https://github.com/fullstackcodingguy/jabi
Just a bot idea - personal task management assistant.
https://github.com/fullstackcodingguy/jabi
Last synced: about 1 year ago
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Just a bot idea - personal task management assistant.
- Host: GitHub
- URL: https://github.com/fullstackcodingguy/jabi
- Owner: FullstackCodingGuy
- Created: 2025-03-30T09:45:24.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-30T10:22:35.000Z (about 1 year ago)
- Last Synced: 2025-03-30T10:28:03.380Z (about 1 year ago)
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Just A Bot Idea
> Personal task management assistant.
Running gemma model locally,
First ensure your ollama instance is running
```
ollama pull gemma:2b
```
To build an **AI-powered personal assistant** for mobile, follow these steps:
---
## **🔹 1. Define the Core Features**
✅ **Conversational AI** (text & voice-based interactions)
✅ **Multi-Modal Support** (text, voice, images, documents)
✅ **Task Automation** (reminders, scheduling, note-taking)
✅ **Integration with APIs** (weather, news, calendar, smart home)
✅ **Offline Mode** (on-device inference for privacy & speed)
---
## **🔹 2. Choose the Tech Stack**
**🔸 Frontend (Mobile App UI)**
- **Flutter** (Dart) → Cross-platform (iOS & Android)
- **React Native** (JS/TS) → Web & Mobile
- **Native Android (Kotlin) / iOS (Swift)** → Best performance
**🔸 Backend (AI Processing & APIs)**
- **Gemma 2B / Mistral 7B** → LLM for fast on-device inference
- **Whisper** → Speech-to-Text (voice interactions)
- **TTS (Text-to-Speech)** → For voice output
- **FastAPI / Flask** → For cloud-based AI APIs
**🔸 Database & Storage**
- **SQLite / Room (Mobile)** → Local storage
- **ChromaDB / Pinecone** → AI memory for conversations
- **Firebase / Supabase** → Cloud sync
---
## **🔹 3. Build the AI Assistant Core**
### **🛠️ Step 1: Set Up the AI Model (On-Device LLM)**
Use **Gemma 2B** for fast local processing.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load Gemma 2B model for on-device inference
model_name = "google/gemma-2b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
def chat_with_ai(prompt):
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_length=200)
return tokenizer.decode(output[0], skip_special_tokens=True)
print(chat_with_ai("How can I improve my productivity?"))
```
---
### **🛠️ Step 2: Add Speech-to-Text (STT)**
Use **Whisper** to convert voice to text.
```python
import whisper
model = whisper.load_model("small")
result = model.transcribe("audio.mp3")
print(result["text"])
```
---
### **🛠️ Step 3: Text-to-Speech (TTS)**
Use **Google TTS / Coqui TTS** for voice responses.
```python
from gtts import gTTS
import os
text = "Hello! How can I assist you today?"
tts = gTTS(text)
tts.save("response.mp3")
os.system("mpg321 response.mp3")
```
---
### **🛠️ Step 4: Connect to Mobile App (Flutter / React Native)**
Use **HTTP APIs / WebSockets** to integrate the AI backend with your mobile app.
Example API (FastAPI):
```python
from fastapi import FastAPI
app = FastAPI()
@app.get("/chat")
async def chat(prompt: str):
response = chat_with_ai(prompt)
return {"response": response}
```
---
### **Integrating Gemma for Intent Recognition in a Personal Assistant**
To use **Gemma** for **intent recognition** in your personal assistant, follow these steps:
---
## **🔹 1. Approach to Intent Recognition**
### ✅ **Steps**:
1️⃣ **User Input** → The assistant receives a **text command**
2️⃣ **Gemma Processes the Input** → The LLM **analyzes intent**
3️⃣ **Intent Classification** → Matches the query to a **predefined action**
4️⃣ **Execute Action** → Calls the function or API
5️⃣ **Generate Response** → LLM confirms the action
---
## **🔹 2. Running Gemma Locally for Intent Recognition**
Since **Gemma is an open-source model**, you can run it locally using **Ollama** or **Hugging Face Transformers**.
