app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from transformers import Qwen2ForCausalLM
import torch

model_id = "cyirr/finetunecoder"

print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained("unsloth/deepseek-r1-distill-qwen-7b")

print("Loading model...")
model = Qwen2ForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float32,
    low_cpu_mem_usage=True,
)
model.eval()
print("Model ready!")

SYS = "You are an expert Python developer. You write clean, efficient, well-commented Python code. You follow PEP8, use Pythonic patterns, and always explain your reasoning."

def chat(message, history):
    prompt = "<|im_start|>system\n" + SYS + "\n<|im_end|>\n"
    for user, assistant in history:
        prompt += "<|im_start|>user\n" + user + "\n<|im_end|>\n"
        prompt += "<|im_start|>assistant\n" + assistant + "\n<|im_end|>\n"
    prompt += "<|im_start|>user\n" + message + "\n<|im_end|>\n<|im_start|>assistant\n"

    inputs = tokenizer(prompt, return_tensors="pt")
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=256,
            temperature=0.7,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
        )
    response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
    return response

gr.ChatInterface(
    fn=chat,
    title="DeepSeek Python Pro",
    description="Fine-tuned Python coding assistant",
    examples=[
        "Write a Python function to reverse a linked list",
        "Explain decorators with an example",
        "Write a binary search in Python",
    ],
).launch()