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0b35d70 5e63253 0b35d70 806346b 8b2c48f 0b35d70 bbd3c3e | 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 | # utils.py
import logging
import streamlit as st
import os
from dotenv import load_dotenv
# --- Configuration ---
def load_config():
"""Loads API keys and other config from .env file."""
load_dotenv()
config = {
"openrouter_api_key": os.getenv("OPENROUTER_API_KEY"),
"openrouter_base_url": "https://openrouter.ai/api/v1",
"openrouter_chat_model": "deepseek/deepseek-r1-0528-qwen3-8b:free",
# Removed openrouter_embedding_model as we'll use HF
"huggingface_api_token": os.getenv("HUGGINGFACEHUB_API_TOKEN"), # Load HF token
"huggingface_embedding_model": "sentence-transformers/all-MiniLM-L6-v2", # Define HF model
"use_local_embeddings": True, # Flag to indicate we're using local embeddings
"embedding_cache_dir": "/tmp/hf_models" # Cache directory for local models
}
if not config["openrouter_api_key"]:
st.error("🚨 OpenRouter API key not found! Please set OPENROUTER_API_KEY in the .env file.")
st.stop()
if not config["huggingface_api_token"]:
st.warning(
"⚠️ Hugging Face API token (HUGGINGFACEHUB_API_TOKEN) not found in .env file. "
"Inference API Embeddings might fail if the model requires authentication or hits rate limits. "
"Consider using local embeddings (HuggingFaceEmbeddings) if issues persist."
)
# Depending on the model, the API might work without a key for public models under rate limits
# Set it to None if not found, the HuggingFaceInferenceAPIEmbeddings class might handle it.
config["huggingface_api_token"] = None # Allow proceeding without key, but with warning
return config
# --- Logging Setup --- (Keep the rest of the logging code as is)
class StreamlitLogHandler(logging.Handler):
def __init__(self, container):
super().__init__()
self.container = container
self.log_records = []
def emit(self, record):
try:
msg = self.format(record)
self.log_records.append(msg)
except Exception:
self.handleError(record)
def get_logs(self): return "\n".join(self.log_records)
def clear_logs(self): self.log_records = []
logger = logging.getLogger("RAG_APP")
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
if not logger.handlers: pass
def setup_streamlit_logging(log_container):
for handler in logger.handlers[:]:
if isinstance(handler, StreamlitLogHandler): logger.removeHandler(handler)
st_handler = StreamlitLogHandler(log_container)
st_handler.setFormatter(formatter)
logger.addHandler(st_handler)
return st_handler
# --- Helper Functions ---
def get_manual_text(filepath="data/ace900_takeoff_manual.txt"):
try:
with open(filepath, 'r', encoding='utf-8') as f: return f.read()
except FileNotFoundError:
st.error(f"🚨 Manual file not found at: {filepath}")
st.stop()
except Exception as e:
st.error(f"🚨 Error reading manual file: {e}")
st.stop() |