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| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| from threading import Thread | |
| import time | |
| # Load small and fast model for Python code debugging | |
| MODEL_NAME = "Salesforce/codet5-small" | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| def debug_python(code, progress=gr.State(0)): | |
| if not code.strip(): | |
| return "❌ Please enter Python code.", 0 | |
| prompt = f"Fix this Python code:\n{code}\nCorrected code:\n" | |
| inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512).to(device) | |
| def update_progress(): | |
| for i in range(1, 101): | |
| time.sleep(0.005) # Faster loading feel | |
| progress.value = i | |
| Thread(target=update_progress).start() | |
| outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.2, do_sample=False) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| progress.value = 100 | |
| return response.strip(), 100 | |
| with gr.Blocks(css=".gradio-container {background-color: #cbedec;}") as app: | |
| gr.Markdown("## 🐍 Python Code Debugger (Fast Mode)") | |
| gr.Markdown("Just paste your Python code. The AI will fix any issues it detects.") | |
| code_box = gr.Textbox(label="📝 Your Python Code", lines=12) | |
| progress_bar = gr.Slider(minimum=0, maximum=100, value=0, interactive=False, label="⏳ Progress (%)") | |
| output = gr.Code(label="✅ Suggested Fix") | |
| fix_btn = gr.Button("🛠️ Debug Code") | |
| fix_btn.click(fn=debug_python, inputs=[code_box], outputs=[output, progress_bar]) | |
| app.launch() | |