import gradio as gr import torch from transformers import AutoModelForCausalLM, GPT2TokenizerFast # Load fine-tuned model model_path = "saksh-d/recipe-gpt" tokenizer = GPT2TokenizerFast.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path) model.eval() # Device setup device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu") model.to(device) def generate(ingredient_text, temperature, top_k, top_p, max_length): # Format ingredients into a list ingredients = [line.strip("- ").strip() for line in ingredient_text.strip().splitlines() if line.strip()] prompt = "\nIngredients:\n" for ing in ingredients: prompt += f"- {ing}\n" prompt += "Directions:\n" inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs.input_ids.to(device) attention_mask = inputs.attention_mask.to(device) with torch.no_grad(): output_ids = model.generate( input_ids, attention_mask=attention_mask, do_sample=True, temperature=temperature, top_k=top_k, top_p=top_p, max_length=max_length, eos_token_id=tokenizer.convert_tokens_to_ids("") ) generated = tokenizer.decode(output_ids[0], skip_special_tokens=False) if "Directions:" in generated: generated = generated.split("Directions:")[1] if "" in generated: generated = generated.split("")[0] return generated.strip() iface = gr.Interface( fn=generate, inputs=[ gr.Textbox(lines=8, label="Ingredients (one per line)"), gr.Slider(minimum=0.5, maximum=1.5, value=0.7, step=0.1, label="Temperature (Creativity)"), gr.Slider(minimum=0, maximum=100, value=50, step=5, label="Top-k (Word choices at each step)"), gr.Slider(minimum=0.5, maximum=1.0, value=0.9, step=0.05, label="Top-p (Nucleus Sampling"), gr.Slider(minimum=50, maximum=150, value=90, step=10, label="Recipe Length"), ], outputs=gr.Textbox(lines=12, label="Generated Recipe Directions"), title="Recipe-GPT", description="Enter a list of ingredients to generate step-by-step cooking directions. Adjust the sliders for more or less creativity." ) iface.launch()