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| import requests | |
| import base64 | |
| from io import BytesIO | |
| from PIL import Image | |
| import json | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| # This is an example client that demonstrates how to use the MCP server | |
| # You would replace this URL with the actual URL of your deployed Huggingface Space | |
| MCP_SERVER_URL = "https://your-username-nomic-vision-mcp.hf.space/mcp" | |
| def embed_image_from_url(image_url): | |
| """ | |
| Generate embeddings for an image using the MCP server's embed_image tool | |
| Args: | |
| image_url: URL of the image to embed | |
| Returns: | |
| The embedding vector and its dimension | |
| """ | |
| # Prepare the MCP request | |
| mcp_request = { | |
| "jsonrpc": "2.0", | |
| "method": "callTool", | |
| "params": { | |
| "name": "embed_image", | |
| "arguments": { | |
| "image_url": image_url | |
| } | |
| }, | |
| "id": 1 | |
| } | |
| # Send the request to the MCP server | |
| response = requests.post(MCP_SERVER_URL, json=mcp_request) | |
| # Parse the response | |
| result = response.json() | |
| if "error" in result: | |
| print(f"Error: {result['error']['message']}") | |
| return None | |
| # Extract the embedding from the response | |
| content = result["result"]["content"][0]["text"] | |
| embedding_data = json.loads(content) | |
| return embedding_data | |
| def embed_image_from_file(image_path): | |
| """ | |
| Generate embeddings for an image using the MCP server's embed_image tool | |
| Args: | |
| image_path: Path to the image file | |
| Returns: | |
| The embedding vector and its dimension | |
| """ | |
| # Load the image | |
| with open(image_path, "rb") as f: | |
| image_data = f.read() | |
| # Encode the image as base64 | |
| image_base64 = base64.b64encode(image_data).decode("utf-8") | |
| # Prepare the MCP request | |
| mcp_request = { | |
| "jsonrpc": "2.0", | |
| "method": "callTool", | |
| "params": { | |
| "name": "embed_image", | |
| "arguments": { | |
| "image_data": image_base64 | |
| } | |
| }, | |
| "id": 1 | |
| } | |
| # Send the request to the MCP server | |
| response = requests.post(MCP_SERVER_URL, json=mcp_request) | |
| # Parse the response | |
| result = response.json() | |
| if "error" in result: | |
| print(f"Error: {result['error']['message']}") | |
| return None | |
| # Extract the embedding from the response | |
| content = result["result"]["content"][0]["text"] | |
| embedding_data = json.loads(content) | |
| return embedding_data | |
| def visualize_embedding(embedding): | |
| """ | |
| Visualize the embedding vector | |
| Args: | |
| embedding: The embedding vector | |
| """ | |
| # Convert the embedding to a numpy array | |
| embedding_array = np.array(embedding) | |
| # Plot the embedding | |
| plt.figure(figsize=(10, 5)) | |
| plt.plot(embedding_array) | |
| plt.title("Embedding Vector") | |
| plt.xlabel("Dimension") | |
| plt.ylabel("Value") | |
| plt.grid(True) | |
| plt.show() | |
| # Plot the histogram of the embedding | |
| plt.figure(figsize=(10, 5)) | |
| plt.hist(embedding_array, bins=50) | |
| plt.title("Embedding Histogram") | |
| plt.xlabel("Value") | |
| plt.ylabel("Frequency") | |
| plt.grid(True) | |
| plt.show() | |
| if __name__ == "__main__": | |
| # Example usage with an image URL | |
| image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/bert-architects.png" | |
| print(f"Generating embedding for image: {image_url}") | |
| embedding_data = embed_image_from_url(image_url) | |
| if embedding_data: | |
| print(f"Embedding dimension: {embedding_data['dimension']}") | |
| print(f"First 10 values of embedding: {embedding_data['embedding'][:10]}...") | |
| # Visualize the embedding | |
| visualize_embedding(embedding_data['embedding']) | |
| # Example usage with a local image file | |
| # Uncomment the following lines to use a local image file | |
| # image_path = "path/to/your/image.jpg" | |
| # print(f"Generating embedding for image: {image_path}") | |
| # embedding_data = embed_image_from_file(image_path) | |
| # | |
| # if embedding_data: | |
| # print(f"Embedding dimension: {embedding_data['dimension']}") | |
| # print(f"First 10 values of embedding: {embedding_data['embedding'][:10]}...") | |
| # | |
| # # Visualize the embedding | |
| # visualize_embedding(embedding_data['embedding']) |