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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 = "<start>\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("<end>")
)
generated = tokenizer.decode(output_ids[0], skip_special_tokens=False)
if "Directions:" in generated:
generated = generated.split("Directions:")[1]
if "<end>" in generated:
generated = generated.split("<end>")[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()
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