🧠 Smart Report Analyzer(Hugging Face + Streamlit)

Smart Report Analyzer is an LLM-powered web app built with Streamlit, powered by Hugging Face models, that intelligently summarizes and analyzes structured data (Excel/CSV) and unstructured text (PDF). It can also answer questions about the uploaded content using LLMs.

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🚀 Features

• 📄 Upload PDF, CSV, or Excel files
• 🔍 Automatic data preview and exploratory data analysis (EDA)
• ✨ Generate smart summaries from structured or unstructured content
• 💬 Ask natural language questions and get answers powered by Hugging Face LLMs
• 📊 Dynamic visualizations with Plotly

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🧰 Tech Stack

• Streamlit
• Transformers (Hugging Face)
• PyTorch
• Pandas, Plotly, PDFplumber, OpenPyXL

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📁 Project Structure

Smart-Report-Analyzer/ ├── app.py # Main Streamlit app

├── requirements.txt

├── .env # Contains your Hugging Face token (not to be pushed)

├── utils/

   ├── file_handler.py # Handles file upload and parsing 
   
   ├── eda.py # Data visualization (EDA) 
   
   └── llm_agent.py # LLM summarization and Q&A logic 

   ├── sample_sales_data.xlsx # Test Excel file (optional) 
    
   ├── sample_report.pdf # Test PDF file (optional) 

└── README.md

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🛠️ Setup Instructions

1. Clone the Repository

git clone https://github.com/your-username/smart-report-analyzer.git

cd smart-report-analyzer

2. Create and Activate Virtual Environment

python -m venv venv venv\Scripts\activate # On Windows

OR

source venv/bin/activate # On Mac/Linux

3. Install Dependencies

pip install -r requirements.txt

4. Create .env File

Create a .env file in the root directory and add your Hugging Face token:

HUGGINGFACE_HUB_TOKEN=hf_xxxxxxxxxxxxxxxxxxxxxx

You can generate a free token from your Hugging Face account here: 👉 https://huggingface.co/settings/tokens

▶️ Run the App

streamlit run app.py

The app will open in your browser at http://localhost:8501.

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📦 Sample Files

Use the included sample files to test:

sample_sales_data.xlsx – Structured Excel data

sample_report.pdf – Business-style unstructured report

✅ Models Used

Purpose Model

Summarization knkarthick/MEETING_SUMMARY

Table Q&A google/tapas-large-finetuned-wtq

PDF/Text Q&A google/flan-t5-small

🔐 Important Notes

Never commit your .env file or token to GitHub.

You can add .env to .gitignore:

.env

📌 TODOs / Future Improvements

Add support for larger files or chunked analysis

Upload multiple files for comparison

Deploy to Streamlit Cloud or HuggingFace Spaces

Improve accuracy using retrieval-augmented generation (RAG)

📄 License

MIT License – free to use and modify.

💡Author

Created by Sreeja Bethu 🔗 LinkedIn (linkedin.com/in/sreejabethu)

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Would you like me to:

• Help write the requirements.txt from your current setup?
• Generate .gitignore for Python + Streamlit?
• Zip this project structure for upload?

Let’s get you live on GitHub and ready to showcase 💫