import requests

API_URL = 'https://api-inference.huggingface.co/models/vishwajeet456/android-automation-ai'
HEADERS = {'Authorization': 'Bearer YOUR_HF_TOKEN'}

def get_android_action(command):
    prompt = f'''Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
You are an Android automation assistant. Convert the user's natural language command into the correct Android action.

### Input:
{command}

### Response:
'''
    response = requests.post(API_URL, headers=HEADERS, json={'inputs': prompt, 'parameters': {'max_new_tokens': 150, 'temperature': 0.1}})
    result = response.json()
    if isinstance(result, list):
        text = result[0]['generated_text']
        return text.split('### Response:')[-1].strip()
    return str(result)

# Example
print(get_android_action('Turn on flashlight'))
# Output: ACTION: toggle_flashlight(state='on')

Training Details

Base model: Llama 3.2 3B Instruct
Method: LoRA fine-tuning with Unsloth
Dataset: 40 real-life Android automation examples
Training platform: Google Colab (T4 GPU)
Training time: ~20 minutes
Format: Alpaca instruction template

Limitations

Trained on 40 examples — works well for covered patterns, may struggle with completely new command types
Best suited for Indian English phrasing and Indian apps
Action sequences are structured strings — actual execution requires Android Accessibility Service integration