A collection of small Python projects that explore chat models, structured output, embeddings, and Streamlit interfaces using LangChain.

Projects

Project	Description	Run
Mood AI chatbot	Chat with an assistant in angry, sad, or funny modes.	streamlit run chatmodels/UIchatbot.py
Movie information extractor	Converts a movie description into structured fields such as title, genre, cast, rating, and summary.	streamlit run CineSage/UICore.py
CLI movie extractor	Command-line version of the movie information extractor.	python CineSage/core.py
CLI mood chatbot	Command-line version of the mood chatbot.	python chatmodels/chatbot.py
Embeddings example	Creates a Mistral embedding vector for a text query.	python embeddingmodels/embeddings.py

Requirements

• Python 3.10 or later
• A Mistral AI API key

Install the project dependencies:

pip install -r requirements.txt
pip install streamlit

Configuration

Create a .env file in the project root and add your Mistral API key:

MISTRAL_API_KEY=your_mistral_api_key

The Hugging Face example in chatmodels/huggingface.py may additionally require a Hugging Face access token, depending on the model and endpoint configuration.

Quick start

Run the movie extractor web app:

streamlit run CineSage/UICore.py

Or run the mood chatbot web app:

streamlit run chatmodels/UIchatbot.py

Streamlit will display a local address in the terminal; open it in your browser to use the app.

Project structure

.
|-- chatmodels/
|   |-- UIchatbot.py       # Streamlit mood chatbot
|   |-- chatbot.py         # CLI mood chatbot
|   |-- chat.py            # Basic chat-model example
|   `-- huggingface.py     # Hugging Face chat-model setup
|-- CineSage/
|   |-- UICore.py          # Streamlit movie extractor
|   `-- core.py            # CLI movie extractor
|-- embeddingmodels/
|   `-- embeddings.py      # Mistral embeddings example
`-- requirements.txt

Notes

• The examples use mistral-small-latest for chat and mistral-embed-2312 for embeddings.
• Keep .env private; never commit API keys to source control.