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Update app.py
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app.py
CHANGED
@@ -1,30 +1,36 @@
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import streamlit as st
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import os
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import pandas as pd
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from huggingface_hub import login, InferenceClient
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import pickle
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import
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st.set_page_config(layout="wide")
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# Authenticate with Hugging Face
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login(token=os.getenv("TOKEN"))
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with open('l.pkl', 'rb') as file:
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with open('items_dict.pkl', 'rb') as file:
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data = pd.read_csv('marketing_sample_for_walmart_com-product_details__20200101_20200331__30k_data.csv')
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# Initialize the inference client for the Mixtral model
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client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
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# Initialize session state variables
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if 'recipe' not in st.session_state:
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st.session_state.recipe = None
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@@ -32,7 +38,7 @@ if 'recipe_saved' not in st.session_state:
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st.session_state.recipe_saved = None
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if 'user_direction' not in st.session_state:
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st.session_state.user_direction =
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if 'serving_size' not in st.session_state:
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st.session_state.serving_size = 2
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@@ -41,7 +47,7 @@ if 'selected_difficulty' not in st.session_state:
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st.session_state.selected_difficulty = "Quick & Easy"
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if 'exclusions' not in st.session_state:
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st.session_state.exclusions =
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def create_detailed_prompt(user_direction, exclusions, serving_size, difficulty):
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@@ -74,6 +80,60 @@ def generate_recipe(user_inputs):
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prompt = create_detailed_prompt(user_inputs['user_direction'], user_inputs['exclusions'],
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user_inputs['serving_size'], user_inputs['difficulty'])
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generate_kwargs = dict(
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temperature=0.9,
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max_new_tokens=10000,
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@@ -82,17 +142,16 @@ def generate_recipe(user_inputs):
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do_sample=True,
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)
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response = client.text_generation(prompt, **generate_kwargs)
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st.session_state.recipe = json.loads(response)
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except json.JSONDecodeError:
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st.session_state.recipe = None
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st.session_state.recipe_saved = False
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def clear_inputs():
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st.session_state.user_direction =
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st.session_state.exclusions =
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st.session_state.serving_size = 2
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st.session_state.selected_difficulty = "Quick & Easy"
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@@ -101,7 +160,7 @@ st.title("Let's get cooking")
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st.session_state.user_direction = st.text_area(
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"What do you want to cook? Describe anything - a dish, cuisine, event, or vibe.",
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value=st.session_state.user_direction,
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placeholder="quick snack,
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)
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st.session_state.serving_size = st.number_input(
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st.session_state.selected_difficulty = st.radio(
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"Choose a difficulty level for your recipe.",
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index=list(difficulty_dictionary).index(st.session_state.selected_difficulty)
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)
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for level, details in difficulty_dictionary.items():
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if level == st.session_state.selected_difficulty:
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st.caption(details["description"])
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st.session_state.exclusions = st.text_area(
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"Any ingredients you want to exclude?",
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value=st.session_state.exclusions,
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@@ -141,6 +196,7 @@ st.session_state.exclusions = st.text_area(
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)
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fancy_exclusions = ""
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if st.session_state.selected_difficulty == "Professional":
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exclude_fancy = st.checkbox(
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"Exclude cliche professional ingredients? (gold leaf, truffle, edible flowers, microgreens)",
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@@ -150,7 +206,7 @@ if st.session_state.selected_difficulty == "Professional":
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user_inputs = {
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"user_direction": st.session_state.user_direction,
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"exclusions": f"{st.session_state.exclusions}, {fancy_exclusions}",
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"serving_size": st.session_state.serving_size,
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"difficulty": st.session_state.selected_difficulty
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}
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@@ -164,22 +220,27 @@ with button_cols_submit[1]:
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with button_cols_submit[2]:
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st.empty()
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if st.session_state.recipe:
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st.divider()
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import streamlit as st
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import re
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import json
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import os
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import pandas as pd
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from huggingface_hub import login, InferenceClient
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import pickle
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from sklearn.metrics.pairwise import cosine_similarity
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st.set_page_config(layout="wide")
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login(token=os.getenv("TOKEN"))
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try:
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with open('l.pkl', 'rb') as file:
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similarity = pickle.load(file)
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except FileNotFoundError:
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st.error("The similarity file was not found.")
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try:
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with open('items_dict.pkl', 'rb') as file:
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items_dict = pickle.load(file)
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except FileNotFoundError:
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st.error("The items dictionary file was not found.")
