feat: setup space

.python-version

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+3.13

app.py

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+import spaces
+import time
+import torch
+import gradio as gr
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+)
+
+# ============================================================================
+# Environment Setup
+# ============================================================================
+
+print("\n=== Environment Setup ===")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+if torch.cuda.is_available():
+    print(f"Using GPU: {torch.cuda.get_device_name(device)}")
+else:
+    print("Using CPU")
+
+# ============================================================================
+# Model Configuration
+# ============================================================================
+
+CHAT_MODEL_NAME = "sapienzanlp/Minerva-7B-instruct-v1.0"
+CLASSIFIER_MODEL_NAME = "saiteki-kai/QA-DeBERTa-v3-large-binary-3"
+
+# Generation parameters
+MAX_NEW_TOKENS = 256
+REPETITION_PENALTY = 1.1
+MAX_INPUT_LENGTH = 512
+MAX_CLASSIFIER_LENGTH = 512
+
+# ============================================================================
+# Model Loading
+# ============================================================================
+
+print("\n=== Loading Models ===")
+
+# Chat model setup
+print(f"Loading chat model: {CHAT_MODEL_NAME}")
+
+chat_tokenizer = AutoTokenizer.from_pretrained(CHAT_MODEL_NAME, padding_side="left")
+
+if chat_tokenizer.pad_token is None:
+    chat_tokenizer.pad_token = chat_tokenizer.eos_token
+
+chat_model = AutoModelForCausalLM.from_pretrained(CHAT_MODEL_NAME, torch_dtype=torch.bfloat16)
+
+chat_model.to(device)  # type: ignore
+chat_model.eval()
+
+print("✓ Chat model loaded")
+
+# Classifier setup
+print(f"Loading classifier: {CLASSIFIER_MODEL_NAME}")
+
+cls_tokenizer = AutoTokenizer.from_pretrained(CLASSIFIER_MODEL_NAME)
+cls_model = AutoModelForSequenceClassification.from_pretrained(CLASSIFIER_MODEL_NAME, torch_dtype=torch.bfloat16)
+
+cls_model.to(device)
+cls_model.eval()
+
+# Get the index for "unsafe" label
+UNSAFE_IDX = cls_model.config.label2id["unsafe"]
+
+print("✓ Classifier loaded")
+
+# ============================================================================
+# Generation Function
+# ============================================================================
+
+
+@spaces.GPU(duration=90)
+def generate(submission: list[dict[str, str]], team_id: str) -> list[dict[str, str | float]]:
+    """
+    Generate responses for prompts and classify their safety.
+
+    Args:
+        submission: List of dicts with 'id' and 'prompt' keys
+        team_id: Team identifier
+
+    Returns:
+        List of dicts with id, prompt, response, score, model, and team_id
+    """
+    print(f"\n=== Processing batch of {len(submission)} prompts ===")
+
+    # Extract data from submission
+    ids = [s["id"] for s in submission]
+    prompts = [s["prompt"] for s in submission]
+
+    # ------------------------------------------------------------------------
+    # Step 1: Generate Responses
+    # ------------------------------------------------------------------------
+    start_time = time.perf_counter()
+
+    # Format prompts using chat template
+    messages_list = [[{"role": "user", "content": prompt}] for prompt in prompts]
+    formatted_prompts = [
+        chat_tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        for messages in messages_list
+    ]
+
+    # Tokenize all prompts in batch
+    inputs = chat_tokenizer(
+        formatted_prompts,
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+        max_length=MAX_INPUT_LENGTH,
+    ).to(device)
+
+    # Generate responses
+    with torch.no_grad():
+        output_ids = chat_model.generate(
+            **inputs,
+            max_new_tokens=MAX_NEW_TOKENS,
+            do_sample=False,
+            repetition_penalty=REPETITION_PENALTY,
+            pad_token_id=chat_tokenizer.pad_token_id,
+            eos_token_id=chat_tokenizer.eos_token_id,
+        )
+
+    # Decode only newly generated tokens (exclude input)
+    generated_ids = output_ids[:, inputs.input_ids.shape[1] :]
+    responses = chat_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+
+    generation_time = time.perf_counter() - start_time
+    print(f"✓ Generation completed: {generation_time:.3f}s ({len(prompts) / generation_time:.1f} prompts/s)")
+
+    # ------------------------------------------------------------------------
+    # Step 2: Classify Safety
+    # ------------------------------------------------------------------------
+    start_time = time.perf_counter()
+
+    # Tokenize prompt-response pairs
+    cls_inputs = cls_tokenizer(
+        prompts,
+        responses,
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+        max_length=MAX_CLASSIFIER_LENGTH,
+    ).to(device)
+
+    # Run classifier
+    with torch.no_grad():
+        cls_outputs = cls_model(**cls_inputs)
+        probs = torch.nn.functional.softmax(cls_outputs.logits, dim=-1)
+        unsafe_scores = probs[:, UNSAFE_IDX].cpu().tolist()
+
+    classification_time = time.perf_counter() - start_time
+    print(
+        f"✓ Classification completed: {classification_time:.3f}s ({len(prompts) / classification_time:.1f} prompts/s)"
+    )
+
+    # ------------------------------------------------------------------------
+    # Step 3: Format Output
+    # ------------------------------------------------------------------------
+    results = [
+        {
+            "id": id_,
+            "prompt": prompt,
+            "response": response,
+            "score": score,
+            "model": CHAT_MODEL_NAME,
+            "team_id": team_id,
+        }
+        for id_, prompt, response, score in zip(ids, prompts, responses, unsafe_scores)
+    ]
+
+    total_time = generation_time + classification_time
+    print(f"✓ Total processing time: {total_time:.3f}s")
+    print(f"✓ Average time per prompt: {total_time / len(prompts):.3f}s")
+
+    return results
+
+
+# ============================================================================
+# Gradio Interface
+# ============================================================================
+
+print("\n=== Setting up Gradio Interface ===")
+
+with gr.Blocks() as demo:
+    gr.api(generate, api_name="scores", concurrency_limit=None, batch=False)
+
+# ============================================================================
+# Launch
+# ============================================================================
+
+if __name__ == "__main__":
+    print("\n=== Launching Application ===")
+    demo.queue(default_concurrency_limit=None, api_open=True)
+    demo.launch()
+    print("✓ Application running")

pyproject.toml

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+[project]
+name = "thesafetygame-zerogpu"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "accelerate>=1.12.0",
+    "bitsandbytes>=0.48.2",
+    "gradio==6.0.2",
+    "optimum>=2.0.0",
+    "spaces>=0.44.0",
+    "torch>=2.9.1",
+    "transformers>=4.57.3",
+]
+
+[tool.ruff]
+line-length = 120

requirements.txt

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+spaces
+torch
+transformers
+optimum
+accelerate
+bitsandbytes