Blame for inference.diff

��commit b73fc11a8c99354df8c68bac26d7338aa0f40313

Author: VISHAL_V <vish1603v@gmail.com>

Date:   Tue Apr 7 14:33:13 2026 +0530



    fix: enforce strict (0,1) score range with centralized clamp_score validator - add Pydantic field_validator on Reward.score to auto-clamp to (0.01, 0.99) - use centralized clamp_score in graders.py and environment.py - clamp scores in inference.py before printing



diff --git a/inference.py b/inference.py

index 2bdb4d1..67aaf17 100644

--- a/inference.py

+++ b/inference.py

@@ -131,8 +131,9 @@ def run_task(task_id: str) -> float:

         done = response.get("done", True)

 

         score_now = reward_dict.get("score", 0.01) if isinstance(reward_dict, dict) else 0.01

+        score_now = max(0.01, min(0.99, float(score_now)))

         success = done and score_now > 0.5

-        print(f"[STEP] reward={score_now:.2f} done={str(done).lower()} success={str(success).lower()}")

+        print(f"[STEP] reward={score_now:.4f} done={str(done).lower()} success={str(success).lower()}")

 

     print("[END]")

     final_score = reward_dict.get("score", 0.01) if isinstance(reward_dict, dict) else 0.01



commit b5e447a011fb71978124db2d94ad0aeaa74c8285

Author: VISHAL_V <vish1603v@gmail.com>

Date:   Tue Apr 7 12:51:29 2026 +0530



    Fix score bounds: clamp to (0.01, 0.99) instead of [0.0, 1.0]



diff --git a/inference.py b/inference.py

index d9fbbc6..2bdb4d1 100644

--- a/inference.py

+++ b/inference.py

@@ -62,7 +62,7 @@ def step_env(action: dict) -> dict:

         print(f"[ERROR] Step failed: {e}", file=sys.stderr)

         return {

             "observation": {},

-            "reward": {"score": 0.0, "breakdown": {}},

+            "reward": {"score": 0.01, "breakdown": {}},

             "done": True,

             "info": {"error": str(e)}

         }

@@ -113,13 +113,13 @@ def run_task(task_id: str) -> float:

 

     obs = reset_env(task_id)

     if not obs:

-        print("[STEP] reward=0.00 done=true success=false")

+        print("[STEP] reward=0.01 done=true success=false")

         print("[END]")

-        return 0.0

+        return 0.01

 

     done = False

     steps = 0

-    reward_dict: dict = {"score": 0.0}

+    reward_dict: dict = {"score": 0.01}

 

     while not done and steps < MAX_STEPS:

         steps += 1

@@ -127,15 +127,16 @@ def run_task(task_id: str) -> float:

 

         response = step_env(action)

         obs = response.get("observation", {})

-        reward_dict = response.get("reward", {"score": 0.0})

+        reward_dict = response.get("reward", {"score": 0.01})

         done = response.get("done", True)

 

-        score_now = reward_dict.get("score", 0.0) if isinstance(reward_dict, dict) else 0.0

+        score_now = reward_dict.get("score", 0.01) if isinstance(reward_dict, dict) else 0.01

         success = done and score_now > 0.5

         print(f"[STEP] reward={score_now:.2f} done={str(done).lower()} success={str(success).lower()}")

 

     print("[END]")

-    final_score = reward_dict.get("score", 0.0) if isinstance(reward_dict, dict) else 0.0

+    final_score = reward_dict.get("score", 0.01) if isinstance(reward_dict, dict) else 0.01

+    final_score = max(0.01, min(0.99, final_score))

     return final_score

 

 



commit 4d30c103b600a290bbdd3f9d8fe494fbb358f87e

Author: VISHAL_V <vish1603v@gmail.com>

Date:   Tue Apr 7 10:55:44 2026 +0530



    fix: inference.py stdout format compliance for [START]/[STEP]/[END] spec



diff --git a/inference.py b/inference.py

index da78eb6..d9fbbc6 100644

--- a/inference.py

+++ b/inference.py

@@ -1,4 +1,5 @@

 import os

+import sys

 import json

 import requests

 from typing import Dict, Any

@@ -47,7 +48,7 @@ def reset_env(task_id: str) -> dict:

         response.raise_for_status()

         return response.json()

     except Exception as e:

-        print(f"[ERROR] Reset failed: {e}")

+        print(f"[ERROR] Reset failed: {e}", file=sys.stderr)

         return {}

 

 

