Geography Chatbot

This chatbot is designed to assist users with various geography-related queries and tasks. It utilises a combination of APIs, natural language processing techniques, and machine learning models to provide accurate and informative responses.

Functionalities

• Answer Geography Queries: The chatbot can respond to user queries about geographical locations, countries, continents, and other trivia related to geography.
• Differentiated Responses: It employs different switch cases based on the type of questions asked by the user.
• Wikipedia Integration: When users ask 'what' or 'who' questions, the chatbot uses the Wikipedia API to provide relevant answers.
• Weather Information: For weather-related queries, the chatbot leverages a weather API to furnish the user with accurate weather information.
• Cosine Similarity Matching: If a user input's a question pattern not recognised by the chatbot, it utilises cosine similarity to find similar matches within its knowledge base.
• Logical Knowledge Base: The chatbot contains a logical knowledge base inferred using the ResolutionProver from NLTK to verify the correctness of user queries and statements about places being countries or continents.
• Inferencing: It can infer from user statements and dynamically update its knowledge base, ensuring consistency and accuracy.
• Game Interaction: Users can engage the chatbot in games that involve fuzzy logic-based questions and store their answers for evaluation.
• Flag Recognition: The chatbot can recognise countries from flag images and videos provided by users, employing a trained machine learning model for accurate predictions.

Technologies Used

• Wikipedia API
• Weather API
• NLTK (Natural Language Toolkit)
• Googlesearch API
• Text-to-Speech (MacOS system's function)
• ResolutionProver (from NLTK)
• Machine Learning Models (for flag recognition)
• Video Processing Techniques (for flag recognition from videos)
• Convolutional Neural Network (CNN) Model
• Hyperparameter Tuning (Bayesian Optimisation, Random Search, Grid Search)

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How to Use

1. Download Dependencies:

   • Download the required dependencies using the requirements.txt file.

2. Run the Chatbot:

   • With the files available, run the chatbot.py file and the chatbot will start.

3. Training Your Own Model:

   • If you want to train your own model:
     • Use the image_preprocessing.py file to process and save training datasets as numpy (npy) files.
     • Use the cnn_trainer.py to train and save the training model. Alternatively, use hyper_para_bayesian_optimisation.py or hyper_para_random_search.py to train with hyperparameter-tuned layers for the CNN model.

4. Run the Chatbot:

   • After training (if applicable), run the chatbot.py file, and you are good to go.