GWR: My Cook-a-thon experience was a 'breakthrough' for me - Chef Faila

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• Offshore Waters 1 year ago

  import asyncio
  from flask import Flask, request
  from flask_socketio import SocketIO, emit
  import tensorflow as tf
  from transformers import pipeline
  
  app = Flask(__name__)
  socketio = SocketIO(app)
  
  # Load pre-trained ...
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  import asyncio
  from flask import Flask, request
  from flask_socketio import SocketIO, emit
  import tensorflow as tf
  from transformers import pipeline
  
  app = Flask(__name__)
  socketio = SocketIO(app)
  
  # Load pre-trained NLP model for query interpretation
  nlp_model = pipeline("text-classification")
  
  # Dummy model for predictions (replace with a real trained model)
  model = tf.keras.models.Sequential([
  tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)), # Adjust input_shape as necessary
  tf.keras.layers.Dense(10, activation='softmax')
  ])
  
  # API endpoint for predictions
  @app.route('/predict', methods=['POST'])
  def predict():
  data = request.json['data']
  predictions = model.predict(tf.convert_to_tensor(data))
  return {'predictions': predictions.numpy().tolist()}
  
  # API endpoint for query interpretation
  @app.route('/query', methods=['POST'])
  def handle_query():
  query = request.json['query']
  response = interpret_query(query)
  return {'response': response}
  
  def interpret_query(query):
  # Interpret the query using the NLP model
  intent = nlp_model(query)[0]['label']
  if intent == "ExecuteTask":
  # Logic to execute specific tasks based on the query
  return "Task executed successfully" # Placeholder for actual task execution logic
  return "Unknown command"
  
  # SocketIO event handler for real-time predictions
  @socketio.on('message')
  def handle_message(data):
  response = model.predict(tf.convert_to_tensor(data))
  emit('response', response.numpy().tolist())
  
  if __name__ == '__main__':
  socketio.run(app, debug=True)

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