github: add images, update README

README.md

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 # SmolChat - On-Device Inference of SLMs in Android
 
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 ## Project Goals
 
 - Provide a usable user interface to interact with local SLMs (small language models) locally, on-device
 - Allow users to add/remove SLMs (GGUF models) and modify their system prompts or inference parameters (temperature, 
   min-p)
+- Allow users to create specific-downstream tasks quickly and use SLMs to generate responses
+- Simple, easy to understand, extensible codebase
+
+## Setup
+
+1. Clone the repository with its submodule originating from llama.cpp,
+
+```commandline
+git clone --depth=1 https://github.com/shubham0204/SmolChat-Android
+cd SmolChat-Android
+git submodule update --init --recursive
+```
+
+2. Android Studio starts building the project automatically. If not, select **Build > Rebuild Project** to start a project build.
+
+3. After a successful project build, [connect an Android device](https://developer.android.com/studio/run/device) to your system. Once connected, the name of the device must be visible in top menu-bar in Android Studio.
+
+## Working
+
+1. The application uses llama.cpp to load and execute GGUF models. As llama.cpp is written in pure C/C++, it is easy 
+   to compile on Android-based targets using the [NDK](https://developer.android.com/ndk). 
+
+2. The `smollm` module uses a `llm_inference.cpp` class which interacts with llama.cpp's C-style API to execute the 
+   GGUF model and a JNI binding `smollm.cpp`. Check the [C++ source files here](https://github.com/shubham0204/SmolChat-Android/tree/main/smollm/src/main/cpp). On the Kotlin side, the [`SmolLM`](https://github.com/shubham0204/SmolChat-Android/blob/main/smollm/src/main/java/io/shubham0204/smollm/SmolLM.kt) class provides 
+   the required methods to interact with the JNI (C++ side) bindings.
+
+3. The `app` module contains the application logic and UI code. Whenever a new chat is opened, the app instantiates 
+   the `SmolLM` class and provides it the model file-path which is stored by the [`LLMModel`](https://github.com/shubham0204/SmolChat-Android/blob/main/app/src/main/java/io/shubham0204/smollmandroid/data/DataModels.kt) entity in the ObjectBox.
+   Next, the app adds messages with role `user` and `system` to the chat by retrieving them from the database and 
+   using `LLMInference::add_chat_message`.
+
+4. For tasks, the messages are not persisted, and we inform to `LLMInference` by passing `store_chats=false` to 
+   `LLMInference::load_model`.
 
 ## Technologies
 
 * [ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp) is a pure C/C++ framework to execute machine learning 
   models on multiple execution backends. It provides a primitive C-style API to interact with LLMs 
-  converted to the [GGUF format](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md) native to [ggml]
-  (https://github.com/ggerganov/ggml)/llama.cpp. The app uses JNI bindings to interact with a small class `smollm.
+  converted to the [GGUF format](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md) native to [ggml](https://github.com/ggerganov/ggml)/llama.cpp. The app uses JNI bindings to interact with a small class `smollm.
   cpp` which uses llama.cpp to load and execute GGUF models.
 
 * [ObjectBox](https://objectbox.io) is a on-device, high-performance NoSQL database with bindings available in multiple 
@@ -20,4 +83,16 @@
 
 * [noties/Markwon](https://github.com/noties/Markwon) is a markdown rendering library for Android. The app uses 
   Markwon and [Prism4j](https://github.com/noties/Prism4j) (for code syntax highlighting) to render Markdown responses 
-  from the SLMs.
+  from the SLMs.
+
+## Future
+
+The following features/tasks are planned for the future releases of the app:
+
+- Allow non-ASCII responses from the SLM i.e. pass a buffer containing non-ASCII from C++ to Kotlin
+- Assign names to chats automatically (just like ChatGPT and Claude)
+- Add a search bar to the navigation drawer to search for messages within chats using ObjectBox's query capabilities
+- Add a background service which uses BlueTooth/HTTP/WiFi to communicate with a desktop application to send queries 
+  from the desktop to the mobile device for inference
+- Enable auto-scroll when generating partial response in `ChatActivity`
+- Measure token generation speed (tokens/second) and RAM consumption