Update README.md (#1)

- Update README.md (101d3962248e9a4c53cb42ab46492599351ee10b)

README.md

@@ -8,5 +8,72 @@ pinned: false
 license: other
 short_description: final submission
 ---
+# MistralAI_GameJam2025
+This repo hosts Team'7 project for the Mistral AI Game Jam 2025. Thanks to Mistral, Huggingface, Scaleway and ElevenLabs for providing the ressources!
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Game Description
+
+TODO
+
+## Architecture
+![Architecture Diagram](readme_assets/mermaid-diagram-2025-01-25-105038.svg)
+
+## How to Build the Front-End
+
+This project contains three main components, the Python LLM middleware (using Django), Web interface (using Vite React), and Unity 6 game project.
+
+To build the front-end code, you must follow the following steps:
+
+1. Build the game
+
+Open the `./unity` folder through Unity Hub with Unity 6. Then, switch the build target to `Web`.
+
+2. Prepare the build folder
+
+When building the game, you'll be asked to choose the name and path of the output folder. Make sure you set that as `gamejamproj`, or all the contents of the `Build` folder has that name.
+
+Next, rename the `Build` folder to `build` (only for consistency. You can change the build path and name in `./app/src/config`).
+
+3. Build the Web UI
+
+Move the `build` folder to the `.app/public` folder.
+Go to the `./app` folder, and run `yarn` to install the dependencies, and `yarn build` to build everything. The build output should be stored in the `./app/dist` folder.
+
+4. Move the Unity build to the web UI
+
+Make sure the `build` folder that contains the Unity WebGL binaries are in the same path as the `index.html` file in the `dist` folder.
+
+5. Run the page
+
+You can deploy the page or run the `index.html` through a local HTML server. Note that due to WASM policy, you cannot just double-click the `.html` file and load it in your browser to play the game.
+
+
+## Backend
+
+# Personality-Based Decision Workflow
+![Personalities workflow ](readme_assets/personalities_workflow.svg)
+This flowchart illustrates the decision-making process for an AI personality-based word-guessing game. It visually represents how the AI determines its behavior and output based on its assigned personality trait.
+
+## Workflow Overview
+
+1. **Start Node**: The process begins by parsing the `BASE_PROMPT` and inserting relevant context.  
+2. **Personality Decision**: The workflow checks the assigned personality trait (`sensitive_to_compliments`, `rebellious`, `stubborn`, `lazy`, `normal`, or `overthinker`), which determines how the AI interprets advice and makes guesses.
+   - **Sensitive to Compliments**: Trusts advice only when it includes compliments.  
+   - **Rebellious**: Challenges or twists user advice, often defying direct suggestions.  
+   - **Stubborn**: Prefers to stick with previous guesses unless provided overwhelming evidence.  
+   - **Lazy**: Takes minimal effort, often choosing random or obvious guesses.  
+   - **Normal**: Processes advice straightforwardly and logically.  
+   - **Overthinker**: Analyzes hints from multiple angles, often second-guessing decisions.  
+3. **Making Guesses**: Based on the selected personality, rules and context are applied to propose guesses.  
+4. **Satisfaction Rating**: The AI evaluates the advice, assigning a satisfaction rating (`0`, `1`, or `2`) based on its helpfulness or relevance.  
+5. **Output Assembly**: A JSON object is created, containing the guesses, satisfaction rating, and reasoning.  
+6. **End Node**: The process completes with the assembled output.
+
+## Visual Key
+
+- **Purple (Decision Nodes)**: Indicate points where a decision is made (e.g., personality check, satisfaction rating).  
+- **Pink (Personality Nodes)**: Represent personality-specific rules and behavior.  
+- **Blue (Process Nodes)**: Represent operational steps, such as parsing prompts and proposing guesses.  
+- **Green (Start)** and **Red (End)**: Highlight the start and end of the workflow.
+
+This structured workflow ensures that each personality behaves uniquely, adding diversity to AI interactions. The color-coded nodes make the chart easy to follow and visually intuitive.