This Food Recommendation Chatbot is designed to provide restaurant suggestions based on user preferences and location. It utilizes natural language processing and geocoding to offer a seamless and interactive experience to users.

• Conversational Interaction: Engage with users in a natural, conversational manner.
• Location-Based Recommendations: Provide restaurant suggestions near the user's current location.
• NLP for User Input Processing: Analyze and interpret user requests using advanced NLP techniques.

To run this chatbot, the following Python libraries are required:

• streamlit
• transformers
• sentence_transformers
• pandas
• numpy
• pickle
• nltk
• geopy

1. Install Python 3.x from the official website.
2. Install the necessary packages using pip:

   pip install streamlit transformers sentence_transformers pandas numpy pickle nltk geopy
   

To use the chatbot, you need to download the necessary NLTK data. Run the following Python commands:

import nltk
nltk.download('punkt')
nltk.download('stopwords')

To run the Streamlit app, execute the following command in your terminal:

streamlit run your_script_name.py

• business_in_ca.csv: Contains business data for California.
• yelp_recommendation_model_8.pkl: A pickle file with user and business embeddings.

• clean_text_data(texts): Cleans and processes text data.
• haversine(lon1, lat1, lon2, lat2): Calculates distances between two geographical points.
• get_recommendations(input_text, user_lat, user_lon): Provides restaurant recommendations.
• geocode_address(address): Converts an address into geographical coordinates.
• handle_conversation(user_input, user_lat, user_lon): Manages conversation logic and user interactions.

• Chat Input: Where users can type their queries or requests.
• Text Input: For users to enter their address for location-specific recommendations.
• Chat History: Displays the ongoing conversation.

• The chatbot's recommendations are currently limited to California.
• It relies on accurate geocoding for location-based suggestions.

• Expanding the geographical scope of the chatbot.
• Implementing more sophisticated NLP models for enhanced user query understanding.
• For further assistance or queries, feel free to reach out to the support team or refer to the project documentation.