Update app.py

app.py

@@ -1,93 +1,19 @@
+from transformers import AutoModel, AutoTokenizer
 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')
+# Load model and tokenizer from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained("Erfan11/Neuracraft", use_auth_token="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")
+model = AutoModel.from_pretrained("Erfan11/Neuracraft", use_auth_token="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")
 
-# 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
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.get_json()
+    inputs = tokenizer(data["text"], return_tensors="pt")
+    outputs = model(**inputs)
+    # Process your model's output as needed
+    return jsonify(outputs)
 
 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()
-    from transformers import AutoTokenizer, TFAutoModelForCausalLM
-
-# Load the tokenizer and model from Hugging Face using your model name
-tokenizer = AutoTokenizer.from_pretrained("Erfan11/Neuracraft")
-model = TFAutoModelForCausalLM.from_pretrained("Erfan11/Neuracraft")
+    app.run(debug=True)