app.py

import gradio as gr
import os
import io
import png
import tensorflow as tf
import tensorflow_text as tf_text
import tensorflow_hub as tf_hub
import numpy as np
from PIL import Image
from huggingface_hub import snapshot_download, HfFolder
from sklearn.metrics.pairwise import cosine_similarity
import traceback
import time
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}")

# --- Prompts ---
criteria_list_positive = [
    "optimal centering", "optimal inspiration", "optimal penetration",
    "complete field of view", "scapulae retracted", "sharp image", "artifact free"
]
criteria_list_negative = [
    "poorly centered", "poor inspiration", "non-diagnostic exposure",
    "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

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 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)
    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)
    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)
    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 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
         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
    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()]))}')

    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
    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
        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
        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

    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()

    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')
    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 = {
            '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
        }
        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}")
        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"

        try:
            #, qformer_infer):
    """Calcula similitudes y clasifica."""
    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.")
    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")
        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(),

            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]})")

            # 2. Calcular Similitudes
            similarity_positive = cosine_similarity(image]}) vs Texto Neg ({text_embedding_neg.shape[1]})")_embedding, text_embedding_pos)[0][0]
            similarity_negative =

            # 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}")

        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
        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
        }
    return detailed_resultsnegative': float(similarity_negative) if similarity_negative is not None else None,
            'difference': float(difference) if difference is not None

# --- Carga Global de Modelos ---
print("--- Iniciando carga global de modelos else None,
            'classification_comparative': classification_comp,
 ---")
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

try:
    # Añadir token si
    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
    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,

    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)
    elixrc_infer_global = elixrc_model.signatures['serving_default']
    print("Modelo aquí
    print("Modelos descargados/verificados.")

    # 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 =)
    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 ---")
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.")

    # Cargar QFormer (_pil):
    """Procesa la imagen y devuelve actualizaciones para la UI."""ELIXR-B Text)
    print("Cargando QFormer
    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
        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(value="N/A"),   # Borra etiqueta
            pdmodels_loaded = True
    end_time = time.time()
.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()
    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():
            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']
            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 ])
            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
        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
            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)"
        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
        return (
            output_data.append([
                criterion,
                f"{sim_pos:.4f}"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
            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
        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 ---
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
        gr.update(value="N/A"),   # Borra etiqueta
        _primary="#4b5563",          # Borde primario (gray-pd.DataFrame(),           # Borra dataframe
        None                      # Borra JSON
    )

600)

    # Botones y Elementos Interactivos
    # --- Definir Tema Oscuro Personalizado (CORREGIDO) ---
#button_primary_background_fill="*primary_600", # Usa color primario (azul)
    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_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

    # 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
)


# --- Definir la Interfaz Gradio con="#1f2937",       # Fondo de componentes (gray-800)
 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>
            <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

        # --- 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.Row():
                 analyze_btn = gr=1):
                          gr.Markdown("#### Summary", elem_id=".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>"
            )


        # --- 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(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"
                )

    # --- 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.


    # --- 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
        ]
    )

    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
        ]
    )

# ----label")
                          # Podríamos añadir más texto de resumen aquí si quisiéramos

 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)