📊Analyzing Income Statements Using Large Language Models (LLMs) Welcome to Analyzing Income Statements Using LLMs – a project that harnesses the power of AI to automate the analysis of complex financial documents. By utilizing LLMs, this tool can extract, interpret, and summarize key financial metrics from income statements and balance sheets with precision.

🚀 Key Features 📄 Upload PDF Reports – Effortlessly upload income statements or balance sheets for AI-driven analysis. 🧠 AI-Powered Summarization – Leverages LLMs to generate concise, accurate financial summaries. 🔍 Key Metrics Extraction – Extracts essential data points such as revenue, expenses, and profit margins. 📊 Real-time Analysis – Provides instant insights after document upload and processing. 💼 Business-Driven Insights – Designed to support businesses and financial analysts with quick, reliable financial overviews. 🛠️ Installation & Setup Follow these steps to get the project running locally:

1. Clone the Repository bash Copy code git clone https://github.com/Nidhi18-git/Analyzing_Income_statements_using_LLm.git
   cd Analyzing_Income_statements_using_LLm
2. Set Up Virtual Environment (Optional but Recommended) bash Copy code python -m venv venv Activate the Virtual Environment: Windows: venv\Scripts\activate macOS/Linux: source venv/bin/activate
3. Install Dependencies bash Copy code pip install -r requirements.txt 🖥️ How to Use Run the Application bash Copy code streamlit run app.py Upload Your PDF Use the drag-and-drop feature or the upload button to select an income statement PDF. Generate Insights AI will analyze the PDF and summarize key financial metrics in seconds. 📁 Project Structure bash Copy code Analyzing_Income_statements_using_LLm/ ├── app.py # Main Streamlit application
   ├── requirements.txt # Project dependencies
   ├── utils/
   │ ├── data_extraction.py # PDF data extraction logic
   │ └── analysis.py # Core financial analysis methods
   ├── README.md # Project documentation
   └── assets/ # Additional resources and icons
   🧰 Technology Stack Frontend: Streamlit – For building the interactive web interface Backend: Python (PyPDF2, Pandas) – For PDF parsing and data processing AI Model: OpenAI LLMs – For summarization and key insight extraction 🌐 Contributing Contributions are welcome! Here's how you can help:

Fork this repository Create a new branch (feature/YourFeature) Commit your changes Push to your branch Open a Pull Request 📝 License This project is licensed under the MIT License.

📧 Contact For inquiries or collaboration opportunities, feel free to reach out