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Banking & Financial Assistant Chatbot. Presented by: GuhanSelvam.
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Introduction. Greetings and Introduction Hello everyone! My name is GuhanSelvam. Project Overview A sophisticated chatbot designed to assist users with banking and financial queries using text input. Leverages advanced AI and natural language processing for instant, accurate responses..
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Frontend - User Interface. Built with Gradio A Python library that creates interactive web-based interfaces. User Interaction Features Text Input: Users can type their queries into a user-friendly textbox. Modular Input Components: Adjustable sliders to customize parameters: Maximum tokens Temperature Top-p sampling.
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Backend - Model Integration. Inference Client Connects to the model: “HuggingFaceH4/zephyr-7b-beta” hosted on Hugging Face. Designed for natural language understanding and generating contextually relevant responses. Application Focus Specifically tailored for banking and financial service inquiries..
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Code Structure and Packages. Modular Organization The application is structured into several components for clarity and maintainability. Dependencies: gradio: For creating the interactive web interface. huggingface_hub: For efficient model access and response generation. numpy: For handling numerical data..
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Key Code Components. Response Generation The generate_response() function: Processes user input. Constructs messages for the model. Maintains contextual understanding throughout the conversation.
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Functionality. User Interaction Users submit inquiries solely through text input. Focused and streamlined communication. Response Retrieval Interacts with the Hugging Face model to generate accurate responses. Maintains contextual awareness from system messages. Output Handling Comprehensive conversational history ensures relevance. User Feedback Intelligent feedback for unrelated queries, guiding users back to banking and finance topics..