GeminiFileWiz

Welcome to GeminiFileWiz! This is a file summarization and question-answering application that allows users to upload various document types (PDF, Word, PowerPoint, Excel, CSV) and ask questions based on the document content. It uses the power of Google Gemini API to summarize documents and provide accurate answers to user queries

Web UI

Features

• Supports multiple file formats (PDF, Word, PowerPoint, Excel, CSV)
• Summarizes the uploaded files using the Google Gemini API
• Allows users to ask questions based on the content of the documents
• Provides fast document searching using FAISS (Facebook AI Similarity Search)
• Simple, user-friendly interface built with Streamlit

Technologies Used

• Streamlit: The project uses Streamlit to create a web-based user interface.
• Google Gemini API: Utilized for summarizing the content of the uploaded documents.
• FAISS: Employed for fast document similarity searches.
• PyPDF2: Used for extracting text from PDF files.
• python-docx: Used for extracting text from Word documents.
• python-pptx: Used for extracting text from PowerPoint presentations.
• Pandas: Used for handling Excel and CSV file operations.
• dotenv: Used for managing environment variables securely.

Installation

1. Clone the repository:

git clone https://github.com/DadvaiahPavan/GeminiFileWiz.git

2. Create a virtual environment:

python -m venv venv

3. Activate the virtual environment:

• For Windows:

venv\Scripts\activate

• For macOS/Linux:

source venv/bin/activate

4. Install the required dependencies:

pip install -r requirements.txt

5. Set Google Gemini API:

• Obtain your Google Gemini API key from the Google Cloud Console.
• Create a .env file in the root directory of the project and add your API key as follows:

GOOGLE_API_KEY=your_api_key_here

6. Start the application:

streamlit run app.py

7. Open your web browser and visit http://localhost:5000 to access the Application.

Supported File Formats

• PDF: Extracts text from PDF files
• Word (.docx): Extracts text from Word documents
• PowerPoint (.pptx): Extracts text from PowerPoint presentations
• Excel (.xlsx, .xls): Extracts text from Excel files
• CSV: Extracts text from CSV files

Authors

• Dadvaiah Pavan

Contribution

Contributions are welcome! If you find any issues or have suggestions for improvements, please feel free to create an issue or submit a pull request.

License

MIT License

Contact

For any inquiries or further information, please contact [Pavan Dadvaiah] via email at [pavannetha219@gmail.com].

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