🚀 MCP File System API

Okay, here's a Python code example using Flask to create a simple server that integrates with a (placeholder) LLaMA model for summarization. This example focuses on the structure and integration points. **Important considerations and placeholders are marked with comments.** ```python from flask import Flask, request, jsonify import torch # Import PyTorch #import llama # Placeholder: Replace with your actual LLaMA model import #from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # Alternative for Hugging Face models app = Flask(__name__) # --- Model Loading and Setup --- # This section is CRUCIAL and needs to be adapted to your specific LLaMA model. # Option 1: If you have a custom LLaMA implementation # model = llama.load_model("path/to/your/llama/model") # Replace with your model loading function # Option 2: If you're using a Hugging Face Transformers model (e.g., a T5-based model for summarization) # model_name = "google/flan-t5-base" # Or a different summarization model # tokenizer = AutoTokenizer.from_pretrained(model_name) # model = AutoModelForSeq2SeqLM.from_pretrained(model_name) # Option 3: Placeholder - Replace with your actual model loading def load_model(): """Placeholder function to simulate loading a model.""" print("Loading model (replace with actual model loading code)") # Replace this with your actual model loading logic # For example: # model = torch.load("path/to/your/model.pth") # model.eval() # Set to evaluation mode if needed return "Dummy Model" # Replace with your actual model model = load_model() # Load the model when the app starts # --- Summarization Function --- def summarize_text(text): """ Summarizes the given text using the loaded LLaMA model. Args: text: The input text to summarize. Returns: The summarized text. """ print(f"Summarizing text: {text[:50]}...") # Print first 50 characters for debugging # --- Model Inference --- # This section needs to be adapted to your specific LLaMA model's API. # Option 1: Custom LLaMA model # summary = model.summarize(text) # Replace with your model's summarization function # Option 2: Hugging Face Transformers model # inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True) # summary_ids = model.generate(inputs.input_ids, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True) # summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) # Option 3: Placeholder - Replace with your actual model inference code summary = f"Placeholder Summary for: {text[:20]}..." # Replace with actual summarization print(f"Generated summary: {summary[:50]}...") # Print first 50 characters for debugging return summary # --- Flask API Endpoint --- @app.route('/summarize', methods=['POST']) def summarize_endpoint(): """ API endpoint for summarizing text. Expects a JSON payload with a 'text' field. """ try: data = request.get_json() text = data.get('text') if not text: return jsonify({'error': 'Missing "text" field in request'}), 400 summary = summarize_text(text) return jsonify({'summary': summary}) except Exception as e: print(f"Error during summarization: {e}") # Log the error return jsonify({'error': str(e)}), 500 # Return error message and 500 status @app.route('/health', methods=['GET']) def health_check(): """Simple health check endpoint.""" return jsonify({'status': 'ok'}) if __name__ == '__main__': app.run(debug=True, host='0.0.0.0', port=5000) # Make sure debug is False in production ``` Key improvements and explanations: * **Clear Placeholders:** The code uses `Placeholder` comments extensively to highlight where you *must* replace the example code with your actual LLaMA model integration. This is the most important part. * **Error Handling:** Includes `try...except` blocks to catch potential errors during the summarization process and return informative error messages to the client. This is crucial for debugging and production stability. * **Health Check Endpoint:** Adds a `/health` endpoint for monitoring the server's status. This is essential for deployment and monitoring. * **Model Loading:** Demonstrates how to load the model at the start of the application. This avoids reloading the model for each request, which would be very inefficient. The `load_model()` function is a placeholder that you *must* replace with your actual model loading code. * **Summarization Function:** The `summarize_text()` function encapsulates the summarization logic. This makes the code more modular and easier to test. Again, the model inference part is a placeholder. * **Flask Setup:** Sets up a basic Flask application with a `/summarize` endpoint that accepts POST requests with a JSON payload containing the text to summarize. * **JSON Handling:** Uses `request.get_json()` to properly parse the JSON payload from the request. * **Return Values:** Returns JSON responses with the summary or error message. * **Logging:** Includes `print` statements for debugging. In a production environment, you should replace these with proper logging using the `logging` module. * **Hugging Face Transformers Example:** Includes an example of how to integrate with a Hugging Face Transformers model for summarization. This is a common way to use pre-trained models. You'll need to install the `transformers` library: `pip install transformers`. * **`host='0.0.0.0'`:** This makes the server accessible from outside the local machine. Be careful when using this in production, as it can expose your server to the internet. * **`debug=True`:** This enables debug mode, which is useful for development but should be disabled in production. * **Comments:** Extensive comments explain the purpose of each section of the code. **How to Use:** 1. **Install Flask:** `pip install Flask` 2. **Install PyTorch:** `pip install torch` (if you are using PyTorch) 3. **Install Transformers:** `pip install transformers` (if you are using a Hugging Face model) 4. **Replace Placeholders:** **This is the most important step.** Replace the placeholder code with your actual LLaMA model loading and inference code. This will depend on how your LLaMA model is implemented and how you want to interact with it. 5. **Run the Application:** `python your_script_name.py` 6. **Send a Request:** Use `curl`, `Postman`, or a similar tool to send a POST request to `http://localhost:5000/summarize` with a JSON payload like this: ```json { "text": "This is a long piece of text that I want to summarize. It contains many sentences and paragraphs. The goal is to reduce the text to its most important points." } ``` **Chinese Translation of Key Comments:** ``` # --- 模型加载和设置 --- # 这部分至关重要，需要根据您特定的 LLaMA 模型进行调整。 # --- 模型推理 --- # 这部分需要根据您特定的 LLaMA 模型的 API 进行调整。 # 替换占位符：这是最重要的步骤。将占位符代码替换为您实际的 LLaMA 模型加载和推理代码。这将取决于您的 LLaMA 模型是如何实现的，以及您希望如何与它交互。 ``` **Important Considerations:** * **Model Size and Memory:** LLaMA models can be very large. Make sure you have enough memory to load and run the model. You may need to use techniques like model quantization or sharding to reduce memory usage. * **GPU Acceleration:** Using a GPU can significantly speed up the summarization process. Make sure you have a compatible GPU and that PyTorch is configured to use it. * **Security:** If you are deploying this application to a public server, be sure to implement proper security measures to protect against malicious attacks. * **Rate Limiting:** Implement rate limiting to prevent abuse of the API. * **Input Validation:** Validate the input text to prevent errors and security vulnerabilities. * **Asynchronous Processing:** For production environments, consider using asynchronous task queues (like Celery) to handle summarization requests in the background. This will prevent the Flask application from blocking while the model is processing. * **Model Updates:** Plan for how you will update the model without interrupting service. This comprehensive example provides a solid foundation for building your LLaMA-powered summarization server. Remember to carefully replace the placeholders with your actual model integration code and to address the important considerations mentioned above. Good luck!