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app (1).py
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import gradio as gr
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModel
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# ββ Config ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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BASE_MODEL = "Qwen/Qwen2.5-Coder-0.5B-Instruct"
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ADAPTER_REPO = "TRUFELLINI/qwen2.5-coder-0.5b-bugfix"
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SYSTEM_PROMPT = """You are a Python bug fixing assistant.
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Rules:
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- Make the minimal change possible
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- Do not rewrite unrelated code
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- Return only fixed code
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- Preserve original behavior unless required"""
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# ββ Load model (runs once when Space starts) βββββββββββββββββββββββββββββ
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print("Loading base model...")
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tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
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model = AutoModelForCausalLM.from_pretrained(
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BASE_MODEL,
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torch_dtype=torch.float32, # CPU β float32
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device_map="cpu",
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)
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print("Loading LoRA adapter...")
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model = PeftModel.from_pretrained(model, ADAPTER_REPO)
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model.eval()
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print("Model ready.")
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# ββ Inference ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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def fix_bug(buggy_code, max_new_tokens=400):
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if not buggy_code.strip():
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return "Paste some buggy Python code above."
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": f"Fix the bug:\n{buggy_code}"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt")
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens = max_new_tokens,
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temperature = 0.1,
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do_sample = True,
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pad_token_id = tokenizer.eos_token_id,
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)
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new_tokens = output[0][inputs["input_ids"].shape[1]:]
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return tokenizer.decode(new_tokens, skip_special_tokens=True)
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# ββ UI βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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EXAMPLES = [
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["def calculate_average(numbers):\n total = 0\n for n in numbers:\n total += n\n return total / len(numbers)"],
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["def add_item(item, items=[]):\n items.append(item)\n return items"],
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["def sum_list(nums):\n total = 0\n for i in range(len(nums)+1):\n total += nums[i]\n return total"],
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["def first_element(lst):\n return lst[1]"],
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]
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demo = gr.Interface(
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fn = fix_bug,
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inputs = gr.Code(
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label = "Paste buggy Python code here",
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language = "python",
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lines = 14,
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),
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outputs = gr.Code(
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label = "Fixed code",
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language = "python",
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),
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title = "π Python Bug Fixer",
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description = """**Qwen2.5-Coder 0.5B fine-tuned on real buggyβfixed Python pairs.**
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Makes minimal, correct fixes β not rewrites. Trained using QLoRA on [alexjercan/bugnet](https://huggingface.co/datasets/alexjercan/bugnet).
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[[Model]](https://huggingface.co/your-username/qwen2.5-coder-0.5b-bugfix) Β· [[GitHub]](https://github.com/your-username/python-bugfix-llm)""",
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examples = EXAMPLES,
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theme = gr.themes.Soft(),
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cache_examples = False,
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)
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demo.launch()
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