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Update app.py
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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline
title = "SantaCoder πŸŽ… Swift 🍏 Completion"
description = "This is a subspace to make code generation with [SantaCoder fine-tuned on The Stack Swift](https://huggingface.co/mrm8488/santacoder-finetuned-the-stack-swift)"
EXAMPLE_0 = "import SwiftUI\n\nstruct ContentView: View {\n var body: some View {"
EXAMPLE_1 = "// Make a naviagtion list with the days of the week\nNavigationView {"
CKPT = "mrm8488/santacoder-finetuned-the-stack-swift"
examples = [[EXAMPLE_0, 9, 0.6, 42], [EXAMPLE_1, 114, 0.6, 42]]
tokenizer = AutoTokenizer.from_pretrained(CKPT)
model = AutoModelForCausalLM.from_pretrained(CKPT, trust_remote_code=True)
def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42):
set_seed(seed)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
generated_text = pipe(gen_prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text']
return generated_text
iface = gr.Interface(
fn=code_generation,
inputs=[
gr.Textbox(lines=10, label="Input code"),
gr.inputs.Slider(
minimum=8,
maximum=256,
step=1,
default=8,
label="Number of tokens to generate",
),
gr.inputs.Slider(
minimum=0,
maximum=2,
step=0.1,
default=0.6,
label="Temperature",
),
gr.inputs.Slider(
minimum=0,
maximum=1000,
step=1,
default=42,
label="Random seed to use for the generation"
)
],
outputs=gr.Textbox(label="Predicted code", lines=10),
examples=examples,
layout="horizontal",
theme="peach",
description=description,
title=title
)
iface.launch()