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Browse files- app.py +56 -0
- requirements.txt +3 -0
app.py
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
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from transformers import pipeline
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title = "InCoder Generator"
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description = "This is a subspace to make code generation with InCoder, it is used in a larger space for model comparison."
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example = [
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["def print_hello_world():", "Sample", 8, 42],
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["def get_file_size(filepath):", "Sample", 22, 42]]
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tokenizer = AutoTokenizer.from_pretrained("facebook/incoder-6B")
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model = AutoModelForCausalLM.from_pretrained("facebook/incoder-6B", low_cpu_mem_usage=True)
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def code_generation(gen_prompt, strategy, max_tokens, seed=42):
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set_seed(seed)
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gen_kwargs = {}
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gen_kwargs["do_sample"] = strategy == "Sample"
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gen_kwargs["max_new_tokens"] = max_tokens
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if gen_kwargs["do_sample"]:
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gen_kwargs["temperature"] = 0.2
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gen_kwargs["top_k"] = 0
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gen_kwargs["top_p"] = 0.95
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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generated_text = pipe(gen_prompt, **gen_kwargs)[0]['generated_text']
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return generated_text
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iface = gr.Interface(
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fn=code_generation,
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inputs=[
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gr.Textbox(lines=10, label="Input code"),
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gr.Dropdown(choices=["Greedy", "Sample"], value="Greedy"),
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gr.inputs.Slider(
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minimum=8,
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maximum=256,
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step=1,
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default=8,
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label="Number of tokens to generate",
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),
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gr.inputs.Slider(
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minimum=0,
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maximum=1000,
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step=1,
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default=42,
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label="Random seed to use for the generation"
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)
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],
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outputs=gr.Textbox(label="Predicted code", lines=10),
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examples=example,
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layout="horizontal",
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theme="peach",
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description=description,
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title=title
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)
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iface.launch()
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requirements.txt
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transformers
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accelerate
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torch
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