import streamlit as st import pandas as pd from datetime import datetime import os import matplotlib.pyplot as plt import seaborn as sns from huggingface_hub import HfApi, upload_file, list_repo_files, hf_hub_download # Configuration for Hugging Face Repository REPO_ID = "MarcosRodrigo/Breakfast-Poll" HISTORY_DIR = "history" TEMP_FILE = "current_selections.csv" # Hugging Face API (requires a token with write access) hf_token = st.secrets["HF_TOKEN"] api = HfApi() # Initialize all required session state variables if "users" not in st.session_state: st.session_state.users = [] if "current_selections" not in st.session_state: st.session_state.current_selections = [] if "step" not in st.session_state: st.session_state.step = 1 if "history" not in st.session_state: st.session_state.history = [] # Load temporary selections from the shared file def load_current_selections(): if os.path.exists(TEMP_FILE): return pd.read_csv(TEMP_FILE) else: return pd.DataFrame(columns=["Name", "Drinks", "Food"]) # Save current user selections to the shared CSV file without overwriting previous data def save_current_selection_to_file(current_selections): current_selections["Drinks"] = current_selections["Drinks"].apply(lambda x: ", ".join(x) if isinstance(x, list) else x) current_selections["Food"] = current_selections["Food"].apply(lambda x: ", ".join(x) if isinstance(x, list) else x) if os.path.exists(TEMP_FILE): existing_selections = pd.read_csv(TEMP_FILE) combined_selections = pd.concat([existing_selections, current_selections]).drop_duplicates() else: combined_selections = current_selections combined_selections.to_csv(TEMP_FILE, index=False) # Upload the shared file to Hugging Face repository for persistence def upload_temp_file_to_repo(): if os.path.exists(TEMP_FILE): upload_file( path_or_fileobj=TEMP_FILE, path_in_repo=TEMP_FILE, repo_id=REPO_ID, token=hf_token, repo_type="space" ) # Delete a file from the repository (e.g., `current_selections.csv`) def delete_file_from_repo(filename): api.delete_file( path_in_repo=filename, repo_id=REPO_ID, token=hf_token, repo_type="space" ) # Download the shared file from the repository to ensure persistence and real-time updates def download_temp_file_from_repo(): try: hf_hub_download(repo_id=REPO_ID, filename=TEMP_FILE, repo_type="space", token=hf_token, local_dir=".") except Exception: pd.DataFrame(columns=["Name", "Drinks", "Food"]).to_csv(TEMP_FILE, index=False) # Load history from the repository def load_history(): history = [] files_in_repo = list_repo_files(REPO_ID, token=hf_token, repo_type="space") history_files = [f for f in files_in_repo if f.startswith(f"{HISTORY_DIR}/") and f.endswith(".txt")] for file in history_files: local_filepath = hf_hub_download(repo_id=REPO_ID, filename=file, token=hf_token, repo_type="space") summary_df = pd.read_csv(local_filepath) date = file.split("/")[-1].split(".txt")[0] history.append({"Date": date, "Summary": summary_df}) return history # Save the current summary to a text file in the history directory def save_summary_to_history(): timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") history_filename = f"{HISTORY_DIR}/{timestamp}.txt" if not os.path.exists(HISTORY_DIR): os.makedirs(HISTORY_DIR) if os.path.exists(TEMP_FILE): summary_df = pd.read_csv(TEMP_FILE) summary_df.to_csv(history_filename, index=False) upload_file(path_or_fileobj=history_filename, path_in_repo=history_filename, repo_id=REPO_ID, token=hf_token, repo_type="space") return timestamp # Load persistent history and temporary selections on app start if "history" not in st.session_state: download_temp_file_from_repo() st.session_state.history = load_history() st.session_state.current_selections = load_current_selections().to_dict(orient="records") # Sidebar for navigating through different views menu = st.sidebar.selectbox("Select View", ["Poll", "Current", "History", "Graph"]) # Function to reset the current selections after submission def reset_selections(): st.session_state.users = [] st.session_state.current_selections = [] # Poll view with four consecutive steps if menu == "Poll": st.title("Breakfast Poll Application") # Step 1: User's Name st.header("Step 1: Enter your name") name = st.text_input("Name:") if st.button("Next", key="step1_next") and name: st.session_state.users.append(name) st.session_state.step = 2 # Set the next step to be visible # Show Step 2 only if Step 1 is completed if st.session_state.step >= 2: st.header("Step 2: Select your drink(s)") drinks_options = [ "Café con leche", "Colacao", "Descafeinado con leche", "Cortado", "Aguasusia", "Aguasusia susia", "Café descafeinado con leche desnatada", "Italiano", "Café con soja", "Té", "Manzanilla", "Nada" ] selected_drinks = st.multiselect("Choose your drinks:", drinks_options) if st.button("Next", key="step2_next") and selected_drinks: st.session_state.current_selections.append({"Name": st.session_state.users[-1], "Drinks": selected_drinks}) st.session_state.step = 3 # Set the next step to be visible # Show Step 3 only if Step 2 is completed if st.session_state.step >= 3: st.header("Step 3: Select your food(s)") food_options = [ "Barrita aceite", "Barrita tomate", "Palmera chocolate", "Palmera chocolate blanco", "Yogurt", "Tortilla", "Nada" ] selected_food = st.multiselect("Choose your food:", food_options) if st.button("Save Selections", key="save_selections") and selected_food: st.session_state.current_selections[-1]["Food"] = selected_food df = pd.DataFrame(st.session_state.current_selections) save_current_selection_to_file(df) upload_temp_file_to_repo() st.success(f"Selections saved for {st.session_state.users[-1]}!") st.session_state.step = 1 # Reset to step 1 for the next user # History view to check past summaries elif menu == "History": st.title("Breakfast Poll History") # Reload history if it's not already loaded if not st.session_state.history: st.session_state.history = load_history() if st.session_state.history: # Display history in reverse chronological order for record in reversed(st.session_state.history): st.subheader(f"Date: {record['Date']}") st.table(record["Summary"]) else: st.write("No history records found.") # # "Current" view to display the current summary of all users' selections and submit to history # elif menu == "Current": # st.title("Current Selections of All Users") # if st.button("Reload Selections"): # download_temp_file_from_repo() # current_df = load_current_selections() # st.table(current_df) # if st.button("Submit Summary to History"): # timestamp = save_summary_to_history() # st.success(f"Summary saved to history at {timestamp}") # st.session_state.history = load_history() # # Clear local and remote current selections # if os.path.exists(TEMP_FILE): # os.remove(TEMP_FILE) # delete_file_from_repo(TEMP_FILE) # Delete the file from the remote repo # # Create an empty CSV to replace the deleted one # pd.DataFrame(columns=["Name", "Drinks", "Food"]).to_csv(TEMP_FILE, index=False) # upload_temp_file_to_repo() # "Current" view to display the current summary of all users' selections and generate ticket elif menu == "Current": st.title("Current Selections of All Users") if st.button("Reload Selections"): download_temp_file_from_repo() # Load the current selections from the session state or from the file current_df = load_current_selections() st.table(current_df) # Define item prices item_prices = { "Cafés": { "Café con leche": 1.20, "Descafeinado con leche": 1.20, "Cortado": 1.20, "Aguasusia": 1.20, "Aguasusia susia": 1.20, "Descafeinado con leche desnatada": 1.20, "Italiano": 1.20, "Café con soja": 1.20, "Café sin lactosa": 1.20, }, "Infusiones": { "Té": 0.90, "Manzanilla": 0.90, }, "Colacaos": { "Colacao": 1.50, }, "Palmeras": { "Palmera chocolate": 1.50, "Palmera chocolate blanco": 1.50, }, "Barrita aceite": 0.65, "Barrita tomate": 1.30, "Napolitana": 0.65, "Croissant": 0.65, "Yogurt": 1.00, "Tortilla": 1.50, "Nada": 0.00, } # # Define item prices # item_prices = { # "Café con leche": 1.20, # "Colacao": 1.50, # "Café Descafeinado con leche": 1.20, # "Cortado": 1.20, # "Café Aguasusia": 1.20, # "Café Aguasusia susia": 1.20, # "Café descafeinado con leche desnatada": 1.20, # "Café Italiano": 1.20, # "Café con soja": 1.20, # "Té": 0.90, # "Manzanilla": 0.90, # "Nada": 0.00, # "Barrita con aceite": 0.65, # "Barrita con tomate": 1.30, # "Palmera de chocolate": 1.50, # "Palmera de chocolate blanco": 1.50, # "Yogurt": 1.00, # "Pincho de tortilla": 1.50 # } # Define combined prices for special combinations combo_prices = { "desayuno + café (aceite)": 1.85, "desayuno + café (tomate)": 2.50, "desayuno + café (napolitana)": 1.85, } # Use session state to persist ticket generation status if "ticket_generated" not in st.session_state: