---
title: Financial Qa Agent
emoji: 🚀
colorFrom: red
colorTo: red
sdk: docker
app_port: 8501
tags:
- streamlit
pinned: false
short_description: Streamlit template space
license: mit
---

# Welcome to Streamlit!

Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:

If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
forums](https://discuss.streamlit.io).

# 📊 Financial QA Agent

An AI-powered financial report assistant built with **RAG (Retrieval-Augmented Generation)**.  
This app lets you upload financial reports, search them with semantic embeddings, and get concise answers/summaries using an open-source LLM.

## 🚀 Features
- Cleans financial report text files automatically  
- Generates vector embeddings with FAISS for efficient retrieval  
- Summarizes answers using `google/gemma-2b` (or lightweight models for deployment)  
- Streamlit UI for easy interaction  
- Evaluation pipeline with ROUGE, BLEU, and BERTScore  

## 🛠️ Tech Stack
- **Streamlit** for UI  
- **FAISS** for vector search  
- **Sentence-Transformers** for embeddings  
- **Transformers** (Gemma/LLMs) for summarization  
- **Scikit-learn, NLTK, BERTScore** for evaluation metrics  

## 📂 Project Structure
├── app.py # Main Streamlit app (entrypoint)
├── Embeddings.py # Embedding + FAISS pipeline
├── Data_Cleaning.py # Data cleaning utility
├── Logger.py # Logging utility
├── evaluation.py # Evaluation pipeline
├── config.json # Configurations
├── eval_dataset.json # Sample evaluation dataset
├── requirements.txt # Dependencies
├── README.md # Project documentation
└── .gitignore # Ignore unnecessary files


## ⚡ Running Locally
```bash
pip install -r requirements.txt
streamlit run app.py