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

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• Dr Mumbi Seraki 1 year ago

  Hello youth of Ghana. You can take your own life with medicine. For instance 10 paracetamol at once..........or combine medicine and akpeteshie or weedicide.

  Hello youth of Ghana. You can take your own life with medicine. For instance 10 paracetamol at once..........or combine medicine and akpeteshie or weedicide.

  reply
• 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)

  reply