VicaBot is an AI-driven customer service chatbot built to provide fast, intelligent, and human-like responses to user inquiries. It combines a rule-based logic engine with Meta’s powerful BlenderBot NLP model to simulate real-time customer support. Designed with simplicity and scalability in mind, VicaBot is deployed using Streamlit and is ideal for use in e-commerce, helpdesks, and digital service platforms.
- Description
- Methodology
- Tools and Technologies
- Deployment Scenarios
- Future Enhancements
- Project Screenshots
- Video Demo
This project demonstrates a customer care chatbot that blends AI text generation and pre-scripted decision trees to enhance digital customer service experiences. The chatbot:
- Provides instant answers to frequently asked questions like “How do I get a refund?” or “Where’s my order?”
- Uses BlenderBot, a conversational NLP model, to manage general queries and simulate real customer-agent conversations.
- Maintains context-aware conversations, storing past responses and user inputs to create a chat history.
- Built with a lightweight Streamlit interface for quick deployment and UI interaction.
The chatbot operates through the following workflow:
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User Input: The user types a question or concern into a text input box.
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Rule-Based Intent Recognition:
- If keywords such as “refund,” “payment,” or “account” are detected, the chatbot serves a curated response.
- If no direct match is found, it defers to BlenderBot for a general AI-generated reply.
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AI Response Generation:
- BlenderBot uses the input to predict a logical response based on pre-trained conversational data.
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Conversation Update:
- The conversation log is updated to preserve context and improve user experience.
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Front-End Display:
- The chatbot interface mimics real messaging platforms for a natural chat feel.
| Tool/Library | Purpose |
|---|---|
| Streamlit | Frontend UI for chat interaction |
| HuggingFace | Hosting and retrieving BlenderBot model |
| BlenderBot 400M | AI model for contextual text generation |
| Python | Core logic and API integration |
| Warnings Module | Suppresses model load warnings for UX |
| VS Code | Python script interface and code editor |
VicBot is adaptable and can be deployed in various real-world customer-facing environments:
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E-commerce Platforms: Automate refund inquiries, order tracking, and account issues.
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Customer Support Centers: Handle tier-1 questions and escalate complex ones to human agents.
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Healthcare Portals: Provide administrative answers related to appointments, billing, or login access.
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Corporate Intranets: Assist employees with internal systems and IT-related FAQs.
To further improve the chatbot's performance, human-likeness, and adaptability across industries, several future upgrades are being considered:
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Emotion Detection: Integrate sentiment and emotion recognition (e.g., using models like BERT or RoBERTa) to make the chatbot more empathetic in handling frustrated or upset users.
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Voice Interaction: Enable voice input and text-to-speech output for a more natural and accessible user experience, especially for visually impaired users or mobile-first interfaces.
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Multilingual Support: Expand the chatbot’s language capabilities to serve diverse users globally by integrating translation APIs or multilingual NLP models.
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User Authentication & Personalization: Add user authentication to personalize interactions, such as remembering previous orders or tailoring responses based on user preferences.
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Domain-Specific Fine-Tuning: Fine-tune the conversational model on industry-specific datasets (e.g., healthcare, finance) to improve relevance and contextual understanding.
These enhancements aim to make the chatbot not only smarter but also more adaptive and emotionally intelligent—ready to serve real-world needs at scale.
https://drive.google.com/file/d/1wc-3gjNNEp8N-y00rqhqtHHdmWc58nuw/view?usp=drive_link
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