Upload 8 files (#3)

- Upload 8 files (e15ba9b54d5b7fade3af4eaf628b4e89d2355415)


Co-authored-by: Anmol Kesarwani <Spacexpedition@users.noreply.huggingface.co>

README.md

@@ -8,3 +8,85 @@ pinned: false
 license: mit
 app_port: 8000
 ---
+Mail Triage Agent v4 (Security Evaluation)
+
+This repository contains a high-fidelity environment and an autonomous agent designed for Email Security Triage. The project focuses on detecting sophisticated threats like "Digital Seduction" (Phishing), typo-squatted domains, and malicious URL redirections.
+
+🚀 Overview
+
+The system consists of two primary components:
+
+Environment (env.py): A FastAPI-based server implementing the OpenEnv specification. it serves a dataset of 15 email scenarios categorized by difficulty (1 to 3).
+
+Agent Logic (inference.py): An LLM-powered agent (using gemini-2.0-flash) that analyzes email metadata, headers, and URLs to make triage decisions.
+
+🛠 Project Structure
+
+env.py: The core environment logic. Includes the dataset and scoring metrics.
+
+inference.py: The agent's decision-making loop.
+
+models.py: Pydantic models defining the Observation and Action spaces.
+
+openenv.yaml: Metadata for the OpenEnv benchmark framework.
+
+Dockerfile: Containerization setup for deployment.
+
+requirements.txt: Python dependencies.
+
+🧪 Scoring Logic
+
+The environment uses a sophisticated reward system:
+
+Perfect Classification: 1.0 + (difficulty * 0.1)
+
+Partial Credit: 0.4 (e.g., classifying Phishing as Spam).
+
+Dangerous Failure: -1.5 (e.g., letting Phishing into the INBOX).
+
+False Positive: -0.5 (e.g., blocking legitimate mail).
+
+Reasoning Bonus: +0.05 for providing detailed justifications.
+
+⚙️ Setup & Installation
+
+Prerequisites
+
+Docker (optional)
+
+Python 3.10+
+
+A Google Gemini API Key
+
+Local Execution
+
+Install dependencies:
+
+pip install -r requirements.txt
+
+
+Set your environment variables:
+
+export GEMINI_API_KEY="your_api_key_here"
+
+
+Run the environment server:
+
+uvicorn env:app --host 0.0.0.0 --port 8000
+
+
+In a separate terminal, run the agent:
+
+python inference.py
+
+
+🛡 Security Scenarios Covered
+
+Clean: Official Manipal or Amazon communications with valid SPF/DKIM.
+
+Spam: Marketing mail from Swiggy or Internshala.
+
+Phishing: Typo-squatted domains (e.g., manipal-edu.in vs manipal.edu) and shortened URLs.
+
+Credential Theft: Fake security alerts from bank/Google look-alikes.
+

env.py

@@ -1,8 +1,8 @@
 import asyncio
 import random
-from typing import Optional, List, Dict
+from typing import Optional, List, Dict, Any
 from types import SimpleNamespace
-from fastapi import FastAPI 
+from fastapi import FastAPI, Body
 from openenv.core.env_server import Environment
 from models import MyEnvV4Observation, MyEnvV4Action, URLInfo
 
@@ -11,16 +11,14 @@ class MyEnvV4Env(Environment):
     def __init__(self):
         super().__init__()
         self.dataset = self._generate_sophisticated_dataset()
-        # For evaluation reproducibility, we could shuffle, but for benchmark stability, we keep order
         self.current_step = 0
 
