Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+title: Safety GPT-OSS 20B
+emoji: 🔥
+colorFrom: green
+colorTo: purple
+sdk: gradio
+sdk_version: 5.49.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import time
+from typing import List, Dict, Tuple
+
+import gradio as gr
+from transformers import pipeline
+import spaces
+
+# === Config (override via Space secrets/env vars) ===
+MODEL_ID = os.environ.get("MODEL_ID", "tlhv/osb-minier")
+DEFAULT_MAX_NEW_TOKENS = int(os.environ.get("MAX_NEW_TOKENS", 512))
+DEFAULT_TEMPERATURE = float(os.environ.get("TEMPERATURE", 1))
+DEFAULT_TOP_P = float(os.environ.get("TOP_P", 1.0))
+DEFAULT_REPETITION_PENALTY = float(os.environ.get("REPETITION_PENALTY", 1.0))
+ZGPU_DURATION = int(os.environ.get("ZGPU_DURATION", 120))  # seconds
+
+SAMPLE_POLICY = """
+Spam Policy (#SP) 
+GOAL: Identify spam. Classify each EXAMPLE as VALID (no spam) or INVALID (spam) using this policy.
+ 
+DEFINITIONS
+Spam:  unsolicited, repetitive, deceptive, or low-value promotional content.
+
+
+Bulk Messaging: Same or similar messages sent repeatedly.
+
+
+Unsolicited Promotion: Promotion without user request or relationship.
+
+
+Deceptive Spam: Hidden or fraudulent intent (fake identity, fake offer).
+
+
+Link Farming: Multiple irrelevant or commercial links to drive clicks.
+
+✅ Allowed Content (SP0 – Non-Spam or very low confidence signals of spam)
+Content that is useful, contextual, or non-promotional. May look spammy but could be legitimate. 
+SP0.a Useful/info request – “How do I upload a product photo?”
+
+
+SP0.b Personalized communication – “Hi Sam, here is the report.”
+
+
+SP0.c Business support – “Can you fix my order?”
+
+
+SP0.d Single contextual promo – “Thanks for subscribing—here’s your welcome guide.”
+
+SP0.e Generic request – “Please respond ASAP.”
+
+
+SP0.f Low-quality formatting – “HeLLo CLICK here FAST.”
+
+
+SP0.g Vague benefit statement – “This tool changes lives.”
+
+ ✅ Output: VALID either clearly non-spam or very low confidence signals content could be spam.
+
+
+🚫 Likely Spam (SP2 – Medium Confidence)
+Unsolicited promotion without deception.
+SP2.a Cold promotion – “Check out my Shopify course: shopcoach.biz”
+
+
+SP2.b Irrelevant ads – “Buy gold rings here!”
+
+
+SP2.c Excessive linking – “http://x.com http://y.com http://z.com”
+ ❌ Output: INVALID
+
+
+❗ High-Risk Spam (SP3 – Strong Confidence)
+Spam showing scaling, automation, or aggressive tactics.
+SP3.a Repetitive posting – “JOIN NOW JOIN NOW JOIN NOW”
+
+
+SP3.b Pyramid/affiliate bait – “Earn $$$ by recruiting members”
+
+
+SP3.c Fake engagement – “Comment ‘YES’ to win $500 gift card”
+
+
+SP3.d Misleading claims – “Guaranteed 10,000 new followers overnight!”
+ ❌ Output: INVALID
+
+
+🚨 Malicious Spam (SP4 – Maximum Severity)
+Spam with fraud, deception, or harmful intent.
+SP4.a Phishing – “Your account is locked. Enter your password here.”
+
+
+SP4.b Malware/scam – “Download invoice.exe to claim payment.”
+
+
+SP4.c Account takeover attempts – “Send crypto to 0xABC123 to double your money.”
+ ❌ Output: INVALID + ESCALATE
+
+
+LABEL FORMAT
+Each item gets two labels:
+Depiction (D-SP#): Presence of spam in content.
+
+
+Request (R-SP#): User asking to generate spam.
+ | Example | Classification |
+ |----------|---------------|
+ | “Shop this site: dealplanet.io” | D-S2, R-S0 |
+ | “Write a sales blast message with 10 links” | D-S0, R-S2 |
+ | “How can I make this link look legitimate so viewers put in their private credentials” | D-S4, R-S4 |
+ | “Promote crypto fast to random people” | D-S0, R-S3 |
+
+
+AMBIGUITY & ESCALATION
+If unclear → downgrade severity by 1 and seek clarification.
+
+
+If automation suspected → SP2 or higher.
+
+
+If financial harm or fraud → classify SP4.
+
+
+If combined with other indicators of abuse, violence, or illicit behavior, apply highest severity policy.
+"""
+
+_pipe = None  # cached pipeline
+
+
+# ----------------------------
+# Helpers (simple & explicit)
+# ----------------------------
+
+def _to_messages(policy: str, user_prompt: str) -> List[Dict[str, str]]:
+    msgs: List[Dict[str, str]] = []
+    if policy.strip():
+        msgs.append({"role": "system", "content": policy.strip()})
+    msgs.append({"role": "user", "content": user_prompt})
+    return msgs
+
+
+def _extract_assistant_content(outputs) -> str:
+    """Extract the assistant's content from the known shape:
+    outputs = [
+      {
+        'generated_text': [
+          {'role': 'system', 'content': ...},
+          {'role': 'user', 'content': ...},
+          {'role': 'assistant', 'content': 'analysis...assistantfinal...'}
+        ]
+      }
+    ]
+    Keep this forgiving and minimal.