@@ -58,7 +59,7 @@ def step_env(action: dict) -> dict:

         response.raise_for_status()

         return response.json()

     except Exception as e:

-        print(f"[ERROR] Step failed: {e}")

+        print(f"[ERROR] Step failed: {e}", file=sys.stderr)

         return {

             "observation": {},

             "reward": {"score": 0.0, "breakdown": {}},

@@ -103,18 +104,17 @@ def get_action_from_llm(obs: dict) -> dict:

         return action_obj

 

     except Exception as e:

-        print(f"[ERROR] LLM call failed: {e}")

+        print(f"[ERROR] LLM call failed: {e}", file=sys.stderr)

         return dict(NOOP_ACTION)

 

 

 def run_task(task_id: str) -> float:

-    print(f"\n{'='*40}")

-    print(f"Starting {task_id}")

-    print(f"{'='*40}")

+    print("[START]")

 

     obs = reset_env(task_id)

     if not obs:

-        print(f"[WARN] Failed to initialize environment for {task_id}.")

+        print("[STEP] reward=0.00 done=true success=false")

+        print("[END]")

         return 0.0

 

     done = False

@@ -124,20 +124,18 @@ def run_task(task_id: str) -> float:

     while not done and steps < MAX_STEPS:

         steps += 1

         action = get_action_from_llm(obs)

-        print(f"  Step {steps} | Action: {action}")

 

         response = step_env(action)

         obs = response.get("observation", {})

         reward_dict = response.get("reward", {"score": 0.0})

         done = response.get("done", True)

-        info = response.get("info", {})

 

         score_now = reward_dict.get("score", 0.0) if isinstance(reward_dict, dict) else 0.0

-        print(f"  Score: {score_now} | Info: {info}")

-        print(f"  {'-'*36}")

+        success = done and score_now > 0.5

+        print(f"[STEP] reward={score_now:.2f} done={str(done).lower()} success={str(success).lower()}")

 

+    print("[END]")

     final_score = reward_dict.get("score", 0.0) if isinstance(reward_dict, dict) else 0.0

-    print(f"Task {task_id} complete. Final score: {final_score}")

     return final_score

 

 

@@ -149,16 +147,3 @@ if __name__ == "__main__":

         score = run_task(task)

         scores[task] = score

         total_score += score

-

-    print(f"\n\n{'='*40}")

-    print("FINAL RESULTS")

-    print(f"{'='*40}")

-    for t, s in scores.items():

-        print(f"  {t}: {s}")

-

-    avg_score = total_score / len(TASKS_TO_RUN) if TASKS_TO_RUN else 0.0

-    print(f"\nAverage Score: {avg_score:.4f}")

-    if avg_score >= 0.8:

-        print("Result: PASS ��� Agent performed well.")

-    else:

-        print("Result: NEEDS IMPROVEMENT ��� Agent performance is below threshold.")



commit 82b9c7a8a7b18863cb7a66fe0621d20a5f321d85

Author: VISHAL_V <vish1603v@gmail.com>

Date:   Fri Apr 3 12:31:09 2026 +0530



    Final Hackathon Submission



diff --git a/inference.py b/inference.py

new file mode 100644

index 0000000..da78eb6

--- /dev/null

+++ b/inference.py

@@ -0,0 +1,164 @@

+import os

+import json

+import requests

+from typing import Dict, Any

+from openai import OpenAI

+

+API_BASE_URL = os.getenv("API_BASE_URL")

+HF_TOKEN = os.getenv("HF_TOKEN")

+MODEL_NAME = os.getenv("MODEL_NAME", "gpt-4o-mini")

+ENV_BASE_URL = os.getenv("ENV_BASE_URL", "http://localhost:7860")

+

+client_kwargs = {"api_key": HF_TOKEN}

+if API_BASE_URL:

+    client_kwargs["base_url"] = API_BASE_URL

+

+client = OpenAI(**client_kwargs)

+

+TASKS_TO_RUN = ["task_1", "task_2", "task_3"]

+MAX_STEPS = 8

+REQUEST_TIMEOUT = 30

+

+SYSTEM_PROMPT = """You are an Enterprise Email Agent.

+Your goal is to handle incoming emails appropriately.

+Allowed Actions: classify_email, route_to, draft_reply, escalate, mark_spam, request_more_info, noop.

+

+You MUST respond strictly in valid JSON format containing two keys: "action_type" and "arguments".

