inference-cli

import os from inference_sdk import InferenceHTTPClient # Ensure you have your Roboflow API key set as an environment variable or replace os.environ.get with your key. # You can find your API key on the Roboflow dashboard. ROBOFLOW_API_KEY = os.environ.get('ROBOFLOW_API_KEY', '') if not ROBOFLOW_API_KEY: print("Warning: ROBOFLOW_API_KEY environment variable not set. Inference may fail.") # For a quick demo without a real key, you might use a dummy value # or skip this part if you are only running a local server without Roboflow API interaction. # For proper usage, always use a real key. # 1. Start a local inference server (requires Docker to be running): # Run in your terminal: inference server start # This will typically start on http://localhost:9001 # 2. Initialize the InferenceHTTPClient client = InferenceHTTPClient( api_url="http://localhost:9001", # Or "https://serverless.roboflow.com" for hosted API api_key=ROBOFLOW_API_KEY, ) # Example image URL for inference image_url = "https://media.roboflow.com/inference/soccer.jpg" # Replace with your actual model_id (e.g., 'your-project-name/your-model-version') # You can find this on your Roboflow model's deploy tab. model_id = "soccer-players-5fuqs/1" # 3. Perform inference try: print(f"Running inference on {image_url} with model {model_id}...") results = client.infer(image_url, model_id=model_id) print("Inference successful!") # Print first few predictions for brevity if results and 'predictions' in results and len(results['predictions']) > 0: print("First 3 predictions:") for i, pred in enumerate(results['predictions'][:3]): print(f" - Class: {pred.get('class')}, Confidence: {pred.get('confidence'):.2f}") else: print("No predictions found or unexpected result format.") except Exception as e: print(f"An error occurred during inference: {e}") print("Ensure the local inference server is running ('inference server start') and the model ID/API key are correct.")