Create client.py

client.py

@@ -0,0 +1,166 @@
+import requests
+import time
+from typing import Dict, Any, Optional
+import os
+
+class MLTracker:
+    """Python client for ML Tracker - Free W&B Alternative"""
+
+    def __init__(self, api_key: str, base_url: str = "https://your-space-url.hf.space"):
+        self.api_key = api_key
+        self.base_url = base_url.rstrip('/')
+        self.current_experiment = None
+        self.step = 0
+
+    def init(self, experiment_name: str, config: Optional[Dict[str, Any]] = None):
+        self.current_experiment = experiment_name
+        self.step = 0
+        if config:
+            self.log_config(experiment_name, config)
+
+    def log_config(self, experiment_name: str, config: Dict[str, Any]):
+        try:
+            response = requests.post(
+                f"{self.base_url}/api/log",
+                json={
+                    "api_key": self.api_key,
+                    "experiment": experiment_name,
+                    "step": 0,
+                    "metrics": {},
+                    "config": config,
+                    "timestamp": time.time()
+                }
+            )
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            print(f"Warning: Failed to log config: {e}")
+            return None
+
+    def log(self, metrics: Dict[str, Any], step: Optional[int] = None):
+        if not self.current_experiment:
+            raise ValueError("No experiment initialized. Call init() first.")
+        if step is None:
+            self.step += 1
+            step = self.step
+        else:
+            self.step = max(self.step, step)
+        try:
+            response = requests.post(
+                f"{self.base_url}/api/log",
+                json={
+                    "api_key": self.api_key,
+                    "experiment": self.current_experiment,
+                    "step": step,
+                    "metrics": metrics,
+                    "timestamp": time.time()
+                }
+            )
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            print(f"Warning: Failed to log metrics: {e}")
+            return None
+
+    def delete_experiment(self, experiment_name: str):
+        try:
+            response = requests.delete(
+                f"{self.base_url}/api/experiment/{experiment_name}",
+                params={"api_key": self.api_key}
+            )
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            print(f"Error deleting experiment: {e}")
+            return None
+
+    def get_experiments(self):
+        try:
+            response = requests.get(
+                f"{self.base_url}/api/experiments",
+                params={"api_key": self.api_key}
+            )
+            response.raise_for_status()
+            return response.json().get("experiments", [])
+        except Exception as e:
+            print(f"Error fetching experiments: {e}")
+            return []
+
+    def get_experiment(self, experiment_name: str):
+        try:
+            response = requests.get(
+                f"{self.base_url}/api/experiment/{experiment_name}",
+                params={"api_key": self.api_key}
+            )
+            response.raise_for_status()
+            return response.json()
+        except Exception as e:
+            print(f"Error fetching experiment: {e}")
+            return None
+
+    def finish(self):
+        self.current_experiment = None
+        self.step = 0
+
+
+# Global tracker and convenience functions
+_global_tracker = None
+
+def init(experiment_name: str, config: Optional[Dict[str, Any]] = None, 
+         api_key: Optional[str] = None, base_url: Optional[str] = None):
+    global _global_tracker
+    if api_key is None:
+        api_key = os.environ.get("ML_TRACKER_API_KEY")
+        if not api_key:
+            raise ValueError("API key not provided and ML_TRACKER_API_KEY not set")
+    if base_url is None:
+        base_url = os.environ.get("ML_TRACKER_BASE_URL", "https://your-space-url.hf.space")
+    _global_tracker = MLTracker(api_key, base_url)
+    _global_tracker.init(experiment_name, config)
+
+def log(metrics: Dict[str, Any], step: Optional[int] = None):
+    if _global_tracker is None:
+        raise ValueError("No experiment initialized. Call init() first.")
+    return _global_tracker.log(metrics, step)
+
+def finish():
+    global _global_tracker
+    if _global_tracker:
+        _global_tracker.finish()
+        _global_tracker = None
+
+
+if __name__ == "__main__":
+    # Example usage
+    tracker = MLTracker(
+        api_key="your-api-key-here",
+        base_url="https://your-space-url.hf.space"
+    )
+    tracker.init("my_experiment", config={
+        "model": "ResNet50",
+        "dataset": "CIFAR-10",
+        "learning_rate": 0.001,
+        "batch_size": 32
+    })
+    for epoch in range(10):
+        loss = 2.0 * (0.9 ** epoch) + 0.1
+        accuracy = 1.0 - (0.9 ** epoch)
+        tracker.log({
+            "loss": loss,
+            "accuracy": accuracy,
+            "epoch": epoch
+        })
+    tracker.finish()
+
+    init("another_experiment", config={"model": "BERT"})
+    for step in range(5):
+        log({"loss": 1.0 / (step + 1), "step": step})
+    finish()
+
+    experiments = tracker.get_experiments()
+    print(f"Found {len(experiments)} experiments")
+
+    if experiments:
+        exp_data = tracker.get_experiment(experiments[0]["experiment"])
+        if exp_data:
+            print(f"Experiment has {len(exp_data.get('metrics', []))} metric entries")