File size: 8,694 Bytes
8516bb3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 | #!/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 <task_id>")
sys.exit(1)
task_id = sys.argv[1]
run_inference(task_id)
if __name__ == "__main__":
main() |