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from flask import Flask, request, jsonify
import numpy as np
import tensorflow as tf
import torch
from transformers import pipeline
import cv2
import os
app = Flask(__name__)
# Example model loading (you can replace this with your actual models)
# TensorFlow model
tf_model = tf.keras.models.load_model('path_to_your_tf_model')
# PyTorch model
torch_model = torch.load('path_to_your_torch_model')
torch_model.eval()
# Hugging Face Transformers pipeline (e.g., for text generation)
text_gen_pipeline = pipeline("text-generation", model="gpt2")
@app.route('/')
def index():
return "Welcome to the AI app! Endpoints are ready."
# Endpoint to make predictions using TensorFlow model
@app.route('/predict_tf', methods=['POST'])
def predict_tf():
data = request.json
input_data = np.array(data['input'])
prediction = tf_model.predict(input_data)
return jsonify({"prediction": prediction.tolist()})
# Endpoint to make predictions using PyTorch model
@app.route('/predict_torch', methods=['POST'])
def predict_torch():
data = request.json
input_data = torch.tensor(data['input'])
prediction = torch_model(input_data)
return jsonify({"prediction": prediction.detach().numpy().tolist()})
# Text generation using Hugging Face Transformers
@app.route('/generate_text', methods=['POST'])
def generate_text():
data = request.json
prompt = data['prompt']
result = text_gen_pipeline(prompt, max_length=100, num_return_sequences=1)
return jsonify({"generated_text": result[0]['generated_text']})
# Endpoint to process an image (using OpenCV)
@app.route('/process_image', methods=['POST'])
def process_image():
if 'file' not in request.files:
return "No file found", 400
file = request.files['file']
img = cv2.imdecode(np.frombuffer(file.read(), np.uint8), cv2.IMREAD_COLOR)
# Example processing: convert image to grayscale
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Save the processed image
processed_path = 'processed_image.jpg'
cv2.imwrite(processed_path, gray_img)
return jsonify({"message": "Image processed", "file_path": processed_path})
# Future abilities and additional features can be added here
if __name__ == '__main__':
# Run the app
app.run(host='0.0.0.0', port=5000, debug=True)
import os
os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
import os
import tensorflow as tf
# Set environment variable
os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
# Check GPU availability
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
# Your main code goes here
def main():
# Your application code
pass
if __name__ == "__main__":
main() |