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import gradio as gr |
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import os, re, gc, time, sys, subprocess, typing, shutil, json, datetime, tempfile, safetensors, torch, threading, spaces |
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import numpy as np |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from accelerate import Accelerator |
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from huggingface_hub import login |
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HF_TOKEN=os.environ.get('HF_TOKEN') |
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login(token=HF_TOKEN) |
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" |
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os.environ["ZERO_GPU_PATCH_TORCH_DEVICE"] = "True" |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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accelerator=Accelerator() |
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TOKENIZER = accelerator.prepare(AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Instruct")) |
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MODEL = accelerator.prepare(AutoModelForCausalLM.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Instruct", torch_dtype=torch.bfloat16,)) |
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@spaces.GPU() |
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def plex(pmpt, input_text): |
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model_inputs="" |
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generated_text="" |
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prompt = f"{pmpt}. The code: {input_text}" |
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messages = [{"role": "system", "content": "Acting as an expert AI Web Development and Programming model. As an expert AI Web Development and Programming model, correct/modify/create/generate/complete the user provided code based on user request, output the code without comments. Only output code without additional prefixed or suffixed messages/comments/wrapping or additional text."}, {"role": "user", "content": prompt}] |
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text = TOKENIZER.apply_chat_template(messages,tokenize=False,add_generation_prompt=True) |
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model_inputs = TOKENIZER([text], return_tensors="pt", return_token_type_ids=False).to(device) |
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generated_text = "" |
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for new_text in TOKENIZER.decode(MODEL.generate(**model_inputs, max_new_tokens=8192, do_sample=True, temperature=0.7, top_k=30, top_p=0.8,)[0][len(model_inputs.input_ids[0]):], skip_special_tokens=True): |
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generated_text += new_text |
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yield generated_text.strip() |
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yield generated_text.strip() |
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with gr.Blocks() as iface: |
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ins=gr.Code(lines=30,placeholder="Paste the code you'd like to modify/fix/extend/edit here.. or start coding.. get assistance when/if required.") |
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pmpt=gr.Textbox(label="Prompt") |
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btn=gr.Button("Assist") |
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btn.click(plex,[pmpt,ins],[ins]) |
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iface.queue() |
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iface.launch(ssr_mode=False,share=False) |
