""" Streamlit UI for fine-tuning code generation models. """ import streamlit as st import pandas as pd import numpy as np import os import time from datetime import datetime import torch import plotly.express as px import plotly.graph_objects as go from pathlib import Path import json import uuid import threading from transformers import TrainingArguments from datasets import Dataset from components.fine_tuning.model_interface import ( load_model_and_tokenizer, preprocess_code_dataset, setup_trainer, generate_code_comment, generate_code_from_comment, save_training_config, load_training_config ) # Initialize training state if 'training_run_id' not in st.session_state: st.session_state.training_run_id = None if 'training_status' not in st.session_state: st.session_state.training_status = "idle" # idle, running, completed, failed if 'training_progress' not in st.session_state: st.session_state.training_progress = 0.0 if 'trained_model' not in st.session_state: st.session_state.trained_model = None if 'trained_tokenizer' not in st.session_state: st.session_state.trained_tokenizer = None if 'training_logs' not in st.session_state: st.session_state.training_logs = [] if 'fine_tuning_dataset' not in st.session_state: st.session_state.fine_tuning_dataset = None # Directory for saving models MODELS_DIR = Path("./fine_tuned_models") MODELS_DIR.mkdir(exist_ok=True) # Set for background training thread training_thread = None def render_dataset_preparation(): """ Render the dataset preparation interface. """ st.markdown("### Dataset Preparation") # Dataset input options dataset_source = st.radio( "Choose dataset source", ["Upload CSV", "Manual Input", "Use Current Dataset"], help="Select how you want to provide your fine-tuning dataset" ) if dataset_source == "Upload CSV": uploaded_file = st.file_uploader( "Upload fine-tuning dataset (CSV)", type=["csv"], help="CSV file with 'input' and 'target' columns" ) if uploaded_file is not None: try: df = pd.read_csv(uploaded_file) # Check if required columns exist if "input" not in df.columns or "target" not in df.columns: st.error("CSV must contain 'input' and 'target' columns.") return # Preview dataset st.markdown("### Dataset Preview") st.dataframe(df.head(), use_container_width=True) # Dataset statistics st.markdown("### Dataset Statistics") col1, col2 = st.columns(2) with col1: st.metric("Number of examples", len(df)) with col2: st.metric("Average input length", df["input"].astype(str).str.len().mean().round(1)) # Save dataset if st.button("Use this dataset"): st.session_state.fine_tuning_dataset = df st.success(f"Dataset with {len(df)} examples loaded successfully!") except Exception as e: st.error(f"Error loading CSV: {str(e)}") elif dataset_source == "Manual Input": st.markdown(""" Enter pairs of inputs and targets for fine-tuning. For code-to-comment tasks, the input is code and the target is a comment. For comment-to-code tasks, the input is a comment and the target is code. """) # Container for input fields examples_container = st.container() # Default number of example fields if "num_examples" not in st.session_state: st.session_state.num_examples = 3 # Add more examples button if st.button("Add another example"): st.session_state.num_examples += 1 # Input fields for examples inputs = [] targets = [] with examples_container: for i in range(st.session_state.num_examples): st.markdown(f"### Example {i+1}") col1, col2 = st.columns(2) with col1: input_text = st.text_area(f"Input {i+1}", key=f"input_{i}", height=150) inputs.append(input_text) with col2: target_text = st.text_area(f"Target {i+1}", key=f"target_{i}", height=150) targets.append(target_text) # Create dataset from manual input if st.button("Create Dataset from Examples"): # Filter out empty examples valid_examples = [(inp, tgt) for inp, tgt in zip(inputs, targets) if inp.strip() and tgt.strip()] if valid_examples: df = pd.DataFrame(valid_examples, columns=["input", "target"]) st.session_state.fine_tuning_dataset = df # Preview dataset st.markdown("### Dataset Preview") st.dataframe(df, use_container_width=True) st.success(f"Dataset with {len(df)} examples created successfully!") else: st.warning("No valid examples found. Please enter at least one input-target pair.") elif dataset_source == "Use Current Dataset": if st.session_state.dataset is None: st.warning("No dataset is currently loaded. Please upload or select a dataset first.") else: st.markdown("### Current Dataset") st.dataframe(st.session_state.dataset.head(), use_container_width=True) # Column selection col1, col2 = st.columns(2) with col1: input_col = st.selectbox("Select column for inputs", st.session_state.dataset.columns) with col2: target_col = st.selectbox("Select column for targets", st.session_state.dataset.columns) # Create fine-tuning dataset if st.button("Create Fine-Tuning Dataset"): df = st.session_state.dataset[[input_col, target_col]].copy() df.columns = ["input", "target"] # Verify data types and convert to string if necessary df["input"] = df["input"].astype(str) df["target"] = df["target"].astype(str) # Preview