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| import gradio as gr | |
| import numpy as np | |
| from scipy.optimize import linprog | |
| # Example data: Nutritional Information and Cost for the healthiest dishes | |
| nutritional_data = { | |
| "Idli with Vegetable Sambar": { | |
| "Energy (kcal)": 100, "Protein (g)": 5.5, "Fat (g)": 1.5, "Carbohydrate (g)": 17.0, "Fiber (g)": 3.0, | |
| "Calcium (mg)": 35, "Iron (mg)": 1.0, "Vitamin C (mg)": 8.0, | |
| "Chennai": 50, "Bengaluru": 55, "Hyderabad": 50, "New Delhi": 60 | |
| }, | |
| "Khichdi": { | |
| "Energy (kcal)": 120, "Protein (g)": 4.0, "Fat (g)": 2.5, "Carbohydrate (g)": 20.0, "Fiber (g)": 2.5, | |
| "Calcium (mg)": 40, "Iron (mg)": 1.0, "Vitamin C (mg)": 5.0, | |
| "Chennai": 60, "Bengaluru": 65, "Hyderabad": 60, "New Delhi": 70 | |
| }, | |
| "Tandoori Chicken": { | |
| "Energy (kcal)": 150, "Protein (g)": 18.0, "Fat (g)": 7.0, "Carbohydrate (g)": 3.0, "Fiber (g)": 0.5, | |
| "Calcium (mg)": 15, "Iron (mg)": 1.2, "Vitamin C (mg)": 1.5, | |
| "Chennai": 150, "Bengaluru": 160, "Hyderabad": 150, "New Delhi": 180 | |
| }, | |
| "Palak Paneer": { | |
| "Energy (kcal)": 140, "Protein (g)": 7.5, "Fat (g)": 10.0, "Carbohydrate (g)": 6.0, "Fiber (g)": 3.0, | |
| "Calcium (mg)": 200, "Iron (mg)": 3.0, "Vitamin C (mg)": 15.0, | |
| "Chennai": 100, "Bengaluru": 110, "Hyderabad": 100, "New Delhi": 120 | |
| }, | |
| "Raita": { | |
| "Energy (kcal)": 60, "Protein (g)": 3.5, "Fat (g)": 2.0, "Carbohydrate (g)": 6.5, "Fiber (g)": 0.5, | |
| "Calcium (mg)": 100, "Iron (mg)": 0.5, "Vitamin C (mg)": 2.0, | |
| "Chennai": 30, "Bengaluru": 35, "Hyderabad": 30, "New Delhi": 40 | |
| }, | |
| "Rajma": { | |
| "Energy (kcal)": 140, "Protein (g)": 7.5, "Fat (g)": 5.0, "Carbohydrate (g)": 20.0, "Fiber (g)": 6.0, | |
| "Calcium (mg)": 50, "Iron (mg)": 3.5, "Vitamin C (mg)": 4.0, | |
| "Chennai": 80, "Bengaluru": 90, "Hyderabad": 80, "New Delhi": 100 | |
| }, | |
| "Baingan Bharta": { | |
| "Energy (kcal)": 70, "Protein (g)": 2.5, "Fat (g)": 3.0, "Carbohydrate (g)": 10.0, "Fiber (g)": 4.0, | |
| "Calcium (mg)": 30, "Iron (mg)": 0.7, "Vitamin C (mg)": 6.0, | |
| "Chennai": 50, "Bengaluru": 55, "Hyderabad": 50, "New Delhi": 60 | |
| }, | |
| "Besan Chilla": { | |
| "Energy (kcal)": 180, "Protein (g)": 8.0, "Fat (g)": 7.0, "Carbohydrate (g)": 20.0, "Fiber (g)": 5.0, | |
| "Calcium (mg)": 30, "Iron (mg)": 2.5, "Vitamin C (mg)": 1.0, | |
| "Chennai": 60, "Bengaluru": 65, "Hyderabad": 60, "New Delhi": 70 | |
| }, | |
| "Masoor Dal": { | |
| "Energy (kcal)": 110, "Protein (g)": 7.5, "Fat (g)": 2.0, "Carbohydrate (g)": 16.0, "Fiber (g)": 5.5, | |
| "Calcium (mg)": 25, "Iron (mg)": 3.0, "Vitamin C (mg)": 4.0, | |
| "Chennai": 70, "Bengaluru": 75, "Hyderabad": 70, "New Delhi": 80 | |
| }, | |
| "Upma": { | |
| "Energy (kcal)": 150, "Protein (g)": 4.0, "Fat (g)": 6.0, "Carbohydrate (g)": 25.0, "Fiber (g)": 3.0, | |
| "Calcium (mg)": 30, "Iron (mg)": 1.0, "Vitamin C (mg)": 3.0, | |
| "Chennai": 40, "Bengaluru": 45, "Hyderabad": 40, "New Delhi": 50 | |
| } | |
| } | |
| def display_dishes_in_city(city): | |
| """Displays all dishes available in the selected city with their nutritional information and cost.""" | |
| result_str = f"### Available Dishes in {city}:\n" | |
