FoodTrend Analyzer

FoodTrend Analyzer is a Python package designed to analyze and summarize food trends by processing text inputs. Users can input articles, social media posts, or any text discussing food preferences and consumption patterns. The package uses llmatch-messages to ensure structured responses, providing insights into trends like the shift from pizza to fried chicken among Gen Z. It extracts key points, identifies trends, and presents them in a consistent format, making it easier to understand and act on food industry insights.

Features

• Text Processing: Analyze articles, social media posts, and other text inputs to extract food trends.
• Structured Responses: Use llmatch-messages to ensure consistent and structured output.
• Customizable LLM: Use the default ChatLLM7 from langchain_llm7 or pass your own LLM instance for flexibility.
• API Key Management: Easily manage API keys for ChatLLM7 via environment variables or direct input.

Installation

You can install the package using pip:

pip install foodtrend_analyzer

Usage

Here is a basic example of how to use the foodtrend_analyzer package:

from foodtrend_analyzer import foodtrend_analyzer

# Example user input
user_input = "Gen Z is shifting from pizza to fried chicken."

# Analyze the input
response = foodtrend_analyzer(user_input)

# Print the response
print(response)

Input Parameters

• user_input (str): The user input text to process.
• llm (Optional[BaseChatModel]): The LangChain LLM instance to use. If not provided, the default ChatLLM7 will be used.
• api_key (Optional[str]): The API key for ChatLLM7. If not provided, the key will be fetched from the environment variable LLM7_API_KEY.

Custom LLM Usage

You can use different LLMs by passing your own LLM instance. Here are examples using OpenAI, Anthropic, and Google:

OpenAI

from langchain_openai import ChatOpenAI
from foodtrend_analyzer import foodtrend_analyzer

llm = ChatOpenAI()
response = foodtrend_analyzer(user_input, llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from foodtrend_analyzer import foodtrend_analyzer

llm = ChatAnthropic()
response = foodtrend_analyzer(user_input, llm=llm)

Google

from langchain_google_genai import ChatGoogleGenerativeAI
from foodtrend_analyzer import foodtrend_analyzer

llm = ChatGoogleGenerativeAI()
response = foodtrend_analyzer(user_input, llm=llm)

API Key Management

The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you need higher rate limits, you can pass your own API key via the environment variable LLM7_API_KEY or directly in the function call:

response = foodtrend_analyzer(user_input, api_key="your_api_key")

You can get a free API key by registering at LLM7 Token.

Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

License

This project is licensed under the MIT License.

Author

• Eugene Evstafev
• Email: hi@euegne.plus
• GitHub: chigwell