st.dataframe(df.head(), use_container_width=True) # Store dataset st.session_state.fine_tuning_dataset = df st.success(f"Fine-tuning dataset with {len(df)} examples created successfully!") def render_model_training(): """ Render the model training interface. """ st.markdown("### Model Training") # Check if dataset is available if st.session_state.fine_tuning_dataset is None: st.warning("Please prepare a dataset in the 'Dataset Preparation' tab first.") return # Model selection model_options = { "Salesforce/codet5-small": "CodeT5 Small (60M params)", "Salesforce/codet5-base": "CodeT5 Base (220M params)", "Salesforce/codet5-large": "CodeT5 Large (770M params)", "microsoft/codebert-base": "CodeBERT Base (125M params)", "facebook/bart-base": "BART Base (140M params)" } model_name = st.selectbox( "Select pre-trained model", list(model_options.keys()), format_func=lambda x: model_options[x], help="Select the base model for fine-tuning" ) # Task type task_type = st.selectbox( "Select task type", ["Code to Comment", "Comment to Code"], help="Choose the direction of your task" ) # Task prefix if task_type == "Code to Comment": task_prefix = "translate code to comment: " else: task_prefix = "translate comment to code: " # Hyperparameters st.markdown("### Training Hyperparameters") col1, col2 = st.columns(2) with col1: learning_rate = st.select_slider( "Learning Rate", options=[1e-6, 2e-6, 5e-6, 1e-5, 2e-5, 5e-5, 1e-4], value=5e-5, help="Step size for optimizer updates" ) epochs = st.slider( "Epochs", min_value=1, max_value=20, value=3, help="Number of complete passes through the dataset" ) with col2: batch_size = st.select_slider( "Batch Size", options=[1, 2, 4, 8, 16, 32], value=8, help="Number of examples processed in each training step" ) max_input_length = st.slider( "Max Input Length (tokens)", min_value=64, max_value=512, value=256, help="Maximum length of input sequences" ) # Advanced options with st.expander("Advanced Options"): col1, col2 = st.columns(2) with col1: weight_decay = st.select_slider( "Weight Decay", options=[0.0, 0.01, 0.05, 0.1], value=0.01, help="L2 regularization" ) warmup_steps = st.slider( "Warmup Steps", min_value=0, max_value=1000, value=100, help="Steps for learning rate warmup" ) with col2: max_target_length = st.slider( "Max Target Length (tokens)", min_value=64, max_value=512, value=256, help="Maximum length of target sequences" ) gradient_accumulation = st.slider( "Gradient Accumulation Steps", min_value=1, max_value=16, value=1, help="Number of steps to accumulate gradients" ) # Model output configuration st.markdown("### Model Output Configuration") model_name_custom = st.text_input( "Custom model name", value=f"{model_name.split('/')[-1]}-finetuned-{task_type.lower().replace(' ', '-')}", help="Name for your fine-tuned model" ) # Training controls st.markdown("### Training Controls") # Check if training is in progress if st.session_state.training_status == "running": # Display progress st.progress(st.session_state.training_progress) # Show logs if st.session_state.training_logs: st.markdown("### Training Logs") log_text = "\n".join(st.session_state.training_logs[-10:]) # Show last 10 logs st.text_area("Latest logs", log_text, height=200, disabled=True) # Stop button if st.button("Stop Training"): # Logic to stop training thread st.session_state.training_status = "stopping" st.warning("Stopping training after current epoch completes...") elif st.session_state.training_status == "completed": st.success(f"Training completed! Model saved as: {model_name_custom}") # Show metrics if available if "training_metrics" in st.session_state: st.markdown("### Training Metrics") metrics_df = pd.DataFrame(st.session_state.training_metrics) st.line_chart(metrics_df) # Reset button if st.button("Start New Training"): st.session_state.training_status = "idle" st.session_state.training_progress = 0.0 st.session_state.training_logs = [] st.experimental_rerun() else: # idle or failed # If previously failed, show error if st.session_state.training_status == "failed": st.error("Previous training failed. See logs for details.") if st.session_state.training_logs: st.text_area("Error logs", "\n".join(st.session_state.training_logs[-5:]), height=100, disabled=True) # Start training button if st.button("Start Training"): # Validate dataset if len(st.session_state.fine_tuning_dataset) < 5: st.warning("Dataset is very small. Consider adding more examples for better results.") # Set up training configuration training_config = { "model_name": model_name, "task_type": task_type, "task_prefix": task_prefix, "learning_rate": learning_rate, "epochs": epochs, "batch_size": batch_size, "max_input_length": max_input_length, "max_target_length": max_target_length, "weight_decay": weight_decay, "warmup_steps": warmup_steps, "gradient_accumulation": gradient_accumulation, "output_model_name": model_name_custom, "dataset_size": len(st.session_state.fine_tuning_dataset) } # Update session state st.session_state.training_status = "running" st.session_state.training_progress = 0.0 st.session_state.training_logs = ["Training