Commit History
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major(feat): add support for Gradio API model and enhance ONNX model handling
- Introduced new model (model_8) using Gradio API for inference. - Added preprocessing and postprocessing functions for Gradio API integration. - Enhanced ONNX model handling with improved logging and error management. - Updated softmax function to return Python floats for better compatibility. - Added new model configuration files for model_8 and updated existing configurations.22628b7
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less load: inference shouldn't take longer than 3 seconds...
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feat: refactor prediction functions and enhance image loading capabilities for improved processing and noise estimation
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feat: enhance model registration by adding architecture and dataset metadata to the ModelEntry class
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feat: implement new app structure with enhanced ensemble prediction capabilities and integrate multiple models for image analysis
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refactor: update import paths for forensics utilities and enhance logging functionality in inference data
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refactor: simplify ELA function name and clean up unused augmentations in Gradio interface
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refactor: ensemble monitoring and weight optimization agents, refactor minmax processing function, and update requirements
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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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