Update app.py

app.py

@@ -1,51 +1,37 @@
-# Step 2: Authenticate the user (this will open a prompt)
-# from google.colab import auth
-# auth.authenticate_user()  # This will prompt you to log in and authenticate
-
-# Step 3: Download the .xlsx file from Google Drive using its ID
 import gdown
+import pandas as pd
+import random
 
-# Extracted file ID from your Google Drive URL
-file_id = '1BJFao3C6p8k3_KjLfmDo5KbNMrTs7MRo'  # Use your actual file ID from the URL
-url = f'https://drive.google.com/uc?id={file_id}'
+# If you upload the file directly to Hugging Face, you can access it directly.
+# For example, if the file is uploaded to the Hugging Face space, you can directly reference it like so:
+# df = pd.read_excel('./data.xlsx', engine='openpyxl')
 
-# Download the file
-gdown.download(url, 'data.xlsx', quiet=False)  # Save it as data.xlsx
+# If you're using gdown to download the file from Google Drive, you can keep this:
+# file_id = '1BJFao3C6p8k3_KjLfmDo5KbNMrTs7MRo'
+# url = f'https://drive.google.com/uc?id={file_id}'
+# gdown.download(url, 'data.xlsx', quiet=False)
 
-# Step 4: Read the .xlsx file using pandas
-import pandas as pd
+# Step 1: Read the Excel file (adjust file path if necessary)
+df = pd.read_excel('data.xlsx', engine='openpyxl')  # Adjust file path if uploaded locally
 
-# Read the Excel file
-df = pd.read_excel('data.xlsx', engine='openpyxl')  # Using openpyxl to read .xlsx files
-
-# Display the first 5 rows of the data
+# Step 2: Check the structure of the data
 print(df.head())
-# Check the column names and basic structure
 print(df.columns)
 
-# Check for any missing data
-print(df.isnull().sum())
+# Convert 'Cost per 4 persons' to numeric (in case there are invalid values)
+df['Cost per 4 persons'] = pd.to_numeric(df['Cost per 4 persons'], errors='coerce')
 
-# Display summary statistics
-print(df.describe())
-import random
-import pandas as pd
-
-# Step 5: Filter dishes by season
+# Step 3: Filter dishes by season
 def filter_dishes_by_season(df, season='All Seasons'):
     """Filters the dataset based on the season."""
-    # Filter by the specified season (Summer, Winter, All Seasons)
     filtered_df = df[df['Season'].isin([season, 'All Seasons'])]
     return filtered_df
 
-# Step 6: Generate weekly menu based on constraints
+# Step 4: Generate weekly menu based on constraints
 def generate_weekly_menu(df, season='All Seasons', budget=6000):
     """Generates a weekly menu based on user preferences."""
-
-    # Filter dishes by the selected season
     available_dishes = filter_dishes_by_season(df, season)
 
-    # Constraints for the weekly menu
     required_categories = {
         'meat': 1,
         'chicken': 2,
@@ -60,17 +46,13 @@ def generate_weekly_menu(df, season='All Seasons', budget=6000):
     weekly_menu = []
     total_cost = 0
 
-    # Generate the menu by selecting dishes that meet the constraints
     for category, count in required_categories.items():
-        # Special handling for 'outing' category
         if category == 'outing':
             category_dishes = available_dishes[available_dishes['Home Made / Outing'].str.contains('Outing', case=False)]
         else:
-            # Filter dishes based on the sub-category (for all other categories)
             category_dishes = available_dishes[available_dishes['Sub-Category'].str.contains(category, case=False)]
 
         if len(category_dishes) > 0:
-            # If there are dishes in the category, select as many as possible
             selected_dishes = random.sample(list(category_dishes.iterrows()), min(count, len(category_dishes)))
             for dish in selected_dishes:
                 dish_info = dish[1]
@@ -79,43 +61,33 @@ def generate_weekly_menu(df, season='All Seasons', budget=6000):
         else:
             print(f"Not enough dishes found in category '{category}'. Adding none.")
 
-    # If the total cost exceeds the budget, we can suggest reducing high-cost items or adjust constraints
     if total_cost > budget:
         print(f"Total cost ({total_cost}) exceeds budget. Try adjusting your criteria.")
-        # Example of dynamic adjustment: remove expensive dishes or reduce number of items
-        # Try reducing the cost by taking fewer dishes (example: reduce number of chicken or sabzi)
-        weekly_menu = []  # Clear the current menu to attempt adjustment
+        weekly_menu = []  # Clear menu and try reducing cost
         total_cost = 0
         for category, count in required_categories.items():
             category_dishes = available_dishes[available_dishes['Sub-Category'].str.contains(category, case=False)]
-
-            # Sort dishes by cost (lowest first) for budget-friendly selection
             category_dishes_sorted = category_dishes.sort_values(by='Cost per 4 persons')
-
             selected_dishes = random.sample(list(category_dishes_sorted.iterrows()), min(count, len(category_dishes_sorted)))
             for dish in selected_dishes:
                 dish_info = dish[1]
                 weekly_menu.append(dish_info['Name'])
                 total_cost += dish_info['Cost per 4 persons']
-
-            if total_cost > budget:  # If the total cost still exceeds, stop and return the result
+            if total_cost > budget:
                 break
 
     return weekly_menu, total_cost
 
-
 # Example Usage
-season = 'Summer'  # Can be 'Summer', 'Winter', or 'All Seasons'
-budget = 6000  # Monthly budget in PKR
+season = 'Summer'
+budget = 6000
 
-# Generate weekly menu
 menu, total_cost = generate_weekly_menu(df, season, budget)
 
-# Print the weekly menu and total cost
 print("Weekly Menu:")
 if isinstance(menu, list):
     for dish in menu:
         print(f"- {dish}")
 else:
     print(menu)
-print(f"Total Cost: {total_cost} PKR")
+print(f"Total Cost: {total_cost} PKR")