• Alessandro Marina
• Marco Laurenzi

This project leverages a Large Language Model (LLM) to automate the identification of company roles within a market. Traditional market analysis methods are effective but time-consuming and labor-intensive. Our solution streamlines this process by quickly processing large volumes of text, ensuring high accuracy and efficiency.

• Automated Data Analysis: Quickly processes large volumes of text.
• High Accuracy: Ensures accurate classification of company roles.
• Efficiency: Reduces manual labor, allowing businesses to focus on strategic decision-making.

1. Web Scraping: Collects data from various sources.
2. Classification: Uses the LLM to classify companies based on their roles.
3. Output Validation: Validates the output against a test set (optional).

• Python 3.x
• Flask
• Other dependencies listed in requirements.txt

1. Clone the repository:

   git clone https://github.com/yourusername/automated-market-analysis.git
   cd automated-market-analysis

2. install the required dependencies:

   pip install -r requirements.txt
   

1. Start the application by running the main script: pyhton main.py
2. Open your web browser and connect to the default address:

    http://127.0.0.1:5000
   

To use the application, upload the following files

•	List of Companies: A CSV file containing the list of companies to be classified.
•	LLM (Optional): The large language model file.
•	Test Set File (Optional): A file to compare the results and validate the model’s performance.
•	Context File: A file describing the roles of the agents in the market.

The application automates the process of market analysis by:

1.	Scraping data from various sources.
2.	Using the LLM to classify the roles of companies within a market.
3.	Validating the output to ensure high accuracy.

By automating these tasks, the application helps businesses gain valuable insights with minimal manual intervention, thereby enhancing strategic decision-making and competitiveness.

This project is licensed under the MIT License.

Special thanks to all contributors and supporters of this project.