Building a Multimodal AI Chatbot with Flask, Transformers, and BLIP

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Introduction

In today’s rapidly evolving AI landscape, combining multiple modalities—such as text and images—into a single system offers more intuitive and dynamic user interactions. This article guides you through the process of building a lightweight multimodal chatbot using Flask, Hugging Face Transformers, and BLIP (Bootstrapped Language Image Pretraining).

You can find the complete project here:
👉 GitHub: multimodal-chatbot-app

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💡 What is a Multimodal Chatbot?

Unlike traditional chatbots that only handle text, multimodal chatbots process multiple forms of input, such as:

• 🧾 Natural language queries (text)

• 🖼️ Image uploads for captioning

This makes the interaction more versatile—for example, a user can ask a science question or upload an image and get a description of what it contains.

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🧰 Tech Stack

Here’s a breakdown of the tools and frameworks used:

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🚀 How It Works

1. Text-Based Q&A

For science-related questions, the chatbot uses a pre-trained DistilBERT model fine-tuned on the SQuAD dataset to generate answers.

Example:

Q: What is the boiling point of water?
A: The boiling point of water is 100°C.

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2. Image Captioning

When a user uploads an image, the app uses BLIP (by Salesforce) to generate a human-like description of the image.

Example:

📷 Image: A cat on a bench
🧠 Output: “A cat sitting on a wooden bench outdoors.”

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🧪 Installation Guide

⚙️ I recommend using a virtual environment to avoid dependency conflicts.

1. Clone the repo:

git clone https://github.com/EkeminiThompson/multimodal-chatbot-app.git
cd multimodal-chatbot-app

2. Set up environment:

python3 -m venv venv
source venv/bin/activate  # macOS/Linux
# .\venv\Scripts\activate  # Windows

3. Install dependencies:

pip install -r requirements.txt

4. Run the app:

python multimodal_chatbot.py

Open your browser or Postman and access:
🔗 http://127.0.0.1:5000/chat

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🧠 Code Overview

The core logic lives in multimodal_chatbot.py. The /chat endpoint handles both text and image POST requests:

@app.route('/chat', methods=['POST'])
def chat():
    if 'text' in data:
        # handle Q&A
    elif 'image' in request.files:
        # handle image captioning

Both models are loaded during startup using Hugging Face's pipeline() function.

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🧪 Testing the Endpoints

🧾 For Text

curl -X POST http://127.0.0.1:5000/chat \
  -H "Content-Type: application/json" \
  -d '{"text": "What is the chemical formula of water?"}'

🖼️ For Image

Use Postman or any frontend tool to send an image as form data (multipart/form-data) with the key image.

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🔄 Future Enhancements

Here are some features I’m exploring for future iterations:

• 🎨 Frontend interface (React, Streamlit, or simple HTML)

• 🗣️ Voice input using SpeechRecognition

• 🌐 Deploy to Hugging Face Spaces or Render

• 🌍 Support for multilingual interaction

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📘 Final Thoughts

This project is a great way to experiment with combining multiple AI models to build intelligent systems. Whether you're a developer, researcher, or AI enthusiast, creating a multimodal chatbot offers both a technical challenge and a glimpse into the future of human-computer interaction.

Check out the full code and try it yourself 👉 GitHub Repo

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📬 Stay Connected

Let’s connect on:

• GitHub: @EkeminiThompson

• LinkedIn: linkedin.com/in/ekemini-thompson

If you found this helpful, don’t forget to ⭐️ the repo and share the article!

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