Spaces:
Sleeping
Sleeping
File size: 3,138 Bytes
0aa79e7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
# app/services/recipe_generator.py
from typing import List, Dict
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import os
class RecipeGenerator:
def __init__(self):
# Set cache directory to a writable location
os.environ['TRANSFORMERS_CACHE'] = '/tmp/huggingface'
# Create cache directory if it doesn't exist
os.makedirs('/tmp/huggingface', exist_ok=True)
# Initialize your fine-tuned model and tokenizer
try:
self.tokenizer = AutoTokenizer.from_pretrained("flax-community/t5-recipe-generation", cache_dir='/tmp/huggingface')
self.model = AutoModelForCausalLM.from_pretrained("flax-community/t5-recipe-generation", cache_dir='/tmp/huggingface')
except Exception as e:
print(f"Error loading model: {str(e)}")
# Provide a fallback or raise the error as needed
raise
async def generate(self, ingredients: List[str]) -> Dict[str, List[str]]:
try:
# Format ingredients for input
input_text = f"Generate a recipe using these ingredients: {', '.join(ingredients)}"
# Tokenize and generate
inputs = self.tokenizer(input_text, return_tensors="pt", padding=True)
outputs = self.model.generate(
inputs.input_ids,
max_length=512,
num_return_sequences=1,
temperature=0.7,
top_p=0.9,
do_sample=True
)
# Decode and parse the generated recipe
generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
# Parse the generated text into structured format
lines = generated_text.split('\n')
title = lines[0] if lines else "Generated Recipe"
# Initialize lists
ingredients_list = []
instructions_list = []
# Simple parsing logic
current_section = None
for line in lines[1:]:
if "Ingredients:" in line:
current_section = "ingredients"
elif "Instructions:" in line:
current_section = "instructions"
elif line.strip():
if current_section == "ingredients":
ingredients_list.append(line.strip())
elif current_section == "instructions":
instructions_list.append(line.strip())
return {
"title": title,
"ingredients": ingredients_list or ["No ingredients generated"],
"instructions": instructions_list or ["No instructions generated"]
}
except Exception as e:
print(f"Error generating recipe: {str(e)}")
return {
"title": "Error Generating Recipe",
"ingredients": ["Error occurred while generating recipe"],
"instructions": ["Please try again later"]
} |