feat: readme based on the structure

Author: swarna1101

README.md

@@ -2,47 +2,74 @@
 
 Flare AI Kit template for Social AI Agents.
 
-## 🏗️ Build & Run Instructions
+## 🚀 Key Features
 
-**Prepare the Environment File:**  
- Rename `.env.example` to `.env` and update the variables accordingly.
-Some parameters are specific to model fine-tuning:
+- **AI-Powered Social Response**: Automatically monitor and respond to mentions across Twitter/X and Telegram using Gemini AI
+- **Custom Model Fine-tuning**: Train personalized models using your own dataset or provided examples
+- **TEE Security Integration**: Run in Trusted Execution Environment for hardware-level security
+- **Multi-Platform Support**: Single interface to manage multiple social media platforms with rate limiting and retry mechanisms
 
-| Parameter             | Description                                                                | Default                              |
-| --------------------- | -------------------------------------------------------------------------- | ------------------------------------ |
-| `tuned_model_name`    | Name of the newly tuned model.                                             | `pugo-hilion`                        |
-| `tuning_source_model` | Name of the foundational model to tune on.                                 | `models/gemini-1.5-flash-001-tuning` |
-| `epoch_count`         | Number of tuning epochs to run. An epoch is a pass over the whole dataset. | `30`                                 |
-| `batch_size`          | Number of examples to use in each training batch.                          | `4`                                  |
-| `learning_rate`       | Step size multiplier for the gradient updates.                             | `0.001`                              |
+## 🎯 Getting Started
+
+1. **Build with Docker**:
+   ```bash
+   # Build the image
+   docker build -t flare-ai-social .
+   
+   # Run the container
+   docker run -p 80:80 -it --env-file .env flare-ai-social
+   ```
+
+2. **Access UI**: Navigate to `http://localhost:80`
+
+## 🛠 Build Manually
 
 ### Fine tuning a model over a dataset
 
-1. **Prepare a dataset:**
+1. **Prepare Environment File**: Rename `.env.example` to `.env` and update these model fine-tuning parameters:
+
+   | Parameter | Description | Default |
+         |-----------|-------------|---------|
+   | `tuned_model_name` | Name of the newly tuned model | pugo-hilion |
+   | `tuning_source_model` | Name of the foundational model to tune on | models/gemini-1.5-flash-001-tuning |
+   | `epoch_count` | Number of tuning epochs to run. An epoch is a pass over the whole dataset | 30 |
+   | `batch_size` | Number of examples to use in each training batch | 4 |
+   | `learning_rate` | Step size multiplier for the gradient updates | 0.001 |
+
+
+2. **Prepare a dataset:**
    An example dataset is provided in `src/data/training_data.json`, which consists of tweets from
    [Hugo Philion's X](https://x.com/HugoPhilion) account. You can use any publicly available dataset
    for model fine-tuning.
 
-2. **Tune a new model**
+3. **Tune a new model**
    Set the name of the new tuned model in `src/flare_ai_social/tune_model.py`, then:
 
    ```bash
    uv run start-tuning
    ```
 
-3. **Observe loss parameters:**
+4. **Observe loss parameters:**
    After tuning in complete, a training loss PNG will be saved in the root folder corresponding to the new model.
    Ideally the loss should minimize to near 0 after several training epochs.
 
    ![pugo-hilion_mean_loss](https://github.com/user-attachments/assets/f6c4d82b-678a-4ae5-bfb7-39dc59e1103d)
 
-4. **Test the new model**
+5. **Test the new model**
    Select the new tuned model and compare it against a set of prompting techniques (zero-shot, few-shot and chain-of-thought):
 
    ```bash
    uv run start-compare
    ```
 
+6. **Start Social Bots**:
+   - Set up Twitter/X API credentials
+   - Configure Telegram bot token
+   - Enable/disable platforms as needed
+   ```bash
+   uv run start-bots
+   ```
+
 ### Build using Docker (Recommended)
 
 **Note:** You can only perform this step once you have finishing training a new model.
@@ -64,6 +91,31 @@ The Docker setup mimics a TEE environment and includes an Nginx server for routi
 3. **Access the Frontend:**  
    Open your browser and navigate to [http://localhost:80](http://localhost:80) to interact with the Chat UI.
 
+## 📁 Repo Structure
+
+```
+src/flare_ai_social/
+├── ai/                            # AI Provider implementations
+│   ├── base.py                    # Base AI provider abstraction
+│   ├── gemini.py                  # Google Gemini integration
+│   └── openrouter.py             # OpenRouter integration
+├── api/                           # API layer
+│   └── routes/                    # API endpoint definitions
+├── attestation/                   # TEE attestation implementation
+│   ├── vtpm_attestation.py       # vTPM client
+│   └── vtpm_validation.py        # Token validation
+├── prompts/                       # Prompt engineering templates
+│   └── templates.py              # Different prompt strategies
+├── telegram/                      # Telegram bot implementation
+│   └── service.py                # Telegram service logic
+├── twitter/                       # Twitter bot implementation
+│   └── service.py                # Twitter service logic
+├── bot_manager.py                # Bot orchestration
+├── main.py                       # FastAPI application
+├── settings.py                   # Configuration settings
+└── tune_model.py                 # Model fine-tuning utilities
+```
+
 ## 🚀 Deploy on TEE
 
 Deploy on a [Confidential Space](https://cloud.google.com/confidential-computing/confidential-space/docs/confidential-space-overview) using AMD SEV.
@@ -171,3 +223,7 @@ If you encounter issues, follow these steps:
 
 3. **Check Firewall Settings:**  
    Confirm that your instance is publicly accessible on port `80`.
+
+## 💡 Next Steps
+
+TODO