     def _generate_sophisticated_dataset(self):
         """
         Expanded dataset with 15 samples across 3 difficulty levels.
-        Covers Clean, Spam, and Phishing (Digital Seduction).
         """
         base_data = [
-            # LEVEL 1: CLEAR CASES (5)
+            # LEVEL 1: CLEAR CASES
             {
                 "sender": "registrar@manipal.edu",
                 "subject": "Semester Registration Open",
@@ -37,7 +35,9 @@ class MyEnvV4Env(Environment):
                 "body": "Track your package delivery status in your Amazon account.",
                 "raw_headers": "Received: from a9-12.smtp-out.amazonses.com... SPF: pass; DKIM: pass;",
                 "auth_results": {"SPF": "pass", "DKIM": "pass", "DMARC": "pass"},
-                "urls": [{"url": "https://amazon.com/track", "is_shortened": False, "domain_age_days": 8000, "has_ssl": True, "reputation_score": 1.0}],
+                "urls": [
+                    {"url": "https://amazon.com/track", "is_shortened": False, "domain_age_days": 8000, "has_ssl": True,
+                     "reputation_score": 1.0}],
                 "label": "INBOX",
                 "difficulty": 1
             },
@@ -47,7 +47,8 @@ class MyEnvV4Env(Environment):
                 "body": "Congratulations! You have been selected as our winner. CLAIM YOUR $1M NOW!",
                 "raw_headers": "Received: from unknown-relay.co (103.22.1.5)... SPF: none; DKIM: fail;",
                 "auth_results": {"SPF": "none", "DKIM": "fail", "DMARC": "none"},
-                "urls": [{"url": "http://get-cash-free.net/claim", "is_shortened": False, "domain_age_days": 2, "has_ssl": False, "reputation_score": 0.1}],
+                "urls": [{"url": "http://get-cash-free.net/claim", "is_shortened": False, "domain_age_days": 2,
+                          "has_ssl": False, "reputation_score": 0.1}],
                 "label": "SPAM",
                 "difficulty": 1
             },
@@ -57,7 +58,8 @@ class MyEnvV4Env(Environment):
                 "body": "Buy now and save 90% on all prescription drugs. No prescription needed!",
                 "raw_headers": "Received: from botnet-node.ru... SPF: softfail;",
                 "auth_results": {"SPF": "softfail", "DKIM": "none", "DMARC": "none"},
-                "urls": [{"url": "http://cheap-rx.biz", "is_shortened": False, "domain_age_days": 15, "has_ssl": False, "reputation_score": 0.05}],
+                "urls": [{"url": "http://cheap-rx.biz", "is_shortened": False, "domain_age_days": 15, "has_ssl": False,
+                          "reputation_score": 0.05}],
                 "label": "SPAM",
                 "difficulty": 1
             },
@@ -67,20 +69,21 @@ class MyEnvV4Env(Environment):
                 "body": "Your Netflix subscription has expired. Click here to login and update billing.",
                 "raw_headers": "Received: from suspicious-vps.com... SPF: fail; DMARC: fail;",
                 "auth_results": {"SPF": "fail", "DKIM": "none", "DMARC": "fail"},
-                "urls": [{"url": "https://bit.ly/fake-netflix-login", "is_shortened": True, "domain_age_days": 3, "has_ssl": True, "reputation_score": 0.02}],
+                "urls": [{"url": "https://bit.ly/fake-netflix-login", "is_shortened": True, "domain_age_days": 3,
+                          "has_ssl": True, "reputation_score": 0.02}],
                 "label": "QUARANTINE",
                 "difficulty": 1
             },
-
-            # LEVEL 2: NUANCED / MARKETING / LEGIT BUT ANNOYING (5)
+            # LEVEL 2: NUANCED
             {
                 "sender": "news@internshala-mail.com",
                 "subject": "New Internships in Manipal",
                 "body": "Check out these new opportunities for CSE students. Apply today!",
                 "raw_headers": "Received: from mktg.server.com... SPF: pass; DKIM: pass;",
                 "auth_results": {"SPF": "pass", "DKIM": "pass", "DMARC": "pass"},
-                "urls": [{"url": "https://internshala.com/n/123", "is_shortened": False, "domain_age_days": 2500, "has_ssl": True, "reputation_score": 0.95}],
-                "label": "SPAM", # Triaged as Promotions/Spam
+                "urls": [{"url": "https://internshala.com/n/123", "is_shortened": False, "domain_age_days": 2500,
+                          "has_ssl": True, "reputation_score": 0.95}],
+                "label": "SPAM",
                 "difficulty": 2
             },
             {
@@ -93,14 +96,15 @@ class MyEnvV4Env(Environment):
                 "label": "SPAM",
                 "difficulty": 2
             },
-             {
+            {
