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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
# Define model loading function
def load_model(model_name):
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
return tokenizer, model
# Load selected models
models = {
"bigcode/python-stack-v1-functions-filtered-sc2-subset": "bigcode/python-stack-v1-functions-filtered-sc2-subset",
"bigcode/python-stack-v1-functions-filtered-sc2": "bigcode/python-stack-v1-functions-filtered-sc2",
"muellerzr/python-stack-v1-functions-filtered-llama-3-8B": "muellerzr/python-stack-v1-functions-filtered-llama-3-8B",
"TheBloke/Python-Code-13B-GGUF": "TheBloke/Python-Code-13B-GGUF",
"replit/replit-code-v1_5-3b": "replit/replit-code-v1_5-3b",
"neulab/codebert-python": "neulab/codebert-python"
}
# Load selected datasets
datasets = {
"kye/all-huggingface-python-code": "kye/all-huggingface-python-code",
"ajibawa-2023/Python-Code-23k-ShareGPT": "ajibawa-2023/Python-Code-23k-ShareGPT",
"suvadityamuk/huggingface-transformers-code-dataset": "suvadityamuk/huggingface-transformers-code-dataset"
}
# Define the function for code generation
def generate_code(prompt, model_name, dataset_name, temperature, max_length):
tokenizer, model = load_model(models[model_name])
# Load dataset (for reference, not directly used)
dataset = load_dataset(datasets[dataset_name], split="train")
# Tokenize input prompt
inputs = tokenizer(prompt, return_tensors="pt")
# Generate output
output_ids = model.generate(**inputs, temperature=temperature, max_length=max_length)
generated_code = tokenizer.decode(output_ids[0], skip_special_tokens=True)
return generated_code
# Create Gradio Interface
iface = gr.Interface(
fn=generate_code,
inputs=[
gr.Textbox(label="Prompt"),
gr.Dropdown(label="Model", choices=list(models.keys())),
gr.Dropdown(label="Dataset", choices=list(datasets.keys())),
gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, value=0.5),
gr.Slider(label="Max Length", minimum=10, maximum=1000, value=200)
],
outputs="text",
title="AI Code Generator with Hugging Face Models",
description="Select a model and dataset, input a prompt, and generate Python code using AI models."
)
# Launch the Gradio App
iface.launch()
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