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This project involves the development of a chatbot using the punkt_tab tokenizer from the NLTK library. The chatbot will be capable of understanding and responding to user queries by tokenizing text input, extracting key phrases, and providing context-aware responses. The project will be built with Flask to serve the chatbot as a web application and deployed for easy accessibility.
🎤 Pitch
This Project Should Be Added:
Improves User Experience: Automates real-time responses with a chatbot.
Uses NLP: Leverages NLTK’s punkt_tab for better text processing.
Scalable Deployment: Easily deployable via Flask for multiple users.
4.Practical Learning: Combines NLP and web deployment for real-world application.
Implementation:
1.Setup: Install Python, NLTK, Flask.
2.Input Processing: Tokenize user input with punkt_tab.
3.Chatbot Logic: Generate responses using rule-based logic.
4.Web Interface: Build with Flask.
Deploy: Host the chatbot on a Flask server for web access.
👀 Have you spent some time to check if this issue has been raised before?
🔖 Project description
This project involves the development of a chatbot using the punkt_tab tokenizer from the NLTK library. The chatbot will be capable of understanding and responding to user queries by tokenizing text input, extracting key phrases, and providing context-aware responses. The project will be built with Flask to serve the chatbot as a web application and deployed for easy accessibility.
🎤 Pitch
This Project Should Be Added:
4.Practical Learning: Combines NLP and web deployment for real-world application.
Implementation:
1.Setup: Install Python, NLTK, Flask.
2.Input Processing: Tokenize user input with punkt_tab.
3.Chatbot Logic: Generate responses using rule-based logic.
4.Web Interface: Build with Flask.
Deploy: Host the chatbot on a Flask server for web access.
👀 Have you spent some time to check if this issue has been raised before?
🏢 Have you read the Code of Conduct?
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