from mixtrain import Model

model = Model("hunyuan-video")
result = model.run({"prompt": "A cat playing piano"})
print(result.video.url)

model = Model("flux-pro")

# Synchronous - blocks until complete
result = model.run({"prompt": "A sunset over mountains"})
print(result.image.url)

# Asynchronous - returns immediately
run_info = model.submit({"prompt": "A dog running"})
print(f"Started run #{run_info['run_number']}")

from mixtrain import Model

# Single model batch
results = Model.batch(
    models=["flux-pro"],
    inputs=[{"prompt": "A sunset"}, {"prompt": "A mountain"}]
)

for r in results.to_dict()["flux-pro"]:
    print(r.image.url)

# Multiple models on same inputs
results = Model.batch(
    models=["flux-pro", "stable-diffusion-xl"],
    inputs=[{"prompt": "a cat"}, {"prompt": "a dog"}],
    max_in_flight=50
)

# Access as dict
for model_name, model_results in results.to_dict().items():
    print(f"{model_name}:")
    for r in model_results:
        print(f"  {r.image.url}")

# Or convert to DataFrame
df = results.to_pandas()

result = model.run({"prompt": "Generate something"})

result.video   # Video output - .url, .width, .height, .duration_seconds
result.image   # Image output - .url, .width, .height
result.audio   # Audio output - .url, .duration_seconds
result.text    # Text output
result.status  # "completed", "failed", etc.

from mixtrain import MixModel

class ImageClassifier(MixModel):
    def setup(self):
        self.model = self._load_model()

    def run(self, image_url: str, threshold: float = 0.5):
        """Classify an image.

        Args:
            image_url: Image to classify
            threshold: Confidence threshold
        """
        result = self.model.classify(image_url)
        return {"label": result, "confidence": 0.95}

model = ImageClassifier()

# Keyword arguments
result = model.run(image_url="http://...", threshold=0.7)

# Dict input
result = model.run({"image_url": "http://...", "threshold": 0.7})