st.session_state.ticket_generated = False # Generate Ticket Button and Logic if st.button("Generate Ticket"): ticket = [] # Iterate over each user's selections drinks = [] foods = [] for _, row in current_df.iterrows(): drinks.append(row['Drinks']) foods.append(row['Food']) # Simplify items drinks = ["Café" if x in item_prices["Cafés"].keys() else x for x in drinks] drinks = ["Infusión" if x in item_prices["Infusiones"].keys() else x for x in drinks] foods = ["Palmera" if x in item_prices["Palmeras"].keys() else x for x in foods] # Display selections st.write(f"Drinks: {drinks}") st.write(f"Foods: {foods}") # Count items item_count = { "Café": len([x for x in drinks if x == "Café"]), "Infusión": len([x for x in drinks if x == "Infusión"]), "Colacao": len([x for x in drinks if x == "Colacao"]), "Barrita aceite": len([x for x in foods if x == "Barrita aceite"]), "Barrita tomate": len([x for x in foods if x == "Barrita tomate"]), "Napolitana": len([x for x in foods if x == "Napolitana"]), "Palmera": len([x for x in foods if x == "Palmeras"]), "Tortilla": len([x for x in foods if x == "Tortilla"]), "Croissant": len([x for x in foods if x == "Croissant"]), "Yogurt": len([x for x in foods if x == "Yogurt"]), } # Make associations item_association = {} food_count = item_count["Barrita aceite"] + item_count["Barrita tomate"] + item_count["Napolitana"] + item_count["Croissant"] if item_count["Café"] >= food_count: item_association["Desayuno + Café (tomate)"] = item_count["Barrita tomate"] item_association["Desayuno + Café (aceite)"] = item_count["Barrita aceite"] item_association["Desayuno + Café (napolitana)"] = item_count["Napolitana"] item_association["Desayuno + Café (croissant)"] = item_count["Croissant"] item_association["Café"] = item_count["Café"] - food_count else: raise NotImplementedError item_association["Colacaos"] = item_count["Colacao"] item_association["Yogurts"] = item_count["Yogurt"] item_association["Tortillas"] = item_count["Tortilla"] item_association["Palmeras"] = item_count["Palmera"] item_association["Infusiones"] = item_count["Infusión"] # Display total values for key, value in item_association.items(): if value > 0: st.write(f"{key} x {value}") # drinks = row['Drinks'].split(", ") if isinstance(row['Drinks'], str) else [] # food = row['Food'].split(", ") if isinstance(row['Food'], str) else [] # used_drinks = set() # used_food = set() # # Handle combinations of café + barrita con aceite # for drink in drinks: # if "café" in drink.lower() and "Barrita con aceite" in food: # ticket.append({"Item": "desayuno + café (aceite)", "Price": combo_prices["desayuno + café (aceite)"]}) # used_drinks.add(drink) # used_food.add("Barrita con aceite") # break # # Handle combinations of café + barrita con tomate # for drink in drinks: # if "café" in drink.lower() and "Barrita con tomate" in food and drink not in used_drinks: # ticket.append({"Item": "desayuno + café (tomate)", "Price": combo_prices["desayuno + café (tomate)"]}) # used_drinks.add(drink) # used_food.add("Barrita con tomate") # break # # Add remaining individual drinks not used in combinations # for drink in drinks: # if drink not in used_drinks and drink in item_prices: # ticket.append({"Item": drink, "Price": item_prices[drink]}) # used_drinks.add(drink) # # Add remaining individual food not used in combinations # for f in food: # if f not in used_food and f in item_prices: # ticket.append({"Item": f, "Price": item_prices[f]}) # used_food.add(f) # Create a DataFrame to display the ticket ticket_df = pd.DataFrame(ticket) # Format prices to show only 2 decimals ticket_df["Price"] = ticket_df["Price"].apply(lambda x: f"{x:.2f}") st.subheader("Generated Ticket") st.table(ticket_df) # Calculate and display the total price total_price = sum([float(price) for price in ticket_df["Price"]]) st.write(f"**Total Price:** {total_price:.2f} €") # Set ticket_generated to True in session state st.session_state.ticket_generated = True # Only show the "Submit Summary to History" button if a ticket is generated if st.session_state.ticket_generated: if st.button("Submit Summary to History"): timestamp = save_summary_to_history() st.success(f"Summary saved to history at {timestamp}") st.session_state.history = load_history() # Clear local and remote current selections if os.path.exists(TEMP_FILE): os.remove(TEMP_FILE) delete_file_from_repo(TEMP_FILE) # Create