+

+Example 1:

+{"action_type": "classify_email", "arguments": {"category": "support"}}

+

+Example 2:

+{"action_type": "route_to", "arguments": {"department": "sales_department"}}

+

+Example 3:

+{"action_type": "draft_reply", "arguments": {"text": "We are looking into this immediate issue."}}

+

+Always choose the most relevant action. Avoid unnecessary actions.

+"""

+

+NOOP_ACTION = {"action_type": "noop", "arguments": {}}

+

+

+def reset_env(task_id: str) -> dict:

+    url = f"{ENV_BASE_URL}/reset"

+    try:

+        response = requests.post(url, json={"task_id": task_id}, timeout=REQUEST_TIMEOUT)

+        response.raise_for_status()

+        return response.json()

+    except Exception as e:

+        print(f"[ERROR] Reset failed: {e}")

+        return {}

+

+

+def step_env(action: dict) -> dict:

+    url = f"{ENV_BASE_URL}/step"

+    try:

+        response = requests.post(url, json=action, timeout=REQUEST_TIMEOUT)

+        response.raise_for_status()

+        return response.json()

+    except Exception as e:

+        print(f"[ERROR] Step failed: {e}")

+        return {

+            "observation": {},

+            "reward": {"score": 0.0, "breakdown": {}},

+            "done": True,

+            "info": {"error": str(e)}

+        }

+

+

+def get_action_from_llm(obs: dict) -> dict:

+    prompt = f"Current Observation: {json.dumps(obs, indent=2)}\n\nWhat action will you take next? Reply ONLY with JSON."

+

+    try:

+        completion = client.chat.completions.create(

+            model=MODEL_NAME,

+            messages=[

+                {"role": "system", "content": SYSTEM_PROMPT},

+                {"role": "user", "content": prompt}

+            ],

+            temperature=0.0,

+            max_tokens=150,

+            response_format={"type": "json_object"}

+        )

+

+        reply = completion.choices[0].message.content

+        if not reply:

+            return dict(NOOP_ACTION)

+

+        try:

+            action_obj = json.loads(reply)

+        except (json.JSONDecodeError, TypeError):

+            return dict(NOOP_ACTION)

+

+        if not isinstance(action_obj, dict):

+            return dict(NOOP_ACTION)

+

+        if "action_type" not in action_obj or not isinstance(action_obj["action_type"], str):

+            return dict(NOOP_ACTION)

+

+        if "arguments" not in action_obj or not isinstance(action_obj["arguments"], dict):

+            action_obj["arguments"] = {}

+

+        return action_obj

+

+    except Exception as e:

+        print(f"[ERROR] LLM call failed: {e}")

+        return dict(NOOP_ACTION)

+

+

+def run_task(task_id: str) -> float:

+    print(f"\n{'='*40}")

+    print(f"Starting {task_id}")

+    print(f"{'='*40}")

+

+    obs = reset_env(task_id)

+    if not obs:

+        print(f"[WARN] Failed to initialize environment for {task_id}.")

+        return 0.0

+

+    done = False

+    steps = 0

+    reward_dict: dict = {"score": 0.0}

+

+    while not done and steps < MAX_STEPS:

+        steps += 1

+        action = get_action_from_llm(obs)

+        print(f"  Step {steps} | Action: {action}")

+

+        response = step_env(action)

+        obs = response.get("observation", {})

+        reward_dict = response.get("reward", {"score": 0.0})

+        done = response.get("done", True)

+        info = response.get("info", {})

+

+        score_now = reward_dict.get("score", 0.0) if isinstance(reward_dict, dict) else 0.0

+        print(f"  Score: {score_now} | Info: {info}")

+        print(f"  {'-'*36}")

+

+    final_score = reward_dict.get("score", 0.0) if isinstance(reward_dict, dict) else 0.0

+    print(f"Task {task_id} complete. Final score: {final_score}")

+    return final_score

+

+

+if __name__ == "__main__":

+    total_score = 0.0

+    scores: Dict[str, float] = {}

+

+    for task in TASKS_TO_RUN:

+        score = run_task(task)

+        scores[task] = score

+        total_score += score

+

+    print(f"\n\n{'='*40}")

+    print("FINAL RESULTS")

+    print(f"{'='*40}")

+    for t, s in scores.items():

+        print(f"  {t}: {s}")

+

+    avg_score = total_score / len(TASKS_TO_RUN) if TASKS_TO_RUN else 0.0

+    print(f"\nAverage Score: {avg_score:.4f}")

+    if avg_score >= 0.8:

+        print("Result: PASS ��� Agent performed well.")

+    else:

+        print("Result: NEEDS IMPROVEMENT ��� Agent performance is below threshold.")