Reexpress MCP Server

# Copyright Reexpress AI, Inc. All rights reserved. # test-time predictions and formatting for MCP server import torch import numpy as np import constants def _format_probability_as_string_percentage(valid_probability_float: float) -> str: threshold_as_string = ( constants.floatProbToDisplaySignificantDigits( floatProb=valid_probability_float)) return f"{threshold_as_string[2:]}%" def get_formatted_sdm_estimator_output_string(verification_classification, calibration_reliability, gpt5_model_explanation, gemini_model_explanation, agreement_model_classification: bool, hr_class_conditional_accuracy: float) -> str: # If this changes, the docstring in reexpress_mcp_server.reexpress() should also be updated to avoid confusing # the downstream LLMs/agents. classification_confidence = \ get_calibration_confidence_label(calibration_reliability=calibration_reliability, hr_class_conditional_accuracy=hr_class_conditional_accuracy) if agreement_model_classification: agreement_model_classification_string = "Yes" else: agreement_model_classification_string = "No" formatted_output_string = f""" <successfully_verified> {verification_classification} </successfully_verified> \n <confidence> {classification_confidence} </confidence> \n <model1_explanation> {gpt5_model_explanation} </model1_explanation> \n <model2_explanation> {gemini_model_explanation} </model2_explanation> \n <model4_agreement> {constants.AGREEMENT_MODEL_USER_FACING_PROMPT} {agreement_model_classification_string} </model4_agreement> """ return formatted_output_string def get_files_in_consideration_message(attached_files_names_list): if len(attached_files_names_list) > 0: files_in_consideration_message = f'The verification model had access to: ' \ f'{",".join(attached_files_names_list)}\n\n' else: files_in_consideration_message = f'The verification model did not have access to any external files.\n\n' return files_in_consideration_message def get_calibration_confidence_label(calibration_reliability: str, hr_class_conditional_accuracy: float, return_html_class=False) -> str: if calibration_reliability == constants.CALIBRATION_RELIABILITY_LABEL_OOD: classification_confidence_html_class = "negative" classification_confidence = "Out-of-distribution (unreliable)" elif calibration_reliability == constants.CALIBRATION_RELIABILITY_LABEL_HIGHEST: classification_confidence_html_class = "positive" classification_confidence = f">= {_format_probability_as_string_percentage(valid_probability_float=hr_class_conditional_accuracy)}" else: classification_confidence_html_class = "caution" classification_confidence = f"< {_format_probability_as_string_percentage(valid_probability_float=hr_class_conditional_accuracy)} (use with caution)" if return_html_class: return classification_confidence, classification_confidence_html_class return classification_confidence def get_calibration_reliability_label(is_high_reliability_region, is_ood): calibration_reliability = constants.CALIBRATION_RELIABILITY_LABEL_LOW if is_high_reliability_region: calibration_reliability = constants.CALIBRATION_RELIABILITY_LABEL_HIGHEST elif is_ood: calibration_reliability = constants.CALIBRATION_RELIABILITY_LABEL_OOD return calibration_reliability def format_sdm_estimator_output_for_mcp_tool(prediction_meta_data, gpt5_model_explanation, gemini_model_explanation, agreement_model_classification: bool): predicted_class = prediction_meta_data["prediction"] # prediction_conditional_distribution__lower = \ # prediction_meta_data["rescaled_prediction_conditional_distribution__lower"] verification_classification = predicted_class == 1 is_high_reliability_region = prediction_meta_data["is_high_reliability_region"] is_ood = prediction_meta_data["is_ood"] calibration_reliability = \ get_calibration_reliability_label(is_high_reliability_region, is_ood) formatted_output_string = \ get_formatted_sdm_estimator_output_string(verification_classification, calibration_reliability, gpt5_model_explanation, gemini_model_explanation, agreement_model_classification, hr_class_conditional_accuracy= prediction_meta_data["hr_class_conditional_accuracy"]) return formatted_output_string def test(main_device, model, reexpression_input): try: assert main_device.type == "cpu" prediction_meta_data = \ model(reexpression_input, forward_type=constants.FORWARD_TYPE_SINGLE_PASS_TEST_WITH_EXEMPLAR, return_k_nearest_training_idx_in_prediction_metadata=1) # We defer retrieving the training instance from the database, since it is not needed if the # visualization is turned off: prediction_meta_data["nearest_training_idx"] = prediction_meta_data["top_distance_idx"] # add the following model-level values for convenience prediction_meta_data["min_rescaled_similarity_to_determine_high_reliability_region"] = \ model.min_rescaled_similarity_to_determine_high_reliability_region prediction_meta_data["hr_output_thresholds"] = model.hr_output_thresholds.detach().cpu().tolist() prediction_meta_data["hr_class_conditional_accuracy"] = model.hr_class_conditional_accuracy prediction_meta_data["support_index_ntotal"] = model.support_index.ntotal return prediction_meta_data except: return None