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| # streamlit_app.py | |
| import streamlit as st | |
| import os | |
| import pandas as pd | |
| from syngen.ml.worker import Worker | |
| import queue | |
| from loguru import logger | |
| import threading | |
| # Use streamlit to run the application | |
| if __name__ == "__main__": | |
| st.title("HuggingFace Streamlit App with Syngen") | |
| st.write("Upload CSV files, define relationships, and train your model.") | |
| # Create a queue for the logs | |
| log_queue = queue.Queue() | |
| def log_sink(message): | |
| log_queue.put(message.record["message"]) | |
| logger.add(log_sink) | |
| # Path to store the uploaded files | |
| UPLOAD_DIRECTORY = "uploaded_files" | |
| if not os.path.exists(UPLOAD_DIRECTORY): | |
| os.makedirs(UPLOAD_DIRECTORY) | |
| # Define file uploader | |
| uploaded_files = st.file_uploader( | |
| "Upload CSV files", type="csv", accept_multiple_files=True) | |
| dataframes = {} | |
| if uploaded_files: | |
| for uploaded_file in uploaded_files: | |
| # Save file to local directory | |
| file_path = os.path.join(UPLOAD_DIRECTORY, uploaded_file.name) | |
| with open(file_path, 'wb') as f: | |
| f.write(uploaded_file.getvalue()) | |
| df = pd.read_csv(file_path) | |
| dataframes[uploaded_file.name] = df | |
| st.write(f"Preview of {uploaded_file.name}:", df.head()) | |
| # YAML Configuration Editor | |
| st.subheader('YAML Configuration') | |
| yaml_config = st.text_area( | |
| "Define the relationships between the CSV files:", "") | |
| if yaml_config: | |
| st.code(yaml_config, language="yaml") | |
| def train_model(): | |
| logger.info("Starting model training...") | |
| for uploaded_file in uploaded_files: | |
| file_path = os.path.join(UPLOAD_DIRECTORY, uploaded_file.name) | |
| settings = { | |
| "source": file_path, | |
| "epochs": 2, | |
| "drop_null": False, | |
| "print_report": False, | |
| "row_limit": None, | |
| "batch_size": 32 | |
| } | |
| worker = Worker( | |
| table_name=uploaded_file.name, | |
| settings=settings, | |
| metadata_path=None, | |
| log_level='DEBUG', | |
| type="train" | |
| ) | |
| worker.launch_train() | |
| logger.info("Model training completed.") | |
| # Training Button | |
| if st.button('Start Model Training'): | |
| if uploaded_files and yaml_config: | |
| # 1. Save YAML configuration to a file | |
| with open("config.yaml", "w") as f: | |
| f.write(yaml_config) | |
| log_display = st.empty() # create an empty slot to display logs | |
| training_thread = threading.Thread(target=train_model) | |
| training_thread.start() | |
| st.info("Training started. Please wait...") | |
| while not log_queue.empty(): | |
| log_display.text(log_queue.get()) | |
| # 3. Save the model binaries | |
| # model.save("model.bin") | |
| # 4. Display links to download files | |
| st.markdown( | |
| "Download [Generated File](./generated_file.csv) and [Model Binaries](./model.bin).") | |
| else: | |
| st.warning( | |
| "Please upload CSV files and provide a valid YAML configuration.") | |