Hi everyone (if someone’s there). There’s something magical about teaching computers to understand not just our words, but our intent. I recently made a chatbot with Google’s Gemini 2.0 Flash model - and the best part? You can create it too. Let me walk you through this project that blend cutting-edge AI with practical applications.

1. The Spreadsheet Whisperer

Behind the Curtain

This chatbot project came from a simple question: “Why can’t I just make a chatbot to ask questions about any sort of data I give it and ask questions about it?” Here’s how it works:

# The brain of the operation
llm = Ollama(model="llama3.2-vision")
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-large-en-v1.5")

# Custom prompt engineering
qa_prompt = (
    "Context information:\n{context_str}\n"
    "Answer like a data analyst friend: {query_str}\n"
    "If unsure, just say so - no guessing!"
)

The magic happens in three layers:

  1. Document Alchemy: Converts tables into searchable knowledge using DoclingReader

  2. Contextual Understanding: BAAI embeddings create “mental maps” of data relationships

  3. Conversational Interface: Streamlit provides the chat interface we all recognize

2. Seeing Through AI’s Eyes: Multimodal Chat

When Words Meet Images

This project answers: “What if I could show pictures AND ask questions?” Using Google’s Gemini 2.0 Flash model:

def initialize_gemini():
    # Secure API handling
    genai.configure(api_key=decoded_key)
    return genai.GenerativeModel("gemini-2.0-flash-exp")

# Handling multimodal input
if uploaded_file:
    inputs.append(Image.open(uploaded_file))
response = model.generate_content(inputs)

This implementation does something remarkable:

  1. Seamless blending of visual and textual understanding

  2. Context-aware responses that reference image content

  3. Streaming interface that feels truly conversational

I particularly love how it handles follow-up questions about previously uploaded images.

Making It Work: A Developer’s Journey

Prerequisites

  1. Python 3.10+ environment
  2. Ollama installed and running
  3. 8GB+ RAM (16GB recommended)

Installation Steps

  1. Clone the repository:
git clone https://github.com/piktx/flash-mutimodal-chatbot.git
cd flash-mutimodal-chatbot
  1. Install dependencies:
pip install -r requirements.txt
  1. Start Ollama service:
ollama serve
  1. Launch the Streamlit app:
streamlit run app.py

The interface will automatically open in your default browser at localhost:8501.

Final Thoughts

This project demonstrate how accessible AI has become. Whether you’re:

  • A business user tired of pivot tables or

  • A developer exploring multimodal AI or

  • An AI enthusiast curious about real applications

There’s never been a better time to experiment.

Last but not the least, If you liked this project, don’t forget to give it a star on Github