                 "sender": "hr@startup-hiring.co",
                 "subject": "Interview Invitation",
                 "body": "We saw your profile on LinkedIn and want to chat about a role.",
                 "raw_headers": "Received: from linkedin-referral.com... SPF: neutral;",
                 "auth_results": {"SPF": "neutral", "DKIM": "none", "DMARC": "none"},
-                "urls": [{"url": "https://startup-hiring.co/apply", "is_shortened": False, "domain_age_days": 45, "has_ssl": True, "reputation_score": 0.6}],
-                "label": "INBOX", # Legitimate cold outreach
+                "urls": [{"url": "https://startup-hiring.co/apply", "is_shortened": False, "domain_age_days": 45,
+                          "has_ssl": True, "reputation_score": 0.6}],
+                "label": "INBOX",
                 "difficulty": 2
             },
             {
@@ -109,7 +113,8 @@ class MyEnvV4Env(Environment):
                 "body": "We detected an unusual login to your account from Russia. Please verify.",
                 "raw_headers": "Received: from spoofed-host.com... SPF: softfail; DMARC: none;",
                 "auth_results": {"SPF": "softfail", "DKIM": "none", "DMARC": "none"},
-                "urls": [{"url": "https://t.co/secure-bank-login", "is_shortened": True, "domain_age_days": 10, "has_ssl": True, "reputation_score": 0.3}],
+                "urls": [{"url": "https://t.co/secure-bank-login", "is_shortened": True, "domain_age_days": 10,
+                          "has_ssl": True, "reputation_score": 0.3}],
                 "label": "QUARANTINE",
                 "difficulty": 2
             },
@@ -119,61 +124,66 @@ class MyEnvV4Env(Environment):
                 "body": "A new personal access token was added to your account. If this wasn't you, click here.",
                 "raw_headers": "Received: from out-21.smtp.github.com... SPF: pass; DKIM: pass;",
                 "auth_results": {"SPF": "pass", "DKIM": "pass", "DMARC": "pass"},
-                "urls": [{"url": "https://github.com/settings/tokens", "is_shortened": False, "domain_age_days": 6000, "has_ssl": True, "reputation_score": 1.0}],
+                "urls": [{"url": "https://github.com/settings/tokens", "is_shortened": False, "domain_age_days": 6000,
+                          "has_ssl": True, "reputation_score": 1.0}],
                 "label": "INBOX",
                 "difficulty": 2
             },
-
-            # LEVEL 3: DIGITAL SEDUCTION (PHISHING / SPEAR PHISHING) (5)
+            # LEVEL 3: SPEAR PHISHING
             {
-                "sender": "dean.office@manipal-edu.in", # Typo-squatted (.in instead of .edu)
+                "sender": "dean.office@manipal-edu.in",
                 "subject": "Urgent: Faculty Grievance Report",
                 "body": "A report has been filed against your department. Review the grievances here immediately.",
-                "raw_headers": "Received: from rogue-vps.xyz (45.33.22.11) by mx.google.com; SPF: fail; DMARC: fail;",
+                "raw_headers": "Received: from rogue-vps.xyz... SPF: fail; DMARC: fail;",
                 "auth_results": {"SPF": "fail", "DKIM": "none", "DMARC": "fail"},
-                "urls": [{"url": "https://bit.ly/3xYz1-grievance", "is_shortened": True, "domain_age_days": 5, "has_ssl": True, "reputation_score": 0.05}],
+                "urls": [{"url": "https://bit.ly/3xYz1-grievance", "is_shortened": True, "domain_age_days": 5,
+                          "has_ssl": True, "reputation_score": 0.05}],
                 "label": "QUARANTINE",
                 "difficulty": 3
             },
             {
-                "sender": "it-support@manipal-helpdesk.com", # Look-alike domain
+                "sender": "it-support@manipal-helpdesk.com",
                 "subject": "Mandatory Password Reset",
                 "body": "As per the new MIT security policy, all students must reset their password today.",
-                "raw_headers": "Received: from mail-delivery.online... SPF: pass; DKIM: pass;", # Attacker set up SPF/DKIM correctly!
+                "raw_headers": "Received: from mail-delivery.online... SPF: pass; DKIM: pass;",
                 "auth_results": {"SPF": "pass", "DKIM": "pass", "DMARC": "none"},
-                "urls": [{"url": "http://manipal-helpdesk.com/reset", "is_shortened": False, "domain_age_days": 1, "has_ssl": False, "reputation_score": 0.1}],
+                "urls": [{"url": "http://manipal-helpdesk.com/reset", "is_shortened": False, "domain_age_days": 1,
+                          "has_ssl": False, "reputation_score": 0.1}],
                 "label": "QUARANTINE",
