🌍 GeoVQA — End-to-End Geographical Visual Question Answering System

GeoVQA is a fully-integrated multimodal Visual Question Answering (VQA) application that interprets and answers geographical questions about images.
It leverages CLIP-based encoders, multimodal retrieval-augmented generation (RAG), and a GPT-2 decoder to generate contextually aware, human-like answers.

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✨ Key Features

• 🧠 Multimodal Understanding — Fuses image and text representations through CLIP-based encoders and cross-attention fusion.
• 🔍 Retrieval-Augmented Generation (RAG) — Uses a multimodal retriever to enhance contextual reasoning and factual grounding.
• 🗣️ Natural Language Generation — GPT-2 based decoder generates coherent, context-aware responses.
• 🌐 End-to-End Architecture — Deployed as a REST API backend with a clean modern frontend interface.
• ⚙️ Scalable Infrastructure — Integrated with NGINX for reverse proxying and ngrok for secure external access.

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🧩 System Architecture

            ┌────────────────────┐
            │     Frontend       │
            │ (React / Modern UI)│
            └────────┬───────────┘
                     │ REST API
            ┌────────┴───────────┐
            │     Backend API    │
            │ (FastAPI / Flask)  │
            └────────┬───────────┘
                     │
     ┌───────────────┴────────────────┐
     │         Model Server           │
     │                                │
     │  🖼️ CLIP Encoder (Image/Text) │
     │  🔗 Cross Attention Fusion    │
     │  🔍 Multimodal Retriever (RAG)│
     │  🧩 GPT-2 Decoder (Answer Gen)│
     └───────────────┬────────────────┘
                     │
              ┌──────┴──────┐
              │   NGINX     │
              └──────┬──────┘
                     │
                 🌐 ngrok

🚀 Tech Stack

Component	Technology
Frontend	React.js, Tailwind CSS
Backend	Python (FastAPI / Flask)
Model	CLIP (Encoder), Cross-Attention Fusion, Multimodal RAG, GPT-2 (Decoder)
Serving	NGINX, Gunicorn / Uvicorn
Exposure	ngrok (Localhost tunneling)

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🧠 Workflow

1. Input: User uploads a geographical image and enters a textual question.
2. Encoding: CLIP encodes both image and question into a shared embedding space.
3. Fusion: Image tokens are projected and cross-attended with text embeddings to form a correlated tensor.
4. Retrieval: The retriever fetches relevant multimodal context (RAG).
5. Generation: GPT-2 decoder generates a final, context-grounded answer.
6. Output: The system returns a natural language answer with optional visual annotations.

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⚙️ Setup & Installation

# Clone the repository
git clone https://github.com/yourusername/GeoVQA.git
cd GeoVQA

# Install dependencies
pip install -r requirements.txt

# Start backend server
uvicorn app.main:app --reload

# Start frontend
cd frontend
npm install
npm start

# Run NGINX and expose via ngrok
sudo service nginx start
ngrok http 80

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🧾 Example

Input: Image: Satellite view of the Himalayas Question: “Which mountain range is shown in this image?”

Output:

“This image shows the Himalayan mountain range, located in South Asia.”

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📦 Folder Structure

GeoVQA/
│
├── backend/
│   ├── app/
│   │   ├── main.py
│   │   ├── model_handler.py
│   │   └── retriever.py
│
├── model/
│   ├── clip_encoder.py
│   ├── fusion_layer.py
│   ├── rag_retriever.py
│   └── gpt2_decoder.py
│
├── frontend/
│   ├── src/
│   └── public/
│
├── nginx/
│   └── nginx.conf
│
├── requirements.txt
└── README.md

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🧪 Future Enhancements

• 🌏 Integration with geospatial datasets for factual grounding.
• 📈 Support for larger LLM decoders (GPT-3, LLaMA).
• 🎯 Region-based visual grounding and attention visualization.

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👩‍💻 Contributors

Name	Role
Sapna Vishwakarma	Research & Development
Devesh Sharma	Full-Stack Integration

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📜 License

This project is released under the MIT License. See LICENSE for details.

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🖼️ Optional: Preview

You can add screenshots or demo gifs here once your frontend is running:

![GeoVQA Interface](docs/demo_ui.png)

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🧩 Badges

Add GitHub badges for better visibility (optional):

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🌐 GeoVQA — Bridging geography and intelligence through multimodal AI.