feat: add twitter and telegram integration

.env.example

@@ -1,20 +1,26 @@
-# Local variables
-GEMINI_API_KEY=YOUR_GEMINI_API_KEY
+# Google Gemini AI
+GEMINI_API_KEY=your_gemini_api_key
 TUNED_MODEL_NAME=pugo-hilion
 
-# Tuning parameters
+# Twitter/X Bot Settings
+ENABLE_TWITTER=true
+X_API_KEY=your_twitter_api_key
+X_API_KEY_SECRET=your_twitter_api_secret
+X_ACCESS_TOKEN=your_twitter_access_token
+X_ACCESS_TOKEN_SECRET=your_twitter_access_token_secret
+RAPIDAPI_KEY=your_rapidapi_key # get an api key from https://rapidapi.com/davethebeast/api/twitter241
+RAPIDAPI_HOST=twitter241.p.rapidapi.com
+TWITTER_ACCOUNTS_TO_MONITOR=@YourAccount,@AnotherAccount,keyword
+TWITTER_POLLING_INTERVAL=60
+
+# Telegram Bot Settings
+ENABLE_TELEGRAM=true
+TELEGRAM_API_TOKEN=your_telegram_bot_token
+TELEGRAM_ALLOWED_USERS=2323233,32234 # empty to allow all accounts to interact with the bot
+TELEGRAM_POLLING_INTERVAL=5
+
+# Fine-tuning Parameters
 TUNING_SOURCE_MODEL=models/gemini-1.5-flash-001-tuning
 TUNING_EPOCH_COUNT=100
 TUNING_BATCH_SIZE=4
-TUNING_LEARNING_RATE=0.001
-
-# For TEE deployment only
-TEE_IMAGE_REFERENCE=ghcr.io/YOUR_REPO_IMAGE:main
-INSTANCE_NAME=PROJECT_NAME-TEAM-_NAME
-
-# X API (optional)
-X_API_KEY=YOUR_X_API_KEY
-X_API_KEY_SECRET=YOUR_X_API_KEY_SECRET
-X_BEARER_TOKEN=YOUR_X_BEARER_TOKEN
-X_ACCESS_TOKEN=YOUR_X_ACCESS_TOKEN
-X_ACCESS_TOKEN_SECRET=YOUR_X_ACCESS_TOKEN_SECRET
+TUNING_LEARNING_RATE=0.001

README.md

@@ -2,54 +2,78 @@
 
 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:
+- **Secure AI Execution**  
+  Runs within a Trusted Execution Environment (TEE) featuring remote attestation support for robust security.
 
-| 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`                              |
+- **Built-in Chat UI**  
+  Interact with your AI via a TEE-served chat interface.
 
-### Fine tuning a model over a dataset
+- **Gemini Fine-Tuning Support**  
+  Fine-tune foundational models with custom datasets.
 
-1. **Prepare a dataset:**
+- **Social media integrations**  
+  X and Telegram integrations with with rate limiting and retry mechanisms.
+
+## 🎯 Getting Started
+
+### Prerequisites
+
+- [uv](https://docs.astral.sh/uv/getting-started/installation/)
+
+### Fine-tune a model
+
+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**
-   Set the name of the new tuned model in `src/flare_ai_social/tune_model.py`, then:
+3. **Tune a new model:**
+   Depending on the size of your dataset, this process can take several minutes:
 
    ```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
    ```
 
-### Build using Docker (Recommended)
+6. **Start Social Bots (optional):**:
+
+   - Set up Twitter/X API credentials
+   - Configure Telegram bot token
+   - Enable/disable platforms as needed
 
-**Note:** You can only perform this step once you have finishing training a new model.
+   ```bash
+   uv run start-bots
+   ```
+
+### Interact with model
 
 The Docker setup mimics a TEE environment and includes an Nginx server for routing, while Supervisor manages both the backend and frontend services in a single container.
 
-1. **Build the Docker Image:**
+1. **Build the Docker image**:
 
    ```bash
    docker build -t flare-ai-social .
@@ -62,7 +86,32 @@ 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.
+   Open your browser and navigate to [http://localhost:80](http://localhost:80) to interact with the tuned model via the Chat UI.
+
+## 📁 Repo Structure
+
+```plaintext
+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
 
@@ -171,3 +220,7 @@ If you encounter issues, follow these steps:
 
 3. **Check Firewall Settings:**  
    Confirm that your instance is publicly accessible on port `80`.
+
+## 💡 Next Steps
+
+TODO

pyproject.toml

@@ -19,12 +19,18 @@ dependencies = [
     "structlog>=25.1.0",
     "tweepy>=4.15.0",
     "uvicorn>=0.34.0",
+    "aiohttp>=3.9.0",
+    "python-dotenv>=1.0.0",
+    "python-telegram-bot>=20.7",
 ]
 
 [project.scripts]
 start-compare = "flare_ai_social.compare:start"
 start-tuning = "flare_ai_social.tune_model:start"
 start-backend = "flare_ai_social.main:start"
+start-twitter = "flare_ai_social.twitter:start"
+start-telegram = "flare_ai_social.telegram:start"
+start-bots = "flare_ai_social.bot_manager:start_bot_manager"
 
 [build-system]
 requires = ["hatchling"]
@@ -87,4 +93,4 @@ reportUnusedExpression = true
 reportUnnecessaryTypeIgnoreComment = true
 reportMatchNotExhaustive = true
 reportImplicitOverride = true
-reportShadowedImports = true
+reportShadowedImports = true

src/flare_ai_social/__init__.py

@@ -1,10 +1,6 @@
 from flare_ai_social.ai import GeminiProvider
 from flare_ai_social.api import ChatRouter, router
 from flare_ai_social.attestation import Vtpm
+from flare_ai_social.bot_manager import start_bot_manager
 
-__all__ = [
-    "ChatRouter",
-    "GeminiProvider",
-    "Vtpm",
-    "router",
-]
+__all__ = ["ChatRouter", "GeminiProvider", "Vtpm", "router", "start_bot_manager"]