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refactor(model_loader): update dataset names for model registrations in the loader
refactor(ui): change confidence slider default value and update predictions dataframe headers554718d -
refactor(logging): move CustomFormatter to agent_logger and update log handlers
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major(feat): per-agent logging system with AgentLogger class
- Introduced AgentLogger to manage logging for different agents. - Created log files for ensemble monitoring, weight optimization, system health, context intelligence, and forensic anomaly detection. - Updated EnsembleMonitorAgent, WeightOptimizationAgent, SystemHealthAgent, ContextualIntelligenceAgent, and ForensicAnomalyDetectionAgent to use AgentLogger for logging. - Modified app.py to integrate agent logs into the Gradio interface. - Added log reading functionality to display agent logs in the UI. - Removed old logging setup and streamlined logging across agents for consistency.f78b194 -
Load environment variables from .env file in hf_logger.py
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fix: add model paths and class names for ONNX models in model_loader
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fix: add missing import for postprocess_onnx_output in model_loader
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fix: add missing import for infer_onnx_model in model_loader and update its definition in onnx_helpers
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fix: add missing import for infer_onnx_model in model_loader
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fix: add missing import for preprocess_onnx_input in model_loader
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feat: implement model registration logic for ONNX, HuggingFace, and Gradio API
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refactor: extract Gradio logging setup into a separate module
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refactor: ONNX model loading and caching functions
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feat: add Gradio API integration and ONNX preprocessing functions
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major(refactor): prep for refactor project
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sync (#2)
Verified signature- docs: streamline docstring for Bit Plane Extractor interface by removing redundancy and enhancing readability (52ba0af2bbd29c1253c168a2e047c51796960a5f) - refactor: update API names for various functions to enhance clarity and consistency (5aa075b3b24743bbfeb856a03bc3e77eb8e1e776) - refactor: reorganize Gradio interface layout for improved user experience and accessibility (6909ab705263b16d249b0bc2df66d939018edf9b) - feat: add Bit Plane extraction to forensic analysis and enhance Gradio interface with new processing tools (62c8661cd5959f7fde3cb523a9b2479f6e9655d6) - refactor: ensemble monitoring and weight optimization agents, refactor minmax processing function, and update requirements (a7003f1fdfe2f57d50a1a760dfcadbed5de6a5f3) - refactor: rename predict_image_with_json to predict_with_ensemble and update its implementation to return consensus label (4b481078e1ec55a7cecefef8082aab5a1e3235f8) - refactor: rename predict_image_with_json to predict_with_ensemble and update its implement…057dd29
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fix: ensure Hugging Face API token is used for dataset repository initialization
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feat: implement CommitScheduler for automated logging to Hugging Face dataset and refactor dataset initialization process
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fix: enable dataset logging to Hugging Face Hub by uncommenting push_to_hub functionality
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feat: implement custom JSON encoder for numpy types and ensure type consistency in inference results
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feat: improve JSON handling and type safety in inference results and dataset logging
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feat: add detailed logging for input types in inference data processing
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feat: enhance image handling by ensuring input is a PIL Image and updating forensic image logging
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major(feat): restructure models and introduce new monitoring and weight management agents
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feat: add logging
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make clear distinction between ensemble agents and smart agents
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major: prep implementtation of vLLM smart agents
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cv2
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(major)feat: add new functionalities for AI-generated content detection
- Introduced a comprehensive project introduction and implementation strategy for AI-generated content detection. - Added new Gradio tabs for wavelet blocking noise estimation, bit plane extraction, and EXIF metadata extraction. - Enhanced the user interface to include detailed descriptions for each new feature, improving usability and accessibility.c9d5b11
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UPDATED: Updated ELA image processing in app.py and ela.py
- Updated ELA image processing in app.py by replacing the previous ELA image generation with two passes: standard analysis and enhanced visibility. - Updated genELA() function in ela.py to improve the: - Replaced the previous implementation with a new implementation. - Added arguments for quality, scale, contrast, linear, and grayscale. - Compressed the input image using JPEG compression. - Calculated the difference compressed images. - Applied scaling to the difference image. - Applied contrast adjustment to the resulting image. - Added support for linear difference and grayscale output. - Returned the processed ELA image.c0b0cd4