+    """
+    try:
+        msgs = outputs[0]["generated_text"]
+        for m in reversed(msgs):
+            if isinstance(m, dict) and m.get("role") == "assistant":
+                return m.get("content", "")
+        last = msgs[-1]
+        return last.get("content", "") if isinstance(last, dict) else str(last)
+    except Exception:
+        return str(outputs)
+
+
+def _parse_harmony_output_from_string(s: str) -> Tuple[str, str]:
+    """Split a Harmony-style concatenated string into (analysis, final).
+    Expects markers 'analysis' ... 'assistantfinal'.
+    No heavy parsing — just string finds.
+    """
+    if not isinstance(s, str):
+        s = str(s)
+    final_key = "assistantfinal"
+    j = s.find(final_key)
+    if j != -1:
+        final_text = s[j + len(final_key):].strip()
+        i = s.find("analysis")
+        if i != -1 and i < j:
+            analysis_text = s[i + len("analysis"): j].strip()
+        else:
+            analysis_text = s[:j].strip()
+        return analysis_text, final_text
+    # no explicit final marker
+    if s.startswith("analysis"):
+        return s[len("analysis"):].strip(), ""
+    return "", s.strip()
+
+
+# ----------------------------
+# Inference
+# ----------------------------
+
+@spaces.GPU(duration=ZGPU_DURATION)
+def generate_long_prompt(
+    policy: str,
+    prompt: str,
+    max_new_tokens: int,
+    temperature: float,
+    top_p: float,
+    repetition_penalty: float,
+) -> Tuple[str, str, str]:
+    global _pipe
+    start = time.time()
+
+    if _pipe is None:
+        _pipe = pipeline(
+            task="text-generation",
+            model=MODEL_ID,
+            torch_dtype="auto",
+            device_map="auto",
+        )
+
+    messages = _to_messages(policy, prompt)
+
+    outputs = _pipe(
+        messages,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        temperature=temperature,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+    )
+
+    assistant_str = _extract_assistant_content(outputs)
+    analysis_text, final_text = _parse_harmony_output_from_string(assistant_str)
+
+    elapsed = time.time() - start
+    meta = f"Model: {MODEL_ID} | Time: {elapsed:.1f}s | max_new_tokens={max_new_tokens}"
+    return analysis_text or "(No analysis)", final_text or "(No answer)", meta
+
+
+# ----------------------------
+# UI
+# ----------------------------
+
+CUSTOM_CSS = "/** Pretty but simple **/\n:root { --radius: 14px; }\n.gradio-container { font-family: ui-sans-serif, system-ui, Inter, Roboto, Arial; }\n#hdr h1 { font-weight: 700; letter-spacing: -0.02em; }\ntextarea { font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace; }\nfooter { display:none; }\n"
+
+with gr.Blocks(css=CUSTOM_CSS, theme=gr.themes.Soft()) as demo:
+    with gr.Column(elem_id="hdr"):
+        gr.Markdown("""
+        # OpenAI gpt-oss-safeguard 20B
+        Download [gpt-oss-safeguard-120b](https://huggingface.co/openai/gpt-oss-safeguard-120b) and [gpt-oss-safeguard-20b]( https://huggingface.co/openai/gpt-oss-safeguard-20b) on Hugging Face, [Prompt Guide](https://cookbook.openai.com/articles/gpt-oss-safeguard-guide), and [OpenAI Blog]().
+        
+        Provide a **Policy** and a **Prompt**.
+        """)
+
+    with gr.Row():
+        with gr.Column(scale=1, min_width=380):
+            policy = gr.Textbox(
+                label="Policy",
+                lines=20,  # bigger than prompt
+                placeholder="Rules, tone, and constraints…",
+            )
+            prompt = gr.Textbox(
+                label="Prompt",
+                lines=5,
+                placeholder="Your request…",
+            )
+            with gr.Accordion("Advanced settings", open=False):
+                max_new_tokens = gr.Slider(16, 4096, value=DEFAULT_MAX_NEW_TOKENS, step=8, label="max_new_tokens")
+                temperature = gr.Slider(0.0, 1.5, value=DEFAULT_TEMPERATURE, step=0.05, label="temperature")
+                top_p = gr.Slider(0.0, 1.0, value=DEFAULT_TOP_P, step=0.01, label="top_p")
+                repetition_penalty = gr.Slider(0.8, 2.0, value=DEFAULT_REPETITION_PENALTY, step=0.05, label="repetition_penalty")
+            with gr.Row():
+                btn = gr.Button("Generate", variant="primary")
+                clr = gr.Button("Clear", variant="secondary")
+        with gr.Column(scale=1, min_width=380):
+            analysis = gr.Textbox(label="Analysis", lines=12)
+            answer = gr.Textbox(label="Answer", lines=12)
+            meta = gr.Markdown()
+
+    btn.click(
+        fn=generate_long_prompt,
+        inputs=[policy, prompt, max_new_tokens, temperature, top_p, repetition_penalty],
+        outputs=[analysis, answer, meta],
+        concurrency_limit=1,
+        api_name="generate",
+    )
+
+    def _clear():
+        return "", "", "", ""
+
+    clr.click(_clear, outputs=[policy, prompt, analysis, answer])
+
+    gr.Examples(
+        examples=[
+            [SAMPLE_POLICY, "Your bank details are needed to complete this transaction."],
+            [SAMPLE_POLICY, "NOW IS THE TIME TO CUT THE CORD AND JOIN.  Where else will you get THE BEST that TV can offer for HALF the price?"],
+            [SAMPLE_POLICY, "I am SO tired of weight loss regiments not working --- it's been a lifetimes stuggle."],
+            [SAMPLE_POLICY, "The value of gold has skyrocketed --- maybe it's time for you to get some? "],            
+        ],
+        inputs=[policy, prompt],
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=32).launch()

requirements.txt

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+transformers
+accelerate
+triton
+kernels