Edited README.md

README.md

@@ -17,47 +17,231 @@ pinned: false
 4. [API Endpoints and Usage](#api-endpoints-and-usage)
 5. [Instructions for Building and Running the Docker Container](#instructions-for-building-and-running-the-docker-container)
 6. [Deployment Details](#deployment-details)
-7. [Information on How to Use the UI](#information-on-how-to-use-the-ui)
+7. [Running Tests](#running-tests)
+8. [Information on How to Use the UI](#information-on-how-to-use-the-ui)
 
 ---
 
 ## Project Overview
-Description of the Prompt Search Engine and its purpose.
+The Prompt Search Engine is designed to address the growing need for high-quality prompts used in AI-generated content, 
+particularly for models like Stable Diffusion. By leveraging a database of existing prompts, 
+this search engine helps users discover the most relevant and effective prompts, significantly enhancing the quality of generated images.
+
+The main goal of the prompt search engine is to return the top n most similar prompts with respect to the input prompt query. 
+This way, we can generate higher quality images by providing better prompts for the Stable Diffusion models.
+
+### Technology Used
+
+This project leverages a modern tech stack to deliver efficient search functionality:
+
+1. **FastAPI**: A high-performance web framework for building the backend API.
+2. **Gradio**: A lightweight UI framework for creating the frontend interface.
+3. **Hugging Face Spaces**: For hosting the application using Docker.
+4. **Hugging Face Datasets**: Downloads and processes the `google-research-datasets/conceptual_captions` dataset at runtime.
+5. **Uvicorn**: ASGI server for running the FastAPI application.
+6. **Python**: Core language used for development and scripting.
 
 ---
 
 ## Environment Setup
-Steps to install dependencies, configure settings, and prepare the environment.
 
----
+To set up the environment for the Prompt Search Engine, follow these steps:
+
+### Prerequisites
+
+1. **Python**: Ensure Python >= 3.9 is installed. You can download it from [Python.org](https://www.python.org/downloads/).
+2. **Docker**: Install Docker to containerize and deploy the application. Visit [Docker's official site](https://www.docker.com/get-started) for installation instructions.
+3. **Conda (Optional)**: Install Miniconda or Anaconda for managing a virtual environment locally.
+
+### Steps to Install Dependencies
+
+1. Navigate to the project directory:
+   ```bash
+   cd <project-directory>
+   ```
+
+2. Create and activate a Conda environment (optional):
+   ```bash
+   conda create -n prompt_search_env python={version} -y
+   conda activate prompt_search_env
+   ```
+   - Replace `{version}` with your desired Python version (e.g., 3.9).
+
+3. Install dependencies inside the Conda environment using `pip`:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. Review and update the `config.py` file to match your environment, such as specifying API keys or dataset paths.
 
 ## Run the Project
-Detailed instructions to execute the main script.
-```commandline
-python run.py
-python -m fe.gradio_app
-```
----
 
-## API Endpoints and Usage
-List of API endpoints, methods, input parameters, and example requests.
+You can run the application locally using either a Conda environment or Docker:
 
----
+- **Using Conda Environment:**
+  1. Start the backend API. Swagger documentation will be accessible at `http://0.0.0.0:8000/docs`:
+     ```bash
+     python run.py
+     ```
+  2. Run the frontend application:
+     ```bash
+     python -m fe.gradio_app
+     ```
+  The frontend will be accessible at `http://0.0.0.0:7860`.
+
+- **Using Docker:**
+  Refer to the instructions in the next section for building and running the Docker container.
 
 ## Instructions for Building and Running the Docker Container
-Steps to create and run the Docker container for the application.
 