an empty CSV to replace the deleted one pd.DataFrame(columns=["Name", "Drinks", "Food"]).to_csv(TEMP_FILE, index=False) upload_temp_file_to_repo() # Reset session state for current selections and ticket generation status st.session_state.current_selections = [] st.session_state.ticket_generated = False # Reload the current selections to show an empty table current_df = pd.DataFrame(columns=["Name", "Drinks", "Food"]) st.table(current_df) # History view to check past summaries elif menu == "History": st.title("Breakfast Poll History") # Reload history if it's not already loaded if not st.session_state.history: st.session_state.history = load_history() if st.session_state.history: # Display history in reverse chronological order for record in reversed(st.session_state.history): st.subheader(f"Date: {record['Date']}") st.table(record["Summary"]) else: st.write("No history records found.") # Graph view to display a line chart of item selections over time elif menu == "Graph": st.title("Breakfast Poll History - Graph View") # Load the history if not already loaded if not st.session_state.history: st.session_state.history = load_history() # Prepare data for plotting if st.session_state.history: history_data = [] user_data = {} # Store user-specific data for record in st.session_state.history: # Extract only the date part (YYYY-MM-DD) for display date = record['Date'].split("_")[0] # Use only the YYYY-MM-DD portion of the date for index, row in record['Summary'].iterrows(): user = row['Name'] for drink in row['Drinks'].split(', '): history_data.append({'Date': date, 'Item': drink, 'Type': 'Drink', 'User': user}) for food in row['Food'].split(', '): history_data.append({'Date': date, 'Item': food, 'Type': 'Food', 'User': user}) # Append user data for selection if user not in user_data: user_data[user] = True # Initialize all users as visible by default # Create a DataFrame from history data history_df = pd.DataFrame(history_data) # Count occurrences of each item per date item_counts = history_df.groupby(['Date', 'Item', 'Type', 'User']).size().reset_index(name='Count') # Separate items into Drinks and Food, and sort them alphabetically drinks = sorted(item_counts[item_counts['Type'] == 'Drink']['Item'].unique()) foods = sorted(item_counts[item_counts['Type'] == 'Food']['Item'].unique()) # Create a dictionary to store the checkbox values for each item item_visibility = {} # Create interactive checkboxes for Drinks, Food, and Users in the sidebar st.sidebar.header("Select Items to Display") # Drinks Section if drinks: st.sidebar.subheader("Drinks") for item in drinks: # Add a unique key to each checkbox to avoid duplicate widget IDs item_visibility[item] = st.sidebar.checkbox(item, value=True, key=f"checkbox_{item}_Drink") # Food Section if foods: st.sidebar.subheader("Food") for item in foods: # Add a unique key to each checkbox to avoid duplicate widget IDs item_visibility[item] = st.sidebar.checkbox(item, value=True, key=f"checkbox_{item}_Food") # User Section: Create a checkbox for each user to toggle their visibility st.sidebar.subheader("Users") for user in user_data.keys(): user_data[user] = st.sidebar.checkbox(user, value=True, key=f"checkbox_user_{user}") # Filter the data based on selected items and users selected_items = [item for item, visible in item_visibility.items() if visible] selected_users = [user for user, visible in user_data.items() if visible] filtered_item_counts = item_counts[item_counts['Item'].isin(selected_items) & item_counts['User'].isin(selected_users)] # Check if there is data to display if not filtered_item_counts.empty: # Create a line plot for each selected item over time plt.figure(figsize=(12, 6)) sns.lineplot(data=filtered_item_counts, x='Date', y='Count', hue='Item', marker='o') # Customize the y-axis to show only integer labels y_max = max(filtered_item_counts['Count'].max() + 1, 1) # Set y_max to at least 1 to avoid errors plt.yticks(range(0, y_max)) # Show only integer labels on the y-axis # Customize the plot plt.xticks(rotation=45) plt.title('Item Selections Over Time') plt.xlabel('Date') plt.ylabel('Number of Selections') plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=3) # Display the plot st.pyplot(plt.gcf()) else: st.write("No data to display. Please select at least one user and one item.") else: st.write("No historical data available to plot.")