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Create app.py
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app.py
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import torch
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, logging
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checkpoint = "Salesforce/codegen-350M-mono"
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tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(checkpoint, cache_dir="models/", trust_remote_code=True, revision="main")
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def code_gen(text, max_tokens, temp, top_p, rep_penality):
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logging.set_verbosity(logging.CRITICAL)
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pipe = pipeline(
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model=checkpoint,
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max_new_tokens=max_tokens,
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temperature=temp,
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top_p=top_p,
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device= "cuda" if torch.cuda.is_available() else "cpu",
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repetition_penalty=rep_penality
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)
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response = pipe(text)
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print(response)
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return response[0]['generated_text']
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Inferece = gr.Interface(
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fn=code_gen,
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inputs=[
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gr.components.Textbox(label="Input what you want, the AI will make the code for you."),
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gr.components.Slider(minimum=128, maximum=512, step=128, label="Choose Max Token"),
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gr.components.Slider(minimum=0.1, maximum=1, step=0.05, label="Choose the model Temperature"),
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gr.components.Slider(minimum=0.1, maximum=1.25, step=0.05, label="Choose top_p"),
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gr.components.Slider(minimum=0.1, maximum=2, step=0.1, label="Choose repetition_penalty")
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],
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outputs="text",
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live=False # Ensure live is set to False
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)
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gr.Markdown('<h2 align="center">AI Code Gen</h2>')
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Inferece.launch()
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