                gr.Markdown Añadir ejemplos si tienes imágenes de ejemplo
-            # gr.Examples(
-("#### Detailed Criteria Evaluation", elem_id="detailed-title")
-                output            #     examples=[os.path.join("examples", "sample__dataframe = gr.DataFrame(
-                    headers=["Criterion", "Sim (+cxr.png")],
-            #     inputs=input_image, label)", "Sim (-)", "Difference", "Assessment (Comp)", "Assessment (Simp)"],
-                    label=None, # Quitar etiqueta redundante
-                    wrap=True,
-                    max="Example CXR"
-            # )
-            gr.Markdown(
-                "<p style='color:#9ca3af; font-size:0.9_rows=10, # Limitar filas visibles con scroll
-                    overflow_row_behaviour="show_ends", # Muestra inicio/fin al hacer scroll
-                    em;'>Model loading on startup takes ~1 min. Analysis takes ~15-4interactive=False, # No editable
-                    elem_id="results-dataframe"
-                )
-0 sec.</p>"
-            )
-
+            # gr.Examples( 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):                with gr.Accordion("Raw JSON Output (for debugging)", open=False
-
-            # --- Bloque de Bienvenida (Visible Inicialmente) ---
+        with gr.Column(scale=2):
             with gr.Column(visible=True, elem_id="welcome-section") as welcome_block:
-                gr.Markdown):
-                      output_json = gr.JSON(label=None)
-
-                gr.Markdown(
-                    f"""
-                    #### Technical Notes
-                    *   **Criterion:** Quality(
-                    """
-                    ### Welcome!
-                    Upload a chest X-ray image ( aspect evaluated.
-                    *   **Sim (+/-):** Cosine similarity with positive/negative prompt.
-                    *   **Difference:** Sim (+) - Sim (-).
-                    *PNG, JPG, etc.) on the left panel and click "Analyze Image".   **Assessment (Comp):** PASS if Difference > {SIMILARITY_DI
-
-                    The system will evaluate its technical quality based on 7 standard criteria using the ELIXR model family.FFERENCE_THRESHOLD}. (Main Result)
-                    *   **Assessment (
-                    The results will appear here once the analysis is complete.
-                    """,Simp):** PASS if Sim (+) > {POSITIVE_SIMILARITY_THRESHOLD}.
-                    """, elem_id="notes-text"
-                )
+                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" )
+            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():
+                     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")
+                gr.Markdown("#### Detailed Criteria Evaluation", elem_id="detailed-title")
+                output_dataframe = gr.DataFrame( headers=["Criterion", "Sim (+)", "Sim (-)", "Difference", "Assessment (Comp)", "Assessment (Simp)"], label=None, wrap=True, max_rows=10, overflow_row_behaviour="show_ends", interactive=False, 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(
-        """
-         elem_id="welcome-text"
-                )
-                # Podrías añadir un icono o----
-        <p style='text-align:center; color:#9 imagen aquí si quieres
-                # gr.Image("path/to/welcome_icon.pngca3af; font-size:0.8em;'>
-        C", interactive=False, show_label=False, show_download_button=FalseXR Quality Assessment Tool | Model: google/cxr-foundation | Interface: Gradio
-        </p>
-        """, elem_id="app-footer"
-        ))
-
-
-            # --- Bloque de Resultados (Oculto Inicialmente) ---
-            with gr.
-
+    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(
-        fnColumn(visible=False, elem_id="results-section") as results=assess_quality_and_update_ui,
-        inputs=[input_block:
-                gr.Markdown("### 2. Quality Assessment Results", elem_id="results_image],
-        outputs=[
-            welcome_block,      # ->-title")
-                with gr.Row(): # Fila para imagen de salida actualiza visibilidad bienvenida
-            results_block,      # -> actualiza visibilidad resultados
-             y resumen
-                     with gr.Column(scale=1):
-                          outputoutput_image,       # -> muestra imagen analizada
-            output_label,       # -> actualiza etiqueta resumen
-            output_dataframe,   # -> actualiza tabla
-            output_image = gr.Image(type="pil", label="Analyzed Image_json         # -> actualiza JSON
-        ]
+        fn=assess_quality_and_update_ui,
+        inputs=[input_image],
+        outputs=[ welcome_block, results_block, output_image, output_label, output_dataframe, output_json ]
     )
-
     reset_btn.click(
         fn=reset_ui,
-        inputs=None,", interactive=False)
-                     with gr.Column(scale=1):
-                          gr.Markdown("#### # No necesita inputs
-        outputs=[
-            welcome_block,
-             Summary", elem_id="summary-title")
-                          output_label = gr.Label(valueresults_block,
-            input_image,        # -> limpia imagen entrada="N/A", label="Overall Quality Estimate", elem_id="quality
-            output_image,
-            output_label,
-            output_dataframe,
-            output_json
-        ]
+        inputs=None,
+        outputs=[ welcome_block, results_block, input_image, output_image, output_label, output_dataframe, output_json ]
     )
 
-# ----label")
-                          # Podríamos añadir más texto de resumen aquí si quisiéramos
-
- Iniciar la Aplicación Gradio ---
+# --- Iniciar la Aplicación Gradio ---
 if __name__ == "__main__":
-                gr.Markdown("#### Detailed Criteria Evaluation", elem_id="detailed-title     # server_name="0.0.0.0" para accesibilidad en red local
-     # server_port=7860 es el puerto estándar de HF")
-                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 Spaces
-     demo.launch(server_name="0.0.0") # Mostrar 7 filas, permitir scroll si hay más
-                    max_rows=10, # Lim.0", server_port=7860)
+     demo.launch(server_name="0.0.0.0", server_port=7860)