#!/usr/bin/env python3 """ Inference script for the Government Service Application Assistant Environment. Uses OpenAI client with Groq API for stateful session management. """ import os import sys import json import requests from typing import Dict, Any API_BASE_URL = os.getenv("API_BASE_URL", "https://api.groq.com/openai/v1") MODEL_NAME = os.getenv("MODEL_NAME", "llama-3.3-70b-versatile") HF_TOKEN = os.getenv("HF_TOKEN", "") ENV_URL = os.getenv("ENV_URL", "https://dharunkkk-gov-env.hf.space") def check_prereqs(): """Check if required environment variables are set.""" if not HF_TOKEN: print("[ERROR] HF_TOKEN not set. Please set your Groq API key as HF_TOKEN env var.") sys.exit(1) if not ENV_URL: print("[ERROR] ENV_URL not set. Please set your Space URL as ENV_URL env var.") sys.exit(1) def check_space_health(): """Verify the HF Space is reachable.""" try: response = requests.get(f"{ENV_URL}/health", timeout=30) if response.status_code == 200: print(f"[INFO] Space reachable: {ENV_URL}") return True else: print(f"[ERROR] Space returned status {response.status_code}") return False except requests.exceptions.Timeout: print(f"[ERROR] Connection timeout to {ENV_URL}") return False except Exception as e: print(f"[ERROR] Cannot connect to {ENV_URL}: {e}") return False def get_tasks(base_url: str) -> list: """Get available tasks via HTTP.""" try: response = requests.get(f"{base_url}/tasks", timeout=30) response.raise_for_status() return response.json().get("tasks", []) except Exception as e: print(f"[ERROR] Failed to fetch tasks: {e}") return [] from openai import OpenAI llm_client = OpenAI( base_url=API_BASE_URL, api_key=HF_TOKEN ) try: from client import GovEnv from models import GovAction except ImportError as e: print(f"[ERROR] Import failed: {e}") sys.exit(1) def run_inference(task_id: str) -> None: """Run inference for a specific task.""" check_prereqs() if not check_space_health(): print(f"[ERROR] Space not reachable at {ENV_URL}") sys.exit(1) tasks = get_tasks(ENV_URL) if not tasks: print("[ERROR] Could not fetch tasks from space") sys.exit(1) task_info = None for task in tasks: if task["task_id"] == task_id: task_info = task break if not task_info: print(f"[ERROR] Task {task_id} not found. Available: {[t['task_id'] for t in tasks]}") sys.exit(1) print(f"[START] task={task_id} env=gov_env model={MODEL_NAME}") try: with GovEnv(base_url=ENV_URL).sync() as env: result = env.reset() current_obs = result.observation step_count = 0 total_reward = 0.0 done = False rewards = [] while not done and step_count < 10: step_count += 1 prompt = create_prompt(task_info, current_obs, step_count) try: completion = llm_client.chat.completions.create( model=MODEL_NAME, messages=[{"role": "user", "content": prompt}], temperature=0.1, max_tokens=500 ) action_text = completion.choices[0].message.content action_data = parse_action(action_text) gov_action = GovAction(**action_data) step_result = env.step(gov_action) current_obs = step_result.observation reward = step_result.reward if step_result.reward is not None else 0.0 done = step_result.done total_reward += reward rewards.append(reward) print(f"[STEP] step={step_count} action={json.dumps(action_data)} reward={reward:.2f} done={str(done).lower()} error=null") if done: break except Exception as e: print(f"[STEP] step={step_count} action=error reward=0.00 done=false error={str(e)}") break success = done and total_reward > 0.5 rewards_str = ",".join([f"{r:.2f}" for r in rewards]) if rewards else "" print(f"[END] success={str(success).lower()} steps={step_count} score={total_reward:.2f} rewards={rewards_str}") except Exception as e: print(f"[ERROR] Inference failed: {e}") sys.exit(1) def create_prompt(task_info: Dict[str, Any], observation, step: int) -> str: """Create a prompt for the model based on current state.""" task_desc = task_info["description"] service_type = task_info.get("service_type", "") difficulty = task_info.get("expected_difficulty", "") obs_dict = observation.model_dump() if hasattr(observation, 'model_dump') else {} prompt = f"""You are an AI assistant helping users with Indian government service applications. TASK: {task_desc} SERVICE TYPE: {service_type} DIFFICULTY: {difficulty} CURRENT STEP: {step} CURRENT STATE: - Stage: {obs_dict.get('current_stage', 'unknown')} - Service: {obs_dict.get('service_type', 'none')} - Message: {obs_dict.get('message', '')} REQUIRED DOCUMENTS: """ req_docs = obs_dict.get('required_documents', []) if req_docs: for doc in req_docs: prompt += f"- {doc.get('type', '')}: {doc.get('description', '')}\n" else: prompt += "None specified yet\n" submitted_docs = obs_dict.get('submitted_documents', []) if submitted_docs: prompt += "\nSUBMITTED DOCUMENTS:\n" for doc in submitted_docs: prompt += f"- {doc.get('type', '')}: {doc.get('details', '')}\n" validation_results = obs_dict.get('validation_results') if validation_results: prompt += f"\nVALIDATION RESULTS:\n" prompt += f"- Complete: {validation_results.get('is_complete', False)}\n" prompt += f"- Valid: {validation_results.get('is_valid', False)}\n" missing = validation_results.get('missing_documents', []) if missing: prompt += f"- Missing: {', '.join(missing)}\n" invalid = validation_results.get('invalid_documents', []) if invalid: prompt += f"- Invalid: {len(invalid)} documents have issues\n" corrections = obs_dict.get('correction_suggestions', []) if corrections: prompt += "\nCORRECTION SUGGESTIONS:\n" for corr in corrections: prompt += f"- {corr.get('suggested_action', '')}\n" prompt += """ Respond with valid JSON action_types: 1. select_service - provide service_type 2. list_required_documents - no extra fields 3. validate_documents - provide documents array 4. suggest_corrections - no extra fields 5. submit_application - no extra fields Example: {"action_type": "select_service", "service_type": "passport_new"} """ return prompt def parse_action(action_text: str) -> Dict[str, Any]: """Parse the model's response into an action.""" try: action_data = json.loads(action_text) return action_data except json.JSONDecodeError: action_data = {"message": action_text} text_lower = action_text.lower() if "select_service" in text_lower: action_data["action_type"] = "select_service" if "passport" in text_lower: action_data["service_type"] = "passport_new" elif "list_required" in text_lower or "required_documents" in text_lower: action_data["action_type"] = "list_required_documents" elif "validate" in text_lower: action_data["action_type"] = "validate_documents" elif "suggest" in text_lower or "correction" in text_lower: action_data["action_type"] = "suggest_corrections" elif "submit" in text_lower: action_data["action_type"] = "submit_application" return action_data def main(): """Main entry point.""" if len(sys.argv) != 2: print("Usage: python inference.py ") sys.exit(1) task_id = sys.argv[1] run_inference(task_id) if __name__ == "__main__": main()