Intelligent Chatbot Project with Django and spaCy – ApyCoder

Key Features:

1. Django-Powered Interface: Our chatbot comes to life through a sleek and user-friendly interface built on Django. It provides an intuitive platform for users to engage in meaningful conversations.
2. Knowledge Repository – QnA Model: The chatbot’s intelligence is driven by the QnA database model, which serves as the repository for predefined questions and their corresponding answers. This model forms the bedrock of the chatbot’s knowledge base.
3. spaCy Magic for NLP Brilliance: Leveraging spaCy’s en_core_web_sm model, the chatbot possesses advanced Natural Language Processing capabilities. This enables it to comprehend the nuances of language, providing accurate and context-aware responses.
4. Text Preprocessing: A meticulous text preprocessing step ensures that both user input and predefined questions are in optimal format for analysis, enhancing the chatbot’s understanding of queries.
5. Similarity Scoring Precision: The chatbot calculates similarity scores using spaCy, enabling it to identify the most relevant predefined question based on user input. This process ensures that responses are personalized and contextually accurate.
6. Dynamic Response Generation: Armed with the most relevant question, the chatbot intelligently retrieves the corresponding answer, creating a dynamic and engaging response for the user.
7. User Interaction Logging: A logging feature has been implemented to capture user queries and selected responses, laying the groundwork for future analysis and continuous improvement.

Usage:

1. Open the chatbot interface in your web browser.
2. Enter a question in the input field and submit.
3. Witness the chatbot’s prowess as it processes your query and delivers a tailored response.
4. User interactions are logged, contributing to the ongoing refinement of the chatbot’s capabilities.