attached_assets/Pasted-Below-is-a-design-proposal-for-a-Hugging-Face-based-system-that-lets-users-fine-tune-a-code-generati-1740904225626.txt

Below is a design proposal for a Hugging Face–based system that lets users fine-tune a code generation model via a simple Streamlit interface.

Overview:
	1.	Model & Library Setup:
      •   Use a pre-trained code generation model (e.g., CodeT5 or CodeT5-base) from Hugging Face.
      •   Leverage the Hugging Face Transformers and Datasets libraries together with the Hugging Face Trainer API to perform fine-tuning.
	2.	Streamlit Interface:
      •   Input Section: Users can upload a small dataset (e.g., a CSV file with code and target comments) or manually enter a few fine-tuning examples.
      •   Hyperparameter Controls: Sliders or input boxes for settings like learning rate, number of epochs, batch size, and maybe even a choice of optimizer.
      •   Execution Controls: Buttons to start fine-tuning and to monitor training progress (using, for example, real-time logging or a progress bar).
      •   Output Section: Display training metrics (loss curves, evaluation scores) and allow users to run inference on new prompts once fine-tuning completes.
	3.	Back-end Process:
      •   When the user initiates fine-tuning, the uploaded dataset is preprocessed (tokenization using the model’s tokenizer).
      •   A Trainer object is configured with the user-specified hyperparameters.
      •   Fine-tuning is launched (this can run in a background thread or via caching intermediate results).
      •   Once training is complete, the updated model can be saved to disk (or even directly loaded into the interface for inference).
	4.	Deployment & Reproducibility:
      •   The whole pipeline (data upload, preprocessing, training, evaluation, and inference) should be reproducible.
      •   Optionally, support saving the fine-tuned model and the training configuration to allow users to share their work.

Example Code Snippet (Simplified):

Below is a simplified version of what the Streamlit app might look like. (Note: In a production setup, you would want proper error handling and asynchronous processing.)

import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Trainer, TrainingArguments
from datasets import load_dataset, Dataset
import torch

# Title
st.title("Fine-Tune Code Generation Model with Hugging Face & Streamlit")

# Sidebar: Hyperparameters
st.sidebar.header("Training Hyperparameters")
learning_rate = st.sidebar.slider("Learning Rate", 1e-6, 5e-5, 2e-5, 1e-6)
epochs = st.sidebar.number_input("Epochs", 1, 10, 3)
batch_size = st.sidebar.number_input("Batch Size", 4, 32, 8)

# Upload your fine-tuning data: CSV file with columns "input" and "target"
uploaded_file = st.file_uploader("Upload your fine-tuning dataset (CSV)", type="csv")

if uploaded_file is not None:
    import pandas as pd
    df = pd.read_csv(uploaded_file)
    st.write("Dataset preview:", df.head())
    # Convert to Hugging Face Dataset
    dataset = Dataset.from_pandas(df)
else:
    st.info("Please upload a CSV dataset with columns 'input' and 'target'.")

# Model selection
model_name = st.selectbox("Choose a model", ["Salesforce/codet5-base"])

# Load model and tokenizer
@st.cache_resource(show_spinner=False)
def load_model_and_tokenizer(name):
    tokenizer = AutoTokenizer.from_pretrained(name)
    model = AutoModelForSeq2SeqLM.from_pretrained(name)
    return tokenizer, model

tokenizer, model = load_model_and_tokenizer(model_name)

# Preprocess function for tokenization
def preprocess_function(examples):
    inputs = [f"translate code to comment: {ex}" for ex in examples["input"]]
    model_inputs = tokenizer(inputs, max_length=128, truncation=True)
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(examples["target"], max_length=64, truncation=True)
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

if uploaded_file is not None:
    tokenized_dataset = dataset.map(preprocess_function, batched=True)
    
    # Setup training arguments
    training_args = TrainingArguments(
        output_dir="./results",
        num_train_epochs=epochs,
        per_device_train_batch_size=batch_size,
        learning_rate=learning_rate,
        logging_steps=10,
        logging_dir='./logs',
        report_to="none"
    )
    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_dataset,
    )
    
    if st.button("Start Fine-Tuning"):
        st.info("Fine-tuning started... This might take a while.")
        trainer.train()
        st.success("Fine-tuning complete!")
        
        # Save the model to disk (or load it for inference)
        model.save_pretrained("fine_tuned_model")
        tokenizer.save_pretrained("fine_tuned_model")
        st.write("Model saved to 'fine_tuned_model'.")
        
        # Option to run inference on new inputs
        user_input = st.text_area("Enter a new code prompt for inference:")
        if user_input:
            inputs = tokenizer(f"translate code to comment: {user_input}", return_tensors="pt", truncation=True)
            outputs = model.generate(**inputs, max_length=64)
            generated_comment = tokenizer.decode(outputs[0], skip_special_tokens=True)
            st.write("Generated comment:", generated_comment)

Key Points:
   •   User Interaction: The interface lets users set hyperparameters, upload datasets, and start fine-tuning.
   •   Model Integration: It uses Hugging Face’s pre-trained CodeT5 model and tokenizer, then fine-tunes on user-provided examples.
   •   Reproducibility: The pipeline includes caching, dataset conversion, and saving the final model.
   •   Extensibility: You can later add more options (e.g., additional hyperparameters, evaluation metrics, visualization of training progress).

This design should give you a robust, end-to-end solution to let users easily fine-tune a code generation model through a Streamlit interface. Would you like further details on any component of the design?