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try:
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data = pd.read_csv('marketing_sample_for_walmart_com-product_details__20200101_20200331__30k_data.csv')
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except FileNotFoundError:
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st.error("The CSV file was not found.")
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# Initialize the inference client for the Mixtral model
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client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
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if 'recipe' not in st.session_state:
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st.session_state.recipe = None
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st.session_state.recipe_saved = None
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if 'user_direction' not in st.session_state:
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st.session_state.user_direction = None
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if 'serving_size' not in st.session_state:
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st.session_state.serving_size = 2
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st.session_state.selected_difficulty = "Quick & Easy"
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if 'exclusions' not in st.session_state:
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st.session_state.exclusions = None
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def create_detailed_prompt(user_direction, exclusions, serving_size, difficulty):
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prompt = create_detailed_prompt(user_inputs['user_direction'], user_inputs['exclusions'],
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user_inputs['serving_size'], user_inputs['difficulty'])
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functions = [
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{
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"name": "provide_recipe",
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"description": "Provides a detailed recipe strictly adhering to the user input/specifications, especially ingredient exclusions and the recipe difficulty",
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"parameters": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "A creative name for the recipe"
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},
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"description": {
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"type": "string",
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"description": "a brief one-sentence description of the provided recipe"
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},
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"ingredients": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "Quantity and name of the ingredient"
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}
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}
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}
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},
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"instructions": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"step_number": {
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"type": "number",
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"description": "The sequence number of this step"
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},
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"instruction": {
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"type": "string",
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"description": "Detailed description of what to do in this step"
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}
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}
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}
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}
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},
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"required": [
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"name",
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"description",
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"ingredients",
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"instructions"
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],
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},
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}
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]
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generate_kwargs = dict(
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temperature=0.9,
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max_new_tokens=10000,
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do_sample=True,
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)
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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)}"
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response = client.text_generation(prompt, **generate_kwargs)
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st.session_state.recipe = response
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st.session_state.recipe_saved = False
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def clear_inputs():
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st.session_state.user_direction = None
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st.session_state.exclusions = None
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st.session_state.serving_size = 2
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st.session_state.selected_difficulty = "Quick & Easy"
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st.session_state.user_direction = st.text_area(
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"What do you want to cook? Describe anything - a dish, cuisine, event, or vibe.",
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value=st.session_state.user_direction,
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placeholder="quick snack, asian style bowl with either noodles or rice, something italian",
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)
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st.session_state.serving_size = st.number_input(
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st.session_state.selected_difficulty = st.radio(
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"Choose a difficulty level for your recipe.",
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list(difficulty_dictionary.keys()),
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index=list(difficulty_dictionary).index(st.session_state.selected_difficulty)
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)
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st.session_state.exclusions = st.text_area(
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"Any ingredients you want to exclude?",
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value=st.session_state.exclusions,
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)
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fancy_exclusions = ""
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if st.session_state.selected_difficulty == "Professional":
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exclude_fancy = st.checkbox(
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"Exclude cliche professional ingredients? (gold leaf, truffle, edible flowers, microgreens)",
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user_inputs = {
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"user_direction": st.session_state.user_direction,
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"exclusions": f"{st.session_state.exclusions}, {fancy_exclusions}".strip(", "),
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"serving_size": st.session_state.serving_size,
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"difficulty": st.session_state.selected_difficulty
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}
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with button_cols_submit[2]:
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st.empty()
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if st.session_state.recipe is not None:
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st.divider()
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try:
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print(st.session_state.recipe)
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recipe = json.loads(st.session_state.recipe)
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name_and_dis = f'# {recipe["name"]}\n\n'
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name_and_dis += f'{recipe["description"]}\n\n'
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ingredients = '## Ingredients:\n'
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for ingredient in recipe["ingredients"]:
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ingredients += f"- {ingredient['name']}\n"
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instructions = '\n## Instructions:\n'
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for instruction in recipe["instructions"]:
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instructions += f"{instruction['step_number']}. {instruction['instruction']}\n"
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st.write(name_and_dis)
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col01, col02 = st.columns(2)
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with col01:
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cont = st.container()
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cont.write(ingredients)
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with col02:
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cont = st.container()
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cont.write(instructions)
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except (json.JSONDecodeError, KeyError) as e:
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st.error(f"Failed to parse recipe: {e}")
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