- Patient Interaction: CareGenie interacts with patients to answer questions related to hospital services, doctor availability, appointment booking, medical record queries, and more.
- Appointment Scheduling: The chatbot assists patients in scheduling or rescheduling appointments with doctors based on their availability.
- Medical Information: Patients can ask CareGenie about medical services, treatment options, and doctor specialties.
- Query Resolution: CareGenie answers general queries such as operating hours, hospital directions, and provides health tips.
- Conversation History: All user interactions with the chatbot are logged for reference and can be reviewed later.
- Data-Driven Responses: CareGenie leverages a machine learning model trained on labeled intents and entities, ensuring accurate and context-aware responses.
- Natural Language Processing (NLP): The chatbot uses NLP techniques such as tokenization and TF-IDF for text vectorization to understand and classify user queries.
- Machine Learning: A Logistic Regression classifier is employed to predict the appropriate response based on the user input, which is matched to predefined intents.
- Streamlit: A web-based interface is developed using Streamlit, making it easy for users to interact with the chatbot and for hospital administrators to view conversation histories.
- Python Libraries: Key libraries such as nltk, scikit-learn, and streamlit are used to build, train, and deploy the chatbot.
- The dataset used in this project is a collection of labelled intents and entities. The data is stored in a list.
- Intents: The intent of the user input (e.g. "Doctor_Query", "booking", "about_medical_services")
- Entities: The entities extracted from user input (e.g. "Hi", "What is timimg of the doctor?", "Booking_slots")
- Text: The user input text.
- Data Collection: The chatbot’s functionality is based on a dataset of labeled intents and their corresponding patterns and responses. These intents include medical queries, appointment bookings, and general hospital information.
- Training Model: The model is trained using the labeled data to classify user input into relevant intents, which are then matched with predefined responses.
- User Interface: The chatbot interface is designed to be user-friendly, featuring text inputs and responses, along with a sidebar that provides easy navigation to features such as "Home," "History," and "About."
- Continuous Improvement: The system logs conversations, which allows for future improvements and fine-tuning of the chatbot by analyzing interactions and refining the response model.
git clone <repository-url>
cd <repository-directory>python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`pip install -r requirements.txtimport nltk
nltk.download('punkt')To run the chatbot application, execute the following command:
streamlit run app.pyOnce the application is running, you can interact with the chatbot through the web interface. Type your message in the input box and press Enter to see the chatbot's response.
The chatbot's behavior is defined by the intents.json file, which contains various tags, patterns, and responses. You can modify this file to add new intents or change existing ones.
The chatbot saves the conversation history in a CSV file (chat_log.csv). You can view past interactions by selecting the "Conversation History" option in the sidebar.
Contributions to this project are welcome! If you have suggestions for improvements or features, feel free to open an issue or submit a pull request.
- NLTK for natural language processing.
- Scikit-learn for machine learning algorithms.
- Streamlit for building the web interface.
Replace <repository-url> and <repository-directory> with the actual URL of your repository and the name of the directory where the project is located. Adjust any sections as necessary to better fit your project's specifics.