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| import streamlit as st | |
| import re | |
| import json | |
| import os | |
| import pandas as pd | |
| from huggingface_hub import login, InferenceClient | |
| import pickle | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| st.set_page_config(layout="wide") | |
| login(token=os.getenv("TOKEN")) | |
| with open('cv.pkl', 'rb') as file: | |
| cv = pickle.load(file) | |
| with open('vectors.pkl', 'rb') as file: | |
| vectors = pickle.load(file) | |
| with open('items_dict.pkl', 'rb') as file: | |
| items_dict = pd.DataFrame.from_dict(pickle.load(file)) | |
| data = pd.read_csv('marketing_sample_for_walmart_com-product_details__20200101_20200331__30k_data.csv') | |
| # Initialize the inference client for the Mixtral model | |
| client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") | |
| 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 get_recommendations(user_description, count_vectorizer, count_matrix): | |
| user_description = preprocess_text(user_description) | |
| user_vector = count_vectorizer.transform([user_description]) | |
| cosine_similarities = cosine_similarity(user_vector, count_matrix).flatten() | |
| similar_indices = cosine_similarities.argsort()[::-1] | |
| return similar_indices | |
| 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 an 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...'): | |
| prompt = create_detailed_prompt(user_inputs['user_direction'], user_inputs['exclusions'], | |
| user_inputs['serving_size'], user_inputs['difficulty']) | |
| functions = [ | |
| { | |
| "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" | |
| ], | |
| }, | |
| } | |
| ] | |
| generate_kwargs = dict( | |
| temperature=0.9, | |
| max_new_tokens=10000, | |
| top_p=0.9, | |
| repetition_penalty=1.0, | |
| do_sample=True, | |
| ) | |
| prompt += f"\nPlease format the output in JSON. The JSON should include fields for 'name', 'description', 'ingredients', and 'instructions', with each field structured as described below.\n\n{json.dumps(functions)}" | |
| response = client.text_generation(prompt, **generate_kwargs) | |
| st.session_state.recipe = response | |
| 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") | |
| col1,col2=st.columns(2) | |
| with col1: | |
| 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", | |
| ) | |
| with col2: | |
| 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) | |
| if exclude_fancy: | |
| 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}".strip(", "), | |
| "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() | |
| try: | |
| print(st.session_state.recipe) | |
| recipe = json.loads(st.session_state.recipe) | |
| name_and_dis = f'# {recipe["name"]}\n\n' | |
| name_and_dis += f'{recipe["description"]}\n\n' | |
| ingredients = '## Ingredients:\n' | |
| # for ingredient in recipe["ingredients"]: | |
| # ingredients += f"- {ingredient['name']}\n" | |
| instructions = '\n## Instructions:\n' | |
| for instruction in recipe["instructions"]: | |
| instructions += f"{instruction['step_number']}. {instruction['instruction']}\n" | |
| st.write(name_and_dis) | |
| col01, col02 = st.columns(2) | |
| with col01: | |
| cont = st.container(border=True, height=500) | |
| for i in recipe["ingredients"]: | |
| cont.selectbox(i['name'],options=items_dict.iloc[get_recommendations(i['name'],cv,vectors)]["Product Name"].values) | |
| with col02: | |
| cont = st.container(border=True, height=500) | |
| cont.write(instructions) | |
| except (json.JSONDecodeError, KeyError) as e: | |
| st.error(f"Failed to parse recipe: {e}") | |