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( """ # CXR Quality Assessment

Evaluate chest X-ray technical quality usingfill="#1f2937", # Fondo de bloques (gray-8 AI (ELIXR family)

""", 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-"

Model loading on startup takes ~1 min. Analysis takes ~15-40 sec.

" ) # --- 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. """,( """ # CXR elem_id="welcome-text" ) # --- Blo Quality Assessment

Evaluate chest X-ray technical quality using AI (ELIXR family)

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( "

Model loading on startup takes ~1 min. Analysis takes ~15-4interactive=False, # No editable elem_id="results-dataframe" ) 0 sec.

" ) # --- 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----

C", interactive=False, show_label=False, show_download_button=FalseXR Quality Assessment Tool | Model: google/cxr-foundation | Interface: Gradio

""", 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)