Upload any dataset — CSV, PDF, images, or JSON. The system auto-detects the ML task, trains a model with a live loss curve, and lets you query it instantly with built-in explainability.
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CSV · Excel · PDF · TXT · DOCX · JSON · JPG · PNG
Adaptive Pipeline Router
File type detection → automatic preprocessing pipeline selection. Tabular → pandas profiling + feature engineering + XGBoost/neural net. Text → chunking + embedding + RAG or LoRA fine-tune. Image → augmentation + transfer learning.
"Learns on the Spot" Engine
Small data: full training loop via scikit-learn. Text: RAG (fast mode) with ChromaDB, or LoRA fine-tune (deep mode) on Phi/TinyLlama. Image: frozen backbone + trainable classification head. All training streams results via WebSocket.
Explainability Layer
SHAP for tabular predictions. Attention heatmaps for transformer outputs. GradCAM for image classifications. Every prediction comes with a 'Why?' toggle.
I can walk through the pipeline routing logic, model selection heuristics, real-time training architecture, and the explainability integration.