                 "difficulty": 3
             },
             {
-                "sender": "prof.sharma@mit-manipal.org", # Wrong TLD
+                "sender": "prof.sharma@mit-manipal.org",
                 "subject": "Final Exam Paper Leak?",
-                "body": "I suspect the paper has leaked. Look at this screenshot and confirm if these are your questions.",
+                "body": "I suspect the paper has leaked. Look at this screenshot.",
                 "raw_headers": "Received: from sendgrid.net... SPF: pass;",
                 "auth_results": {"SPF": "pass", "DKIM": "none", "DMARC": "none"},
-                "urls": [{"url": "https://dropbox-files.com/s/xyz", "is_shortened": False, "domain_age_days": 4, "has_ssl": True, "reputation_score": 0.2}],
+                "urls": [{"url": "https://dropbox-files.com/s/xyz", "is_shortened": False, "domain_age_days": 4,
+                          "has_ssl": True, "reputation_score": 0.2}],
                 "label": "QUARANTINE",
                 "difficulty": 3
             },
             {
                 "sender": "accounts@google-security.info",
                 "subject": "Critical Security Alert",
-                "body": "Someone just used your password to try to sign in to your account. Go to your Google account now.",
+                "body": "Someone just used your password to try to sign in.",
                 "raw_headers": "Received: from host-12.xyz... SPF: fail;",
                 "auth_results": {"SPF": "fail", "DKIM": "none", "DMARC": "fail"},
-                "urls": [{"url": "https://google-secure-login.info", "is_shortened": False, "domain_age_days": 2, "has_ssl": True, "reputation_score": 0.01}],
+                "urls": [{"url": "https://google-secure-login.info", "is_shortened": False, "domain_age_days": 2,
+                          "has_ssl": True, "reputation_score": 0.01}],
                 "label": "QUARANTINE",
                 "difficulty": 3
             },
             {
                 "sender": "library@manipal.edu",
                 "subject": "Overdue Book Notice",
-                "body": "Your copy of 'Computer Networks' is overdue. Click to pay the fine of ₹50.",
+                "body": "Your copy of 'Computer Networks' is overdue.",
                 "raw_headers": "Received: from mail.manipal.edu... SPF: pass; DKIM: pass;",
                 "auth_results": {"SPF": "pass", "DKIM": "pass", "DMARC": "pass"},
-                "urls": [{"url": "https://portal.manipal.edu/pay", "is_shortened": False, "domain_age_days": 4000, "has_ssl": True, "reputation_score": 1.0}],
+                "urls": [{"url": "https://portal.manipal.edu/pay", "is_shortened": False, "domain_age_days": 4000,
+                          "has_ssl": True, "reputation_score": 1.0}],
                 "label": "INBOX",
-                "difficulty": 3 # Difficult because it looks like a phishing lure but is legit.
+                "difficulty": 3
             }
         ]
         return base_data
@@ -189,7 +199,7 @@ class MyEnvV4Env(Environment):
                 hop_count=0, auth_results={}, urls=[], echoed_message="End of Session"
             )
             return SimpleNamespace(observation=obs, reward=reward, done=True)
-        
+
         data = self.dataset[self.current_step]
         obs = MyEnvV4Observation(
             sender=data["sender"],
@@ -210,54 +220,47 @@ class MyEnvV4Env(Environment):
         target = self.dataset[self.current_step]
         correct = target["label"]
         prediction = action.message.strip().upper()
-        
-        # SOPHISTICATED REWARD LOGIC
+
         reward = 0.0
-        
         if prediction == correct:
-            # Perfect Match: reward scales with difficulty
-            reward = 1.0 + (target["difficulty"] * 0.1) 
+            reward = 1.0 + (target["difficulty"] * 0.1)
         elif correct in ["SPAM", "QUARANTINE"] and prediction in ["SPAM", "QUARANTINE"]:
-            # Partial Credit: Recognized threat but misclassified type
             reward = 0.4
         elif correct == "QUARANTINE" and prediction == "INBOX":
-            # Dangerous Failure: Penalty for letting a threat into the Inbox
-            reward = -1.5 
+            reward = -1.5
         elif correct == "INBOX" and prediction == "QUARANTINE":
-            # False Positive: Penalty for blocking legitimate mail
             reward = -0.5
-        
-        # Add Reasoning Bonus (Explainability)
+
         if hasattr(action, 'reasoning') and action.reasoning and len(action.reasoning) > 30:
-            # Small bonus if agent provides a justification
             reward += 0.05
 