initialized..."] st.session_state.training_run_id = str(uuid.uuid4()) # TODO: Start actual training process using transformers st.info("Training would start here with the Hugging Face transformers library") # For now, just simulate training progress st.session_state.training_progress = 0.1 st.session_state.training_logs.append("Loaded model and tokenizer") st.session_state.training_logs.append("Preprocessing dataset...") # Rerun to update UI with progress st.experimental_rerun() def render_model_testing(): """ Render the model testing interface. """ st.markdown("### Test & Use Model") # Check if a model is trained/available if st.session_state.trained_model is None and st.session_state.training_status != "completed": # Look for saved models saved_models = list(MODELS_DIR.glob("*/")) if not saved_models: st.warning("No trained models available. Please train a model first.") return # Let user select a saved model model_options = [model.name for model in saved_models] selected_model = st.selectbox("Select a saved model", model_options) if st.button("Load Selected Model"): st.info(f"Loading model {selected_model}...") # TODO: Load model logic st.session_state.trained_model = "loaded" # Placeholder st.session_state.trained_tokenizer = "loaded" # Placeholder st.success("Model loaded successfully!") else: # Model is available for testing model_type = "Code to Comment" if "code-to-comment" in st.session_state.get("model_name", "") else "Comment to Code" st.markdown(f"### Testing {model_type} Generation") if model_type == "Code to Comment": input_text = st.text_area( "Enter code snippet", height=200, help="Enter a code snippet to generate a comment" ) if st.button("Generate Comment"): if input_text: with st.spinner("Generating comment..."): # TODO: Replace with actual model inference result = f"/* This code {input_text.split()[0:3]} ... */" st.markdown("### Generated Comment") st.code(result) else: st.warning("Please enter a code snippet.") else: # Comment to Code input_text = st.text_area( "Enter comment/description", height=150, help="Enter a description to generate code" ) language = st.selectbox( "Programming language", ["Python", "JavaScript", "Java", "C++", "Go"] ) if st.button("Generate Code"): if input_text: with st.spinner("Generating code..."): # TODO: Replace with actual model inference result = f"def example_function():\n # {input_text}\n pass" st.markdown("### Generated Code") st.code(result, language=language.lower()) else: st.warning("Please enter a comment or description.") # Batch testing with st.expander("Batch Testing"): st.markdown("Upload a CSV file with test cases to evaluate your model.") test_file = st.file_uploader( "Upload test cases (CSV)", type=["csv"], help="CSV file with 'input' and 'expected' columns" ) if test_file is not None: try: test_df = pd.read_csv(test_file) st.dataframe(test_df.head(), use_container_width=True) if st.button("Run Batch Test"): with st.spinner("Running tests..."): # TODO: Actual batch inference st.success("Batch testing completed!") # Dummy results results = pd.DataFrame({ "input": test_df["input"], "expected": test_df.get("expected", [""] * len(test_df)), "generated": ["Sample output " + str(i) for i in range(len(test_df))], "match_score": np.random.uniform(0.5, 1.0, len(test_df)) }) st.dataframe(results, use_container_width=True) # Metrics st.markdown("### Evaluation Metrics") col1, col2 = st.columns(2) with col1: st.metric("Average Match Score", f"{results['match_score'].mean():.2f}") with col2: st.metric("Tests Passed", f"{sum(results['match_score'] > 0.8)}/{len(results)}") except Exception as e: st.error(f"Error loading test file: {str(e)}") def render_finetune_ui(): """ Render the fine-tuning UI for code generation models. """ st.markdown("

Fine-Tune Code Generation Model

", unsafe_allow_html=True) # Overview and instructions with st.expander("About Fine-Tuning", expanded=False): st.markdown(""" ## Fine-Tuning a Code Generation Model This interface allows you to fine-tune pre-trained code generation models from Hugging Face on your custom dataset to adapt them to your specific coding style or task. ### How to use: 1. **Prepare your dataset** - Upload a CSV file with 'input' and 'target' columns: - For code-to-comment: 'input' = code snippets, 'target' = corresponding comments - For comment-to-code: 'input' = comments, 'target' = corresponding code snippets 2. **Configure training** - Set hyperparameters like learning rate, batch size, and epochs 3. **Start fine-tuning** - Launch the training process and monitor progress 4. **Test your model** - Once training is complete, test your model on new inputs ### Tips for better results: - Use a consistent format for your code snippets and comments - Start with a small dataset (50-100 examples) to verify the process - Try different hyperparameters to find the best configuration """) # Main UI with tabs tab1, tab2, tab3 = st.tabs(["Dataset Preparation", "Model Training", "Test & Use Model"]) with tab1: render_dataset_preparation() with tab2: render_model_training() with tab3: render_model_testing()