README.md

metadata

title: Ema Chatbot
emoji: 🛠️
colorFrom: purple
colorTo: blue
sdk: streamlit
sdk_version: 1.41.1
app_file: app.py
pinned: false

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📚 Ema Chatbot — AI-Powered Academic Assistant

In research and studies, it's essential that students get accurate answers to their questions based on their own study material, rather than from unreliable sources.

Ema Chatbot solves this by allowing students to upload their digital study materials (PDFs or videos), which are then summarized and indexed using RAG (Retrieval-Augmented Generation). A Large Language Model (LLM) uses this context to generate clear and easy-to-understand answers to their queries.

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⚙️ Setting Up the Repository

Follow these steps to get started:

1. Clone the repository

git clone https://huggingface.co/spaces/Mohith202/Ema_Chatbot
cd Ema_Chatbot

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2. Using Conda (Recommended)

Make sure you have Conda installed.

conda create --name Ema-chatbot python=3.10
conda activate Ema-chatbot

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3. Or Using Docker

If you don’t have Conda but have Docker:

docker run -it -p 7860:7860 --platform=linux/amd64 \
  -e Together_API="YOUR_VALUE_HERE" \
  registry.hf.space/mohith202-ema-chatbot:latest streamlit run app.py

🔑 Replace "YOUR_VALUE_HERE" with your Together API key.

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4. Or Using Python Virtual Environment

If you only have Python installed:

python -m venv Ema-chatbot
# On Windows
.\Ema-chatbot\Scripts\activate
# On macOS/Linux
source Ema-chatbot/bin/activate

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5. Install Dependencies

pip install -r requirements.txt

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6. Run the Application

streamlit run app.py

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💡 How to Use

• Upload a PDF or video using the left sidebar.

• Wait for the system to process and summarize the content.

• Ask any question from your material — the chatbot will:

  • Show the relevant extracted context from your document to maintain transparency.
  • Answer in an easy-to-understand format.
  • Optionally, generate 5 sample Q&A pairs from the uploaded document.

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