googlecodelabs/tools

VIAJANTE DO TEMPO. TECNOLOGIA QUE USAMOS EM 2030.. SKY NET FASE 1

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felipeliliti commented

PROJETO MK ULTA AURORA STK 3.6.9

Estrutura do Projeto

aurora_project/
│
├── app/
│   ├── __init__.py
│   ├── routes.py
│   ├── gan.py
│   ├── sentiment_analysis.py
│   ├── speech_recognition.py
│   ├── translation.py
│   └── assistant.py
│
├── templates/
│   └── index.html
│
├── static/
│   └── css/
│       └── styles.css
│
├── run.py
└── requirements.txt

1. app/__init__.py

from flask import Flask

def create_app():
    app = Flask(__name__)

    from .routes import main as main_blueprint
    app.register_blueprint(main_blueprint)

    return app

2. app/routes.py

from flask import Blueprint, request, jsonify
from .gan import generate_image
from .sentiment_analysis import analyze_sentiment
from .speech_recognition import transcribe_audio
from .translation import translate_text
from .assistant import generate_response

main = Blueprint('main', __name__)

@main.route('/')
def index():
    return "Welcome to AURORA AI"

@main.route('/generate_image', methods=['POST'])
def generate_image_route():
    # Implement image generation logic here
    return jsonify({"message": "Image generation route"})

@main.route('/analyze_sentiment', methods=['POST'])
def analyze_sentiment_route():
    text = request.json['text']
    result = analyze_sentiment(text)
    return jsonify(result)

@main.route('/transcribe_audio', methods=['POST'])
def transcribe_audio_route():
    # Implement audio transcription logic here
    return jsonify({"message": "Audio transcription route"})

@main.route('/translate', methods=['POST'])
def translate_route():
    text = request.json['text']
    target_language = request.json['target_language']
    result = translate_text(text, target_language)
    return jsonify({"translated_text": result})

@main.route('/assistant', methods=['POST'])
def assistant_route():
    prompt = request.json['prompt']
    response = generate_response(prompt)
    return jsonify({"response": response})

3. app/gan.py

# Import the necessary libraries for GANs
import torch
from torchvision.utils import save_image
from stylegan2_pytorch import Trainer

# Function to generate image
def generate_image():
    # Define and train the GAN here
    return "GAN Image"

4. app/sentiment_analysis.py

from transformers import pipeline

sentiment_pipeline = pipeline('sentiment-analysis')

def analyze_sentiment(text):
    result = sentiment_pipeline(text)
    return result

5. app/speech_recognition.py

from google.cloud import speech_v1p1beta1 as speech

client = speech.SpeechClient()

def transcribe_audio(file_path):
    with open(file_path, "rb") as audio_file:
        content = audio_file.read()

    audio = speech.RecognitionAudio(content=content)
    config = speech.RecognitionConfig(
        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
        sample_rate_hertz=16000,
        language_code="en-US",
    )

    response = client.recognize(config=config, audio=audio)

    for result in response.results:
        return result.alternatives[0].transcript

6. app/translation.py

from google.cloud import translate_v2 as translate

translate_client = translate.Client()

def translate_text(text, target_language):
    result = translate_client.translate(text, target_language=target_language)
    return result["translatedText"]

7. app/assistant.py

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model_name = "gpt-2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

def generate_response(prompt):
    inputs = tokenizer.encode(prompt, return_tensors="pt")
    outputs = model.generate(inputs, max_length=100, do_sample=True, temperature=0.7)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

8. templates/index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>AURORA AI</title>
    <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}">
</head>
<body>
    <h1>Welcome to AURORA AI</h1>
</body>
</html>

9. static/css/styles.css

body {
    font-family: Arial, sans-serif;
    text-align: center;
    margin-top: 50px;
}

10. run.py

from app import create_app

app = create_app()

if __name__ == '__main__':
    app.run(debug=True)

11. requirements.txt

Flask
torch
transformers
google-cloud-speech
google-cloud-translate
stylegan2_pytorch

Subindo o Projeto

  1. Clone o repositório e instale as dependências:

    git clone https://github.com/seu-usuario/aurora_project.git
cd aurora_project
pip install -r requirements.txt

  2. Configuração do Google Cloud:

    • Crie um projeto no Google Cloud.
    • Habilite as APIs de Speech-to-Text e Translation.
    • Baixe as credenciais e defina a variável de ambiente GOOGLE_APPLICATION_CREDENTIALS:
    export GOOGLE_APPLICATION_CREDENTIALS="path/to/your/credentials.json"

  3. Execute a aplicação:

    python run.py
`