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-# ScheduleBOT+
+# **ScheduleBOT+: An Intelligent Appointment Scheduling Chatbot**
+
+ScheduleBOT+ is a sophisticated, task-oriented chatbot designed for managing appointments. It leverages a modern, modular architecture to understand user requests, manage conversation context, and interact with a real calendar backend that enforces configurable business rules.
+
+The project showcases a complete end-to-end development process, from custom dataset creation and advanced model training to building a fully functional and interactive application.
+
+## **🚀 Features**
+
+- **Advanced Natural Language Understanding (NLU)**: A hybrid NLU pipeline that fuses results from a custom-trained multitask model, spaCy, and Duckling for high-accuracy intent and entity recognition.
+- **Stateful Dialogue Management**: A rule-based state machine that maintains conversation context, handles multi-turn dialogues, and gracefully manages missing information.
+- **Configurable Calendar Backend**: Interacts with a real SQLite database that acts as a calendar, enforcing user-defined business logic at runtime.
+- **Customizable Business Rules**: Users can define slot duration, working hours, lunch breaks, non-working days, and more through a startup UI.
+- **Data-Driven Model Training**: The core NLU model is trained on a custom-built, augmented, and balanced dataset (HASD) to ensure robust performance.
+- **Interactive User Interface**: A user-friendly web interface built with Gradio, featuring a two-stage setup for configuration and chat.
+- **Reproducible Setup**: The entire application and its services are containerized with Docker and managed with a simple `Makefile`.
+
+## **🏛️ Architecture**
+
+The chatbot is built on a modern, three-part pipeline architecture (**NLU -> DM -> NLG**) which ensures a clear separation of concerns, making the system robust, predictable, and maintainable.
+
+1. **Natural Language Understanding (NLU)**: The `NLUProcessor`'s sole responsibility is to convert raw user text into a structured JSON object. It intelligently fuses results from three parallel modules:
+    - **Custom Multitask Model**: A fine-tuned `distilbert-base-uncased` model for domain-specific intent classification and entity recognition.
+    - **spaCy**: For general-purpose named entities (like `PERSON`).
+    - **Duckling**: For robust time/date extraction.
+2. **Dialogue Management (DM)**: The `DialogueManager` acts as the "brain" of the chatbot. It is a rule-based state machine that takes the structured output from the NLU and, based on its internal state and logic, determines the bot's next logical action (e.g., `confirm_booking`, `request_information`).
+3. **Natural Language Generation (NLG)**: The `NLG Module` is responsible for generating the final, human-like text response. It takes the structured action from the Dialogue Manager and uses a template-based system to create a friendly and natural-sounding message for the user.
+
+## **🛠️ Installation & Setup**
+
+### **Prerequisites**
+
+- Docker and Docker Compose
+- Python 3.10+
+- A Hugging Face account (for downloading the NLU model)
+
+### **1. Clone the Repository**
+
+```bash
+git clone <your-repo-url>
+cd schedulebot-plus
+
+```
+
+### **2. Set Up Environment Variables**
+
+Create a `.env` file in the project root by copying the example:
+
+```bash
+cp .env.example .env
+
+```
+
+Edit the `.env` file and add your Hugging Face Hub model ID:
+
+```
+HUB_MODEL_ID="andreaceto/schedulebot-nlu-engine"
+
+```
+
+### **3. Build Docker Services**
+
+This command will build the Docker image for the application, installing all necessary dependencies, including the spaCy model.
+
+```bash
+make build
+
+```
+
+## **▶️ How to Run the Application**
+
+1. **Start Backend Services**: This command starts the Duckling service in the background.
+
+    ```bash
+    make up
+
+    ```
+
+2. **Run the Gradio App**: This command starts the main application.
+
+    ```bash
+    make run
+
+    ```
+
+3. **Open the UI**: Navigate to the local URL provided in your terminal (usually `http://127.0.0.1:7860`).
+4. **Configure the Calendar**: Use the startup screen to set your desired business rules for the calendar and click "Save Configuration and Start Chatbot".
+5. **Chat!**: The chat interface will appear, ready for you to interact with.
+
+When you are finished, you can stop the backend services with:
+
+```bash
+make down
+
+```
+
+## **🤖 Model Training and Data**
+
+The core NLU model was trained on the custom **HASD (Hybrid Appointment Scheduling Dataset)**, which is publicly available [here](https://huggingface.co/datasets/andreaceto/hasd).
+
+- **Data Strategy**: The dataset was built using a hybrid approach. Simple intents (`greeting`, `oos`, etc.) were sourced from the [`clinc/clinc_oos`](https://huggingface.co/datasets/clinc/clinc_oos) dataset and **down-sampled** to prevent class imbalance. Complex, entity-rich intents (`schedule`, `cancel`, etc.) were generated from templates.
+- **Data Augmentation**: To increase diversity, **Contextual Word Replacement** was applied to the templates using a `distilbert-base-uncased` model to generate paraphrased variations while preserving placeholders.
+- **Training Strategy**: The model was trained using a two-stage process:
+    1. **Head Training**: The DistilBERT base was frozen, and only the custom MLP classification heads were trained with a high learning rate.
+    2. **Fine-Tuning**: The top two layers of the DistilBERT base were unfrozen, and the entire model was fine-tuned with a low learning rate, using **Early Stopping** to prevent overfitting.
+
+The Jupyter notebooks detailing this entire process can be found in the `notebooks/` directory.
+
+The final trained NLU model, along with more specific informations and practical how-to-use instructions, is available [here](https://huggingface.co/andreaceto/schedulebot-nlu-engine).
+
+## **🧪 Evaluation**
+
+The chatbot was evaluated at both the component and end-to-end levels.
+
+- **NLU Model**: Achieved excellent F1-scores for both intent classification and NER on the held-out test set.
+- **Dialogue Manager**: The logical correctness was verified with a suite of unit tests located in `tests/test_dialogue_manager.py`.
+- **End-to-End**: The chatbot achieved a **100% Task Success Rate** on a set of predefined core conversational scenarios.