Spaces:
Sleeping
Sleeping
Alexander Watson
commited on
Commit
•
476f41e
1
Parent(s):
f402348
fix logging bug, download logs and data
Browse files
app.py
CHANGED
@@ -1,6 +1,9 @@
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import json
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import logging
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import time
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from io import StringIO
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import pandas as pd
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@@ -8,8 +11,8 @@ import requests
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import streamlit as st
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from datasets import load_dataset
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from gretel_client import Gretel
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# Create a StringIO buffer to capture the logging output
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log_buffer = StringIO()
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@@ -421,9 +424,19 @@ def main():
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with col2:
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stop_button = st.button("🛑 Stop")
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if start_button:
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-
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-
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progress_bar = st.progress(0)
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tab1, tab2 = st.tabs(["Augmented Data", "Logs"])
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with tab1:
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@@ -433,20 +446,23 @@ def main():
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)
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with tab2:
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log_container = st.empty()
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logs = []
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max_log_lines = 50
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def custom_log_handler(msg):
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logs
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logs = logs[-max_log_lines:]
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log_text = "\n".join(logs)
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log_container.text(log_text)
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handler
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logger = logging.getLogger("navigator_helpers")
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logger.addHandler(handler)
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config = DataAugmentationConfig(
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input_fields=selected_fields,
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output_instruction_field=output_instruction_field,
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@@ -463,7 +479,6 @@ def main():
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instruction_format_prompt=instruction_format_prompt,
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response_format_prompt=response_format_prompt,
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)
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augmented_data = []
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start_time = time.time()
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with st.spinner("Generating synthetic data..."):
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for index in range(num_records):
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@@ -476,10 +491,12 @@ def main():
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verbose=True,
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)
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new_df = augmenter.augment()
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augmented_data.append(new_df)
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augmented_data_placeholder.subheader("
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augmented_data_placeholder.dataframe(
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pd.concat(
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)
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progress = (index + 1) / num_records
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progress_bar.progress(progress)
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@@ -500,13 +517,71 @@ def main():
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time.sleep(0.1)
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logger.removeHandler(handler)
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st.success("Data augmentation completed!")
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if stop_button:
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st.warning("Augmentation stopped by the user.")
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st.stop()
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-
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-
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if __name__ == "__main__":
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import json
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import logging
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import os
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import tempfile
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import time
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import zipfile
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from io import StringIO
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import pandas as pd
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import streamlit as st
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from datasets import load_dataset
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from gretel_client import Gretel
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from navigator_helpers import (DataAugmentationConfig, DataAugmenter,
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StreamlitLogHandler)
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# Create a StringIO buffer to capture the logging output
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log_buffer = StringIO()
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with col2:
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stop_button = st.button("🛑 Stop")
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if "logs" not in st.session_state:
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st.session_state.logs = []
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if "augmented_data" not in st.session_state:
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st.session_state.augmented_data = []
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if start_button:
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# Clear the augmented data and logs before starting a new generation
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st.session_state.augmented_data = []
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st.session_state.logs = []
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with st.expander("Synthetic Data", expanded=True):
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st.subheader("Synthetic Data Generation")
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progress_bar = st.progress(0)
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tab1, tab2 = st.tabs(["Augmented Data", "Logs"])
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with tab1:
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)
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with tab2:
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log_container = st.empty()
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max_log_lines = 50
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def custom_log_handler(msg):
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st.session_state.logs.append(msg)
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displayed_logs = st.session_state.logs[-max_log_lines:]
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log_text = "\n".join(displayed_logs)
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log_container.text(log_text)
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# Remove the previous log handler if it exists
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logger = logging.getLogger("navigator_helpers")
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for handler in logger.handlers:
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if isinstance(handler, StreamlitLogHandler):
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logger.removeHandler(handler)
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handler = StreamlitLogHandler(custom_log_handler)
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logger.addHandler(handler)
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config = DataAugmentationConfig(
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input_fields=selected_fields,
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output_instruction_field=output_instruction_field,
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instruction_format_prompt=instruction_format_prompt,
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response_format_prompt=response_format_prompt,
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)
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start_time = time.time()
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with st.spinner("Generating synthetic data..."):
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for index in range(num_records):
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verbose=True,
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)
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new_df = augmenter.augment()
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st.session_state.augmented_data.append(new_df)
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augmented_data_placeholder.subheader("Synthetic Data")
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augmented_data_placeholder.dataframe(
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pd.concat(
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st.session_state.augmented_data, ignore_index=True
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)
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)
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progress = (index + 1) / num_records
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progress_bar.progress(progress)
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time.sleep(0.1)
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logger.removeHandler(handler)
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st.success("Data augmentation completed!")
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st.stop()
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if stop_button:
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st.warning("Augmentation stopped by the user.")
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# Get the complete logs from the session state
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complete_logs = st.session_state.logs
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# Convert complete logs to JSONL format
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log_jsonl = "\n".join([json.dumps({"log": log}) for log in complete_logs])
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# Convert augmented data to JSONL format if it exists
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if st.session_state.augmented_data:
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augmented_df = pd.concat(
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st.session_state.augmented_data, ignore_index=True
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)
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if not augmented_df.empty:
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augmented_data_jsonl = "\n".join(
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[
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json.dumps(row.to_dict())
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for _, row in augmented_df.iterrows()
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]
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)
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else:
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augmented_data_jsonl = None
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else:
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augmented_data_jsonl = None
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# Create a temporary directory to store the files
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with tempfile.TemporaryDirectory() as temp_dir:
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# Write the complete logs to a file
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log_file_path = os.path.join(temp_dir, "complete_logs.jsonl")
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with open(log_file_path, "w") as log_file:
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log_file.write(log_jsonl)
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# Write the augmented data to a file if it exists
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if augmented_data_jsonl:
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augmented_data_file_path = os.path.join(
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temp_dir, "augmented_data.jsonl"
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)
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with open(augmented_data_file_path, "w") as augmented_data_file:
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augmented_data_file.write(augmented_data_jsonl)
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# Create a ZIP file containing the logs and augmented data
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zip_file_path = os.path.join(temp_dir, "augmentation_results.zip")
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with zipfile.ZipFile(zip_file_path, "w") as zip_file:
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zip_file.write(log_file_path, "complete_logs.jsonl")
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if augmented_data_jsonl:
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zip_file.write(augmented_data_file_path, "augmented_data.jsonl")
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# Download the ZIP file
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with open(zip_file_path, "rb") as zip_file:
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st.download_button(
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label="Download Synthetic Data and Logs",
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data=zip_file.read(),
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file_name="augmentation_results.zip",
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mime="application/zip",
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)
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st.stop()
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else:
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st.info(
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"Please upload a file or select a dataset from Hugging Face to proceed."
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)
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if __name__ == "__main__":
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