-```commandline
-docker-compose build --no-cache
-docker-compose up
-docker-compose down --volumes
+1. Build the Docker image:
+   ```bash
+   docker build -t prompt-search-engine .
+   ```
+
+2. Run the Docker container:
+   ```bash
+   docker run -p 8000:8000 -p 7860:7860 prompt-search-engine
+   ```
+
+   - The backend API will be accessible at `http://0.0.0.0:8000/docs`.
+   - The frontend will be accessible at `http://0.0.0.0:7860`.
+
+Your environment is now ready to use the Prompt Search Engine.
+
+## API Endpoints and Usage
+
+### `/search` (GET)
+Endpoint for querying the search engine.
+
+#### Parameters:
+- `query` (str): The search query. **Required**.
+- `n` (int): Number of results to return (default: 5). Must be greater than or equal to 1.
+
+#### Example Request:
+```bash
+curl -X GET "http://0.0.0.0:8000/search?query=example+prompt&n=5"
 ```
+
+#### Example Response:
+```json
+{
+    "query": "example prompt",
+    "results": [
+        {"score": 0.95, "prompt": "example similar prompt 1"},
+        {"score": 0.92, "prompt": "example similar prompt 2"}
+    ]
+}
+```
+
 ---
 
 ## Deployment Details
-Explanation of the deployment process and platform-specific configurations.
 
----
+### Overview
+This section outlines the steps to deploy the **Prompt Search Engine** application using Docker and Hugging Face Spaces. The application comprises a backend (API) and a frontend (Gradio-based UI) that runs together in a single Docker container.
+
+### Prerequisites
+
+1. A [Hugging Face account](https://huggingface.co/).
+2. Git installed locally.
+3. Access to the project repository on GitHub.
+4. Docker installed locally for testing.
+5. A Hugging Face **Access Token** (needed for authentication).
+
+### Deployment Steps
+
+1. **Create a Hugging Face Space:**
+   - Log in to [Hugging Face Spaces](https://huggingface.co/spaces).
+   - Click on **Create Space**.
+   - Fill in the details:
+     - **Space Name**: Choose a name like `promptsearchengine`.
+     - **SDK**: Select `Docker`.
+     - **Visibility**: Choose between public or private.
+   - Click **Create Space** to generate a new repository.
+
+2. **Create a Hugging Face Access Token:**
+   - Log in to [Hugging Face](https://huggingface.co/).
+   - Navigate to **Settings** > **Access Tokens**.
+   - Click **New Token**:
+     - **Name**: `Promptsearchengine Deployment`.
+     - **Role**: Select `Write`.
+   - Copy the token. You’ll need it for pushing to Hugging Face Spaces.
+
+3. **Test the Application Locally:**
+
+   ```bash
+   docker build -t promptsearchengine . 
+   docker run -p 8000:8000 -p 7860:7860 promptsearchengine
+   ```
+
+   - **Backend**: Test at `http://localhost:8000`.
+   - **Frontend**: Test at `http://localhost:7860`.
+
+4. **Prepare the Project for Hugging Face Spaces:**
+
+   - Ensure the `Dockerfile` is updated for Hugging Face Spaces:
+     - Set environment variables for writable directories (e.g., `HF_HOME=/tmp/huggingface`).
+   - Ensure a valid `README.md` is present at the root with the Hugging Face configuration:
+     ```markdown
+     ---
+     title: Promptsearchengine
+     emoji: 🔍
+     colorFrom: blue
+     colorTo: indigo
+     sdk: docker
+     pinned: false
+     ---
+     ```
+
+5. **Push the Project to Hugging Face Spaces:**
+
+    ```bash
+    git remote add space https://huggingface.co/spaces/<your-username>/promptsearchengine
+    git push space main
+    ```
+
+6. **Monitor the Build Logs:**
+
+    - Navigate to your Space on Hugging Face.
+    - Monitor the "Logs" tab to ensure the build completes successfully.
+
+### Testing the Deployment
+
+Once deployed, test the application on `https://huggingface.co/spaces/<your-username>/promptsearchengine`.
+
+
+## Running Tests
+
+Execute all the tests by running in the terminal within your local project environment:
+
+```bash
+python -m pytest -vv tests/
+```
+
+### Test Structure
+
+- **Unit Tests**: Focus on isolated functionality, like individual endpoints or methods.
+- **Integration Tests**: Verify end-to-end behavior using real components.
+
 
 ## Information on How to Use the UI
-Guidelines on interacting with the user interface and interpreting the results.
+
+The **Prompt Search Engine** interface is designed for simplicity and ease of use. Follow these steps to interact with the application:
+
+1. **Enter Your Query**: 
+   - In the "Enter your query" field, type a phrase or keywords for which you want to find related prompts.
+2. **Set the Number of Results**: 
+   - Use the "Number of top results" field to specify how many similar prompts you want to retrieve. Default is 5.
+3. **Submit a Query**:
+   - Click the **Search** button to execute your query and display results in real-time.
+4. **View Results**:
+   - The results will display in a table with the following columns:
+     - **Prompt**: The retrieved prompts that are most similar to your query.
+     - **Similarity**: The similarity score between your query and each retrieved prompt.
+5. **Interpreting Results**:
+   - Higher similarity scores indicate a closer match to your query.
+   - Use these prompts to refine or inspire new input for your task.
+
+The clean, dark theme is optimized for readability, making it easier to analyze and use the results effectively.
+