-        # Normalize reward to [0, 1] range as per OpenEnv specs (clipping/rescaling)
-        # However, many environments allow negative for penalties; we clip to [0,1] for final score
         final_reward = max(0.0, min(1.0, reward))
-
         self.current_step += 1
         done = self.current_step >= len(self.dataset)
-        
+
         return self._get_result(reward=final_reward, done=done)
 
     async def state(self):
         return {"current_step": self.current_step, "total_tasks": len(self.dataset)}
 
-# Global instance for the server
+
+# Global instance
 my_env = MyEnvV4Env()
 app = FastAPI()
 
-@app.get("/reset")
-async def reset():
+
+@app.post("/reset")  # FIXED: Must be POST for OpenEnv validators
+async def reset(payload: Dict[Any, Any] = Body(default={})):
     res = await my_env.reset()
     return {"observation": res.observation, "reward": res.reward, "done": res.done}
 
+
 @app.post("/step")
 async def step(action: MyEnvV4Action):
     res = await my_env.step(action)
     return {"observation": res.observation, "reward": res.reward, "done": res.done}
 
+
 @app.get("/state")
 async def state():
     return await my_env.state()

gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

inference.py

@@ -1,78 +1,88 @@
 import asyncio
 import os
-from typing import List
+import json
 from openai import OpenAI
 from env import MyEnvV4Env
 from models import MyEnvV4Action
 
 # Environment Configuration
-# FIX: Point directly to Google's OpenAI-compatible endpoint for Gemini models
+# Standard OpenEnv evaluation environments inject these env vars
 API_BASE_URL = os.getenv("API_BASE_URL") or "https://generativelanguage.googleapis.com/v1beta/openai/"
-# FIX: Use Gemini API Key instead of Hugging Face token
-API_KEY = os.getenv("GEMINI_API_KEY") or ""
+API_KEY = os.getenv("GEMINI_API_KEY") or os.getenv("OPENAI_API_KEY") or ""
 MODEL_NAME = "gemini-2.0-flash"
-TASK_NAME = "security-mail-triage"
 
 SYSTEM_PROMPT = """
-You are an Advanced Email Security Agent. Analyze the metadata (headers, SPF/DKIM), URLs, and content.
+You are an Advanced Email Security Agent. Analyze the metadata, URLs, and content.
 Categories:
-- INBOX: Trusted academic/official domains, passed auth, clean history.
-- SPAM: Mass marketing, generic lottery/sales, usually safe but unwanted.
-- QUARANTINE: Phishing, spear-phishing, credential theft, high-urgency threats, typo-squatted domains.
+- INBOX: Trusted academic/official domains, passed auth.
+- SPAM: Unwanted marketing or sales.
+- QUARANTINE: Phishing, typo-squatting, or high-risk threats.
 
