Update README.md

README.md

@@ -11,4 +11,76 @@ license: apache-2.0
 short_description: You can train any simple models
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🧠 Universal CPU AI Trainer ⚙️
+
+Welcome to the Universal CPU AI Trainer! This Hugging Face Space allows you to:
+
+*   **Define AI Tasks:** Choose from Tabular Classification, Tabular Regression, or basic Image Classification.
+*   **Select Model Families:** Experiment with classical Scikit-learn models or simpler PyTorch Neural Networks (MLPs, basic CNNs).
+*   **Configure Datasets:**
+    *   Generate synthetic datasets with configurable rows, features, and characteristics.
+    *   Let the "AI Assistant" (heuristic rules) suggest dataset parameters.
+    *   Upload your own datasets (CSV, JSON, Parquet).
+*   **Design Neural Networks:** For PyTorch MLPs, specify hidden layers and get suggestions for target parameter counts (10k - 1M).
+*   **Train Models on CPU:** All training happens on the free CPU tier. Be patient with larger models or datasets!
+*   **Evaluate & Download:** Get basic evaluation metrics and download your trained models (PKL, ONNX for Scikit-learn; PT for PyTorch).
+
+**⚠️ Important Considerations for CPU Training:**
+
+*   **Performance:** This Space runs on a **free CPU tier**. Training complex models (especially Neural Networks with >100k parameters) or large datasets will be **SLOW**. An epoch can take minutes to hours.
+*   **Memory Limits:** The free tier has limited RAM (~15GB). Very large datasets or models might cause the Space to crash.
+*   **Toy Examples:** The "Basic Image Classification" task uses randomly generated pixel data, not real images. It's for demonstrating the CNN pipeline structure on CPU.
+*   **Experimental:** This is a tool for learning and experimentation, not for production-grade model training.
+
+## How to Use
+
+1.  **Tab 1: Define Task & Model**
+    *   Select your desired **Task Type** (e.g., Tabular Classification).
+    *   Choose a **Model Family** (Scikit-learn or PyTorch).
+    *   Select the **Specific Model**.
+    *   If using PyTorch NNs:
+        *   Select a **Target Parameter Range** (e.g., "Small (10k-50k)").
+        *   For MLPs, configure **Hidden Layers** or use the "Suggest MLP Layers" button (after defining a dataset in Tab 2 for better dimension estimates).
+
+2.  **Tab 2: Configure Dataset**
+    *   Choose to **Generate** a new dataset or **Upload** your own.
+    *   **Generation:** Specify rows, features, etc., or use the "AI suggest" checkbox.
+    *   **Upload:** Provide your CSV, JSON, or Parquet file.
+    *   Enter the **Target Column Name** from your dataset.
+    *   Click "Generate & Preview Dataset" or let the upload complete.
+
+3.  **Tab 3: Train Model & Get Results**
+    *   Adjust **Training Hyperparameters** (Epochs, Batch Size, Learning Rate - primarily for NNs).
+    *   Select the desired **Model Output Format**.
+    *   Click "🚀 Train Model".
+    *   Monitor the **Training Log**.
+    *   View **Evaluation Metrics**, **Model Parameters**, and (for PyTorch) a **Loss Curve**.
+    *   Download your trained model using the **Download Trained Model** button.
+
+## Model Output Formats
+
+*   **Scikit-learn:**
+    *   `.pkl`: Python pickle file containing the Scikit-learn pipeline (preprocessor + model).
+    *   `.onnx`: Open Neural Network Exchange format. The exported ONNX model includes the preprocessing steps and expects raw input matching the original training data structure.
+*   **PyTorch:**
+    *   `.pt`: PyTorch file. For MLPs trained on tabular data, this bundles the model's `state_dict` and the Scikit-learn `preprocessor` used. For CNNs, it's typically the `state_dict`.
+
+## Want More Power? Clone & Upgrade!
+
+If training is too slow or you hit resource limits:
+
+1.  Go to this Space's main page.
+2.  Click the three dots (⋮) menu and select "Duplicate this Space."
+3.  On the creation page, choose upgraded **Space Hardware** (e.g., better CPU or a GPU - these are paid options).
+4.  Create your new, more powerful Space! (You'll likely need to re-upload/re-generate data).
+
+## Development & Contributions
+
+This Space is built with Python, Gradio, Scikit-learn, and PyTorch.
+*   **Main Application Logic:** `app.py`
+*   **Dependencies:** `requirements.txt`
+
+Feel free to explore the code, suggest improvements, or report issues!
+
+## License
+This project is licensed under the **Apache License 2.0**. See the `LICENSE` file for details.