Update README.md

README.md

@@ -11,4 +11,126 @@ license: mit
 short_description: Streamlit app for Visual QA using VILT model to answer image
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Image-Based Question Answering System
+
+## Overview
+This repository contains two projects:
+1. **Complete Web Application** – A full-stack web app built using Streamlit for both frontend and backend.
+2. **Flask API Backend** – A standalone Flask-based backend API.
+
+Both implementations allow users to upload an image and ask questions about it. The system uses the **dandelin/vilt-b32-finetuned-vqa** model to analyze and respond to queries based on the provided image.
+
+## Features
+- Users can upload an image.
+- Users can ask questions related to the uploaded image.
+- The model processes the image and answers questions based on its content.
+- Two implementations:
+  - **Streamlit Web App:** A complete frontend and backend application.
+  - **Flask API:** A RESTful API for backend processing.
+
+## Technology Stack
+- **Frontend:** Streamlit (for the web app UI)
+- **Backend:** Flask (for the API)
+- **Model:** `dandelin/vilt-b32-finetuned-vqa`
+- **Libraries:** PyTorch, Transformers, Pillow, OpenCV, Requests
+
+---
+
+
+## Live Demo
+You can test the application live at:  
+[Visual QNA with image](https://huggingface.co/spaces/Tahir5/Visual-QNA)
+
+
+## Installation & Setup
+### 1. Clone the Repository
+```bash
+git clone https://github.com/your-repo/image-vqa.git
+cd image-vqa
+```
+
+### 2. Install Dependencies
+```bash
+pip install -r requirements.txt
+```
+
+### 3. Run the Streamlit Web App
+```bash
+streamlit run stream.py
+```
+
+### 4. Run the Flask API
+```bash
+python flask_app.py
+```
+
+---
+
+## API Endpoints (For Flask Backend)
+### 1. Visual Question Answering (VQA)
+**Endpoint:** `POST /vqa`
+- **Description:** Processes an image and a question, returning an answer.
+- **Request Format:** Multipart form-data
+  - `image`: The uploaded image file.
+  - `question`: The question related to the image.
+- **Response Format:** JSON
+
+**Example Request (cURL):**
+```bash
+curl -X POST "http://127.0.0.1:5000/vqa" \
+     -F "image=@path/to/image.jpg" \
+     -F "question=What is in the image?"
+```
+
+**Example Response:**
+```json
+{
+  "question": "What is in the image?",
+  "answer": "A cat sitting on a table."
+}
+```
+
+---
+
+## Testing with Postman
+### Steps to Test the Flask API in Postman
+1. Open **Postman**.
+2. Select **POST** request.
+3. Enter the request URL: `http://127.0.0.1:5000/vqa`.
+4. Navigate to the **Body** tab and select **form-data**.
+5. Add two key-value pairs:
+   - **Key:** `image` → Select an image file.
+   - **Key:** `question` → Enter a text question related to the image.
+6. Click **Send**.
+7. View the response containing the model's answer in JSON format.
+
+---
+
+## Example Usage
+### Streamlit Web App
+1. Open the app in the browser.
+2. Upload an image.
+3. Enter a question.
+4. View the model's response.
+
+### Flask API
+1. Send a `POST` request to `/vqa` with an image and a question.
+2. Receive the model-generated answer in JSON format.
+
+---
+
+## Model Information
+- **Name:** `dandelin/vilt-b32-finetuned-vqa`
+- **Functionality:** Vision-and-Language Transformer (ViLT) model fine-tuned for Visual Question Answering (VQA).
+- **Source:** [Hugging Face Model Hub](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+
+---
+
+## Contributing
+Feel free to contribute by opening issues or submitting pull requests.
+
+---
+
+## License
+This project is licensed under the MIT License.
+