-Rules:
-1. Examine 'raw_headers' and 'auth_results'.
-2. Inspect 'urls' for low reputation or high age.
-3. Provide reasoning first, then your decision.
-
-Respond in JSON format:
+Respond in strict JSON:
 {
-  "reasoning": "Explain your logic here...",
+  "reasoning": "Explain your logic...",
   "message": "INBOX|SPAM|QUARANTINE"
 }
 """
 
 
 async def main():
+    if not API_KEY:
+        print("[ERROR] No API key found. Please set GEMINI_API_KEY.")
+        return
+
     client = OpenAI(base_url=API_BASE_URL, api_key=API_KEY)
     env = MyEnvV4Env()
 
     rewards = []
-    print(f"[START] Testing Security Triage Environment...")
+    print(f"[START] Running Security Triage Evaluation...")
 
+    # OpenEnv Reset
     result = await env.reset()
     step_idx = 1
 
     while not result.done:
         obs = result.observation
-        prompt = f"Sender: {obs.sender}\nSubject: {obs.subject}\nBody: {obs.body}\nHeaders: {obs.raw_headers}\nURLs: {obs.urls}"
+        # Prepare the prompt by dumping complex URL objects to dictionaries
+        prompt = (
+            f"Sender: {obs.sender}\n"
+            f"Subject: {obs.subject}\n"
+            f"Body: {obs.body}\n"
+            f"Headers: {obs.raw_headers}\n"
+            f"Auth: {obs.auth_results}\n"
+            f"URLs: {[u.model_dump() for u in obs.urls]}"
+        )
 
         try:
             response = client.chat.completions.create(
                 model=MODEL_NAME,
-                messages=[{"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": prompt}],
+                messages=[
+                    {"role": "system", "content": SYSTEM_PROMPT},
+                    {"role": "user", "content": prompt}
+                ],
                 response_format={"type": "json_object"},
                 temperature=0.0
             )
-            import json
-            data = json.loads(response.choices[0].message.content)
 
+            content = response.choices[0].message.content
+            data = json.loads(content)
+
+            # Create action and step the environment
             action = MyEnvV4Action(message=data["message"], reasoning=data["reasoning"])
             result = await env.step(action)
             rewards.append(result.reward)
 
-            print(f"[STEP {step_idx}] Action: {action.message} | Reward: {result.reward:.2f}")
+            print(f"[STEP {step_idx}] Result: {action.message} | Reward: {result.reward:.2f}")
             step_idx += 1
 
-            # Prevent hitting Gemini Free Tier rate limits (15 requests per minute & token limits)
-            # Increased to 10 seconds to ensure we do not hit the burst quotas.
-            await asyncio.sleep(10)
+            # Sleep to respect rate limits (Gemini 2.0 Flash)
+            await asyncio.sleep(2)
         except Exception as e:
             print(f"[ERROR] Step {step_idx}: {e}")
             break
 
-    score = sum(rewards) / len(rewards) if rewards else 0
-    print(f"[END] Final Score: {score:.3f}")
+    final_score = sum(rewards) / len(rewards) if rewards else 0
+    print(f"[END] Evaluation Complete. Final Score: {final_score:.3f}")
 
 
 if __name__ == "__main__":

openenv.yaml

@@ -4,7 +4,7 @@ version: "2.0.0"
 description: "A high-fidelity security evaluation environment for email triage, featuring difficulty scaling, technical header analysis, and URL reputation modeling."
 
 # Environment Specification
-repo_url: "https://huggingface.co/spaces/spacexpedition/mail_triage"
+repo_url: "https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME"
 task_type: "classification"
 
 # Compliance Metrics & Scoring
@@ -14,6 +14,8 @@ tags:
   - security
   - phishing-detection
   - metadata-analysis
+  - mit-manipal-hackathon
+  - digital-seduction
 
 # Typed Model References
 # These map to the classes defined in models.py