import streamlit as st import re,torch import json,os from transformers import AutoModelForCausalLM, AutoTokenizer from datetime import datetime # Load model and tokenizer model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-0.5B-Chat", torch_dtype="auto", device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B-Chat") if 'recipe' not in st.session_state: st.session_state.recipe = None if 'recipe_saved' not in st.session_state: st.session_state.recipe_saved = None if 'user_direction' not in st.session_state: st.session_state.user_direction = None if 'serving_size' not in st.session_state: st.session_state.serving_size = 2 if 'selected_difficulty' not in st.session_state: st.session_state.selected_difficulty = "Quick & Easy" if 'exclusions' not in st.session_state: st.session_state.exclusions = None def create_detailed_prompt(user_direction, exclusions, serving_size, difficulty): if difficulty == "Quick & Easy": prompt = ( f"Provide a 'Quick and Easy' recipe for {user_direction} that excludes {exclusions} and has a serving size of {serving_size}. " f"It should require as few ingredients as possible and should be ready in as little time as possible. " f"The steps should be simple, and the ingredients should be commonly found in a household pantry. " f"Provide a detailed ingredient list and step-by-step guide that explains the instructions to prepare in detail." ) elif difficulty == "Intermediate": prompt = ( f"Provide a classic recipe for {user_direction} that excludes {exclusions} and has a serving size of {serving_size}. " f"The recipe should offer a bit of a cooking challenge but should not require professional skills. " f"The recipe should feature traditional ingredients and techniques that are authentic to its cuisine. " f"Provide a detailed ingredient list and step-by-step guide that explains the instructions to prepare in detail." ) elif difficulty == "Professional": prompt = ( f"Provide a advanced recipe for {user_direction} that excludes {exclusions} and has a serving size of {serving_size}. " f"The recipe should push the boundaries of culinary arts, integrating unique ingredients, advanced cooking techniques, and innovative presentations. " f"The recipe should be able to be served at a high-end restaurant or would impress at a gourmet food competition. " f"Provide a detailed ingredient list and step-by-step guide that explains the instructions to prepare in detail." ) return prompt def generate_recipe(user_inputs): with st.spinner('Building the perfect recipe...'): functions = [ {'type': 'function', 'function':{ "name": "provide_recipe", "description": "Provides a detailed recipe strictly adhering to the user input/specifications, especially ingredient exclusions and the recipe difficulty", "parameters": { "type": "object", "properties": { "name": { "type": "string", "description": "A creative name for the recipe" }, "description": { "type": "string", "description": "a brief one-sentence description of the provided recipe" }, "ingredients": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string", "description": "Quantity and name of the ingredient" } } } }, "instructions": { "type": "array", "items": { "type": "object", "properties": { "step_number": { "type": "number", "description": "The sequence number of this step" }, "instruction": { "type": "string", "description": "Detailed description of what to do in this step" } } } } }, "required": [ "name", "description", "ingredients", "instructions" ], }, } } ] prompt = create_detailed_prompt(user_inputs['user_direction'], user_inputs['exclusions'], user_inputs['serving_size'], user_inputs['difficulty']) messages = [{"role": "user", "content": prompt}] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, tools=functions ) # Tokenize and move to the correct device model_inputs = tokenizer([text], return_tensors="pt") torch.cuda.empty_cache() with torch.no_grad(): generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=64, tools=functions# Reduced value for memory management ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] st.session_state.recipe = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] st.session_state.recipe_saved = False def clear_inputs(): st.session_state.user_direction = None st.session_state.exclusions = None st.session_state.serving_size = 2 st.session_state.selected_difficulty = "Quick & Easy" st.title("Let's get cooking") st.session_state.user_direction = st.text_area( "What do you want to cook? Describe anything - a dish, cuisine, event, or vibe.", value = st.session_state.user_direction, placeholder="quick snack, asian style bowl with either noodles or rice, something italian", ) st.session_state.serving_size = st.number_input( "How many servings would you like to cook?", min_value=1, max_value=100, value=st.session_state.serving_size, step=1 ) difficulty_dictionary = { "Quick & Easy": { "description": "Easy recipes with straightforward instructions. Ideal for beginners or those seeking quick and simple cooking.", }, "Intermediate": { "description": "Recipes with some intricate steps that invite a little challenge. Perfect for regular cooks wanting to expand their repertoire with new ingredients and techniques.", }, "Professional": { "description": "Complex recipes that demand a high level of skill and precision. Suited for seasoned cooks aspiring to professional-level sophistication and creativity.", } } st.session_state.selected_difficulty = st.radio( "Choose a difficulty level for your recipe.", [ list(difficulty_dictionary.keys())[0], list(difficulty_dictionary.keys())[1], list(difficulty_dictionary.keys())[2] ], captions = [ difficulty_dictionary["Quick & Easy"]["description"], difficulty_dictionary["Intermediate"]["description"], difficulty_dictionary["Professional"]["description"] ], index=list(difficulty_dictionary).index(st.session_state.selected_difficulty) ) st.session_state.exclusions = st.text_area( "Any ingredients you want to exclude?", value = st.session_state.exclusions, placeholder="gluten, dairy, nuts, cilantro", ) fancy_exclusions ="" if st.session_state.selected_difficulty == "Professional": exclude_fancy = st.checkbox( "Exclude cliche professional ingredients? (gold leaf, truffle, edible flowers, microgreens)", value=True) fancy_exclusions = "gold leaf, truffle, edible flowers, microgreens, gold dust" user_inputs = { "user_direction" : st.session_state.user_direction, "exclusions": f"{st.session_state.exclusions}, {fancy_exclusions}", "serving_size": st.session_state.serving_size, "difficulty": st.session_state.selected_difficulty } button_cols_submit = st.columns([1, 1, 4]) with button_cols_submit[0]: st.button(label='Submit', on_click=generate_recipe, kwargs=dict(user_inputs=user_inputs), type="primary", use_container_width=True) with button_cols_submit[1]: st.button(label='Reset', on_click=clear_inputs, type="secondary", use_container_width=True) with button_cols_submit[2]: st.empty() if st.session_state.recipe is not None: st.divider() print(st.session_state.recipe) recipe = json.loads(st.session_state.recipe) recipe_md = '' recipe_md += f'# {recipe["name"]} \n\n' recipe_md += f'{recipe["description"]} \n\n' recipe_md += '## Ingredients: \n' for ingredient in recipe['ingredients']: recipe_md += f"- {ingredient['name']} \n" recipe_md += '\n## Instructions:\n' for instruction in recipe['instructions']: recipe_md += f"{instruction['step_number']}. {instruction['instruction']} \n" recipe['md'] = recipe_md recipe['timestamp'] = str(datetime.now()) st.markdown(recipe_md) st.write("")