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🏗️ build(utils): update image preprocess and functionality expansion
- move ELA functionality to separate utils for better modulation and maintenance【config】 🏗️ build(utils): update image preprocess and functionality expansion - update ELA for better performance【Install libraries Pytorch, PIL and opencv】 🔧 chore(doc): update py files - remove ON.NX dependencies【Remove onnx pytorch dependencies】 🔧 chore(doc): update py files - update ignore files setting to educate CI autoignore build filesd307493
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```
✨ feat(images): add column allocation on post processed images - introduces the Granularity by breaking images into 4 columns on display refactor(preprocess): update default values for channel and radius - solves obscure errors -runs performance impact tests -directory dirs[]= dicts(); ```3f08aad -
🐛 fix(image processing): ensure image consistency for gradient and minmax preprocessors
- added type check for PIL Image and conversion to NumPy array in min_max preprocessing - 【Refactor】also converted PIL Image to NumPy array in image prediction module 💄 style(image processing): optimize ELA image list - removed ela_img_4 as it did not add significant insights and kept the uncommented portion orderly322ab55 -
✨ feat(image processing): add MinMax pre-processing to image pipeline
- add support【】for MinMax pre-processing in predict_image_with_html - add MinMax processing to ela_images output - import minmax_preprocess function from utils.minmax.py - adjust gradient processing intensity value ✅ test(gradient): test gradient processing - test default behavior of gradient processing - ensure correct default values for intensity and blue_mode - validate intensity adjustment237292f -
✨ feat(image): add gradient processing in predict_image Function
-Body Mainly：【main】+ - add gradient processing to predict image pipeline using new utils module - introduce new gradient processing parameters for enhanced image analysis ✨ feat(utils): add gradient processing utility - Body Process -add gradient processing utility with various configuration options - Body Methods -add normalization, equalization, and LUT creation for gradient processing -add spatial gradient calculation for image analysis -implement blue mode options for gradient processing - Body Mode -images and dictionaries properly formatted according to function requirements -utiilities imports updated for type relevance -variables initialized for reuse and efficiency π§ chore(utils): add python eol format -update utils/gradiend.py for better visual formatting67883c3 -
✨ feat(predict): add Error Level Analysis to image prediction
【Predict Image Function】 - integrate an extra transform using ELA (Error Level Analysis) 【User Interface】 - display ELA transformed image 【Settings】 - updated the visibility of settings sliderc08bf6c -
✨ feat(ui): add sharpen and noise augmentation
- add new sharpen sliders for image processing - add new noise sliders for image processing 🐛 fix(utils): correct augmentation method assignment - correctly set default augmentations methods - allow Dynamic settings for sharpen 📝 docs(ui): update Image UI with new Augmnents - add new augment methods UI - current weights of image slider - display of image prediction timestamp33581d9 -
🐛 fix(app): add input degrees having a bug for cases without augmentation
- modify `predict_image_with_html` function to accept `augment_method` and `rotate_degrees` - add filter to conditionally show rotate slider - modify `augment_image` function to accept `methods` and `rotate_degrees` parameters and apply the chosen augmentation methods accordingly ✨ feat(app): expand augmentation methods - add new checkbox group to let user select from multiple augmentation methods such as rotate 📝 docs: update imports comment and reorganize - type hint comments for function return values - add new filters to `PIL` Libraries for image manipulation5ccc3f6 -
♻️ refactor(app): code cleanup
- removed unused import: warnings - added logging configuration -【docs】modified logging level -【refactor】renamed old logging config to current logging module - optimized importance of models by introducing model hook configuration modes -【docs】map classes of various models -【feat】move and refactor models into reusable function - modified logic containment - reorganized and refactored prediction logic - moved all augmentation related function to util - clarified all parameters to be consistent -【refactor】modified function name consistent ▪️️ feat(app): new model prediction logic with gpu decorator -【refactor】added model index to output [[model id, model name, class a confidence, class b confidence, label] recommended output] ---· + refactor(file management): code cleanup - remove previous unused paths - moved function into utils67f3560
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Refactor project interface and remove unused utility files
- Update Gradio interface description to emphasize model evaluation - Remove temporary slider and update project description - Delete unused `goat.py` utility file and large model file - Modify `.gitignore` to exclude additional files - Comment out previously imported inference function2669b96 -
Refine UI layout and GOAT inference call
LPX55- Add margin and padding classes to improve results display layout - Modify GOAT inference function to handle image input more flexibly - Add print statement for debugging image input in GOAT inferencef5d6264 -
Update utils/goat.py
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Update utils/goat.py
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Create __init__.py
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Create goat.py
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Create utils/onnx.py
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