### **🛠️ Step 1: Install Ollama (For Local Inference)**
```sh
# Install Ollama (Mac/Linux)
curl -fsSL https://ollama.com/install.sh | sh
# Install Gemma model
ollama pull gemma:2b
```
### **🛠️ Step 2: Process User Queries with Intent Detection**
```typescript
import fetch from "node-fetch";
// Function to call Gemma LLM
async function callGemma(prompt: string): Promise {
const response = await fetch("http://localhost:11434/api/generate", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ model: "gemma:2b", prompt })
});
const data = await response.json();
return data.response.trim();
}
// Define Intent Handlers
const intentHandlers: { [key: string]: Function } = {
"set_alarm": (time: string) => `Alarm set for ${time}.`,
"send_email": (recipient: string, subject: string) =>
`Email sent to ${recipient} with subject: ${subject}.`,
};
// Function to recognize intent
async function processIntent(userQuery: string): Promise {
const prompt = `
Classify the intent of the following message into one of these intents:
- set_alarm
- send_email
- unknown
Message: "${userQuery}"
Response (JSON):
`;
const response = await callGemma(prompt);
console.log("LLM Response:", response);
try {
const result = JSON.parse(response);
if (intentHandlers[result.intent]) {
return intentHandlers[result.intent](...(result.parameters || []));
}
} catch (e) {
console.error("Error parsing response:", e);
}
return "Sorry, I couldn't understand the request.";
}
// Example Usage
processIntent("Set an alarm for 6 AM").then(console.log);
processIntent("Send an email to john@example.com with subject Meeting").then(console.log);
```
✅ **Runs fully locally using Gemma 2B**
✅ **Extracts intent & executes predefined functions**
---
## **🔹 3. Fine-Tuning Gemma for Better Intent Recognition**
For **better accuracy**, train Gemma on **custom intent datasets**.
### **🛠️ Steps to Fine-Tune with Hugging Face**
1️⃣ **Prepare labeled dataset (text → intent mappings)**
2️⃣ **Use Hugging Face `transformers` to fine-tune Gemma**
3️⃣ **Deploy the fine-tuned model locally**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
# Load Gemma model & tokenizer
model_name = "google/gemma-2b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example dataset (fine-tuning intent classification)
dataset = [
{"input": "Set an alarm for 7 AM", "intent": "set_alarm"},
{"input": "Remind me to call Alex", "intent": "set_reminder"},
{"input": "Send an email to John with subject Urgent", "intent": "send_email"},
]
# Convert dataset into tokenized format
def tokenize_function(example):
return tokenizer(example["input"], padding="max_length", truncation=True)
# Train model (if needed)
training_args = TrainingArguments(output_dir="./results", per_device_train_batch_size=4, num_train_epochs=3)
trainer = Trainer(model=model, args=training_args, train_dataset=dataset)
trainer.train()
```
✅ **Fine-tunes Gemma for intent classification**
✅ **Improves accuracy for specific use cases**
---
## **🔹 4. Deploying in a Mobile App**
To integrate with a **mobile assistant (Flutter/React Native)**:
- **Backend**: Run Gemma on **FastAPI/Node.js**
- **Frontend**: Send API requests for **intent recognition & action execution**
- **Voice Integration**: Use **Google TTS / Whisper for voice input/output**
-------------
- https://www.linkedin.com/pulse/running-ai-models-locally-ollama-jerome-lecomte-kuwuf/
- https://ollama.com/blog/openai-compatibility
- https://ollama.com/library/codellama
- https://www.datacamp.com/tutorial/deepseek-r1-ollama
- https://medium.com/@tubelwj/complete-guide-to-running-ollamas-large-language-model-llm-locally-part-1-97f936da4eb0
- https://github.com/ollama/ollama-js