Spaces:
Build error
Build error
| # app.py (Revisit this version from previous long answer) | |
| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| # --- Configuration --- | |
| MODEL_NAME = "neuralnets/cf_codebot" | |
| # --- Model Loading --- | |
| try: | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| model = AutoModelForCausalLM.from_pretrained(MODEL_NAME) | |
| model.eval() | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| print(f"Model loaded on: {device}") | |
| except Exception as e: | |
| print(f"Error loading model '{MODEL_NAME}': {e}") | |
| print("Using a dummy function for demonstration purposes.") | |
| tokenizer, model, device = None, None, "cpu" | |
| # --- Inference Function --- | |
| def generate_editorial(problem_statement: str, max_new_tokens: int, temperature: float, top_p: float) -> str: | |
| if model is None: | |
| return "Model not loaded, using dummy generation. (Check logs)" | |
| try: | |
| input_text = problem_statement | |
| inputs = tokenizer( | |
| input_text, | |
| return_tensors="pt", | |
| padding=True, | |
| truncation=True, | |
| max_length=512 | |
| ).to(device) | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=max_new_tokens, | |
| num_return_sequences=1, | |
| pad_token_id=tokenizer.eos_token_id, | |
| do_sample=True, | |
| top_k=50, | |
| top_p=top_p, | |
| temperature=temperature, | |
| stop_sequences=["<end_of_turn>"] # Can use this, or `stop` if transformers is very new | |
| ) | |
| generated_sequence = tokenizer.decode(outputs[0], skip_special_tokens=False) | |
| if generated_sequence.startswith(input_text): | |
| editorial_content = generated_sequence[len(input_text):].strip() | |
| editorial_content = editorial_content.replace("<end_of_turn>", "").strip() | |
| else: | |
| editorial_content = generated_sequence.strip() | |
| editorial_content = editorial_content.replace("<end_of_turn>", "").strip() | |
| return editorial_content | |
| except Exception as e: | |
| print(f"Error during inference: {e}") | |
| return f"An error occurred during editorial generation: {e}" | |
| # --- Gradio Interface Setup --- | |
| demo = gr.Interface( | |
| fn=generate_editorial, | |
| inputs=[ | |
| gr.Textbox(lines=10, label="Problem Statement", placeholder="Paste your problem statement here...", autofocus=True), | |
| gr.Slider(minimum=1, maximum=1024, value=400, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), | |
| ], | |
| outputs=gr.Textbox(label="Generated Editorial"), | |
| title="Codeforces Editorial Assistant (Model Loaded In-Space)", | |
| description="Paste a Codeforces problem statement and get a generated editorial from neuralnets/cf_codebot.", | |
| flagging_mode="auto", # Updated from allow_flagging | |
| examples=[ | |
| [ | |
| "A. Watermelon\ntime limit per test\n1 second\nmemory limit per test\n64 megabytes\n\nOne hot summer day Pete and his friend Billy decided to buy a watermelon. They chose the biggest and the ripest one, in their opinion. After that the watermelon was weighed, and the scales showed w kilos. They rushed home, dying of thirst, and decided to divide the berry, however they faced a hard problem.\n\nPete and Billy are great fans of even numbers, that's why they want to divide the watermelon in such a way that each of the two parts weighs even number of kilos, at the same time it is not obligatory that the parts are equal. The boys are extremely tired and want to start their meal as soon as possible, that's why you should help them and find out, if they can divide the watermelon in the way they want. For sure, each of them should get a part of positive weight.\nInput\n\nThe first (and the only) input line contains integer number w (1 ≤ w ≤ 100) — the weight of the watermelon bought by the boys.\nOutput\n\nPrint YES, if the boys can divide the watermelon into two parts, each of them weighing even number of kilos; and NO in the opposite case.", | |
| 400, | |
| 0.7, | |
| 0.95 | |
| ] | |
| ] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |