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import os
from datetime import datetime
import json
from huggingface_hub import HfApi
import gradio as gr
import csv
def serialize_docs(docs:list)->list:
new_docs = []
for doc in docs:
new_doc = {}
new_doc["page_content"] = doc.page_content
new_doc["metadata"] = doc.metadata
new_docs.append(new_doc)
return new_docs
## AZURE LOGGING - DEPRECATED
# def log_on_azure(file, logs, share_client):
# """Log data to Azure Blob Storage.
# Args:
# file (str): Name of the file to store logs
# logs (dict): Log data to store
# share_client: Azure share client instance
# """
# logs = json.dumps(logs)
# file_client = share_client.get_file_client(file)
# file_client.upload_file(logs)
# def log_interaction_to_azure(history, output_query, sources, docs, share_client, user_id):
# """Log chat interaction to Azure and Hugging Face.
# Args:
# history (list): Chat message history
# output_query (str): Processed query
# sources (list): Knowledge base sources used
# docs (list): Retrieved documents
# share_client: Azure share client instance
# user_id (str): User identifier
# """
# try:
# # Log interaction to Azure if not in local environment
# if os.getenv("GRADIO_ENV") != "local":
# timestamp = str(datetime.now().timestamp())
# prompt = history[1]["content"]
# logs = {
# "user_id": str(user_id),
# "prompt": prompt,
# "query": prompt,
# "question": output_query,
# "sources": sources,
# "docs": serialize_docs(docs),
# "answer": history[-1].content,
# "time": timestamp,
# }
# # Log to Azure
# log_on_azure(f"{timestamp}.json", logs, share_client)
# except Exception as e:
# print(f"Error logging on Azure Blob Storage: {e}")
# error_msg = f"ClimateQ&A Error: {str(e)[:100]} - The error has been noted, try another question and if the error remains, you can contact us :)"
# raise gr.Error(error_msg)
# def log_drias_interaction_to_azure(query, sql_query, data, share_client, user_id):
# """Log Drias data interaction to Azure and Hugging Face.
# Args:
# query (str): User query
# sql_query (str): SQL query used
# data: Retrieved data
# share_client: Azure share client instance
# user_id (str): User identifier
# """
# try:
# # Log interaction to Azure if not in local environment
# if os.getenv("GRADIO_ENV") != "local":
# timestamp = str(datetime.now().timestamp())
# logs = {
# "user_id": str(user_id),
# "query": query,
# "sql_query": sql_query,
# "time": timestamp,
# }
# log_on_azure(f"drias_{timestamp}.json", logs, share_client)
# print(f"Logged Drias interaction to Azure Blob Storage: {logs}")
# else:
# print("share_client or user_id is None, or GRADIO_ENV is local")
# except Exception as e:
# print(f"Error logging Drias interaction on Azure Blob Storage: {e}")
# error_msg = f"Drias Error: {str(e)[:100]} - The error has been noted, try another question and if the error remains, you can contact us :)"
# raise gr.Error(error_msg)
## HUGGING FACE LOGGING
def log_on_huggingface(log_filename, logs):
"""Log data to Hugging Face dataset repository.
Args:
log_filename (str): Name of the file to store logs
logs (dict): Log data to store
"""
try:
# Get Hugging Face token from environment
hf_token = os.getenv("HF_LOGS_TOKEN")
if not hf_token:
print("HF_LOGS_TOKEN not found in environment variables")
return
# Get repository name from environment or use default
repo_id = os.getenv("HF_DATASET_REPO", "timeki/climateqa_logs")
# Initialize HfApi
api = HfApi(token=hf_token)
# Add timestamp to the log data
logs["timestamp"] = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
# Convert logs to JSON string
logs_json = json.dumps(logs)
# Upload directly from memory
api.upload_file(
path_or_fileobj=logs_json.encode('utf-8'),
path_in_repo=log_filename,
repo_id=repo_id,
repo_type="dataset"
)
except Exception as e:
print(f"Error logging to Hugging Face: {e}")
def log_interaction_to_huggingface(history, output_query, sources, docs, share_client, user_id):
"""Log chat interaction to Hugging Face.
Args:
history (list): Chat message history
output_query (str): Processed query
sources (list): Knowledge base sources used
docs (list): Retrieved documents
share_client: Azure share client instance (unused in this function)
user_id (str): User identifier
"""
try:
# Log interaction if not in local environment
if os.getenv("GRADIO_ENV") != "local":
timestamp = str(datetime.now().timestamp())
prompt = history[1]["content"]
logs = {
"user_id": str(user_id),
"prompt": prompt,
"query": prompt,
"question": output_query,
"sources": sources,
"docs": serialize_docs(docs),
"answer": history[-1].content,
"time": timestamp,
}
# Log to Hugging Face
log_on_huggingface(f"chat/{timestamp}.json", logs)
except Exception as e:
print(f"Error logging to Hugging Face: {e}")
error_msg = f"ClimateQ&A Error: {str(e)[:100]} - The error has been noted, try another question and if the error remains, you can contact us :)"
raise gr.Error(error_msg)
def log_drias_interaction_to_huggingface(query, sql_query, user_id):
"""Log Drias data interaction to Hugging Face.
Args:
query (str): User query
sql_query (str): SQL query used
data: Retrieved data
user_id (str): User identifier
"""
try:
if os.getenv("GRADIO_ENV") != "local":
timestamp = str(datetime.now().timestamp())
logs = {
"user_id": str(user_id),
"query": query,
"sql_query": sql_query,
"time": timestamp,
}
log_on_huggingface(f"drias/drias_{timestamp}.json", logs)
print(f"Logged Drias interaction to Hugging Face: {logs}")
else:
print("share_client or user_id is None, or GRADIO_ENV is local")
except Exception as e:
print(f"Error logging Drias interaction to Hugging Face: {e}")
error_msg = f"Drias Error: {str(e)[:100]} - The error has been noted, try another question and if the error remains, you can contact us :)"
raise gr.Error(error_msg)
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