| for dish, info in nutritional_data.items(): | |
| result_str += f"- **{dish}**\n" | |
| result_str += f" - Cost: ₹{info[city]}\n" | |
| result_str += f" - Energy: {info['Energy (kcal)']} kcal\n" | |
| result_str += f" - Protein: {info['Protein (g)']} g\n" | |
| result_str += f" - Fat: {info['Fat (g)']} g\n" | |
| result_str += f" - Carbohydrate: {info['Carbohydrate (g)']} g\n" | |
| result_str += f" - Fiber: {info['Fiber (g)']} g\n" | |
| result_str += f" - Calcium: {info['Calcium (mg)']} mg\n" | |
| result_str += f" - Iron: {info['Iron (mg)']} mg\n" | |
| result_str += f" - Vitamin C: {info['Vitamin C (mg)']} mg\n\n" | |
| return result_str | |
| def optimize_dishes_for_budget(city, daily_budget): | |
| # Extracting cost, calories, and protein data for the selected city | |
| costs = [nutritional_data[dish][city] for dish in nutritional_data] | |
| calories = [nutritional_data[dish]["Energy (kcal)"] for dish in nutritional_data] | |
| proteins = [nutritional_data[dish]["Protein (g)"] for dish in nutritional_data] | |
| # Objective function: Maximize nutritional value (calories + protein) | |
| c = [-1 * (cal + prot) for cal, prot in zip(calories, proteins)] # Minimize negative of nutrition for maximization | |
| # Constraint: Total cost must not exceed the daily budget | |
| A_ub = [costs] # Sum of costs * portions <= daily_budget | |
| b_ub = [daily_budget] | |
| # Bounds for each dish (x >= 0) | |
| bounds = [(0, None) for _ in costs] | |
| # Solve the optimization problem | |
| result = linprog(c, A_ub=A_ub, b_ub=b_ub, bounds=bounds, method='highs') | |
| if result.success: | |
| selected_dishes = [dish for i, dish in enumerate(nutritional_data) if result.x[i] > 1e-5] | |
| quantities = result.x | |
| total_cost = sum(cost * qty for cost, qty in zip(costs, quantities)) | |
| total_calories = sum(cal * qty for cal, qty in zip(calories, quantities)) | |
| total_protein = sum(prot * qty for prot, qty in zip(proteins, quantities)) | |
| result_str = f"### Best Combination of Dishes for ₹{daily_budget:.2f} in {city}:\n" | |
| for dish, qty in zip(selected_dishes, quantities): | |
| if qty > 1e-5: | |
| result_str += f"- **{dish}**: {qty:.2f} portions\n" | |
| result_str += f"\n### Total Cost: ₹{total_cost:.2f}\n" | |
| result_str += f"### Total Calories: {total_calories:.2f} kcal\n" | |
| result_str += f"### Total Protein: {total_protein:.2f} g\n" | |
| return result_str | |
| else: | |
| return f"No feasible solution found for ₹{daily_budget:.2f} in {city}." | |
| # Gradio Interface | |
| def create_interface(): | |
| cities = ["Chennai", "Bengaluru", "Hyderabad", "New Delhi"] | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Daily Budget Optimization for Best Nutrition") | |
| # User inputs for city and daily budget | |
| city_selector = gr.Dropdown(choices=cities, label="Select City") | |
| budget_input = gr.Number(label="Daily Budget (₹)", value=300) | |
| show_all_dishes_button = gr.Button("Show All Available Dishes") | |
| optimize_button = gr.Button("Optimize Nutrition") | |
| all_dishes_output = gr.Markdown(label="All Available Dishes") | |
| optimization_output = gr.Markdown(label="Optimization Results") | |
| # Function to handle showing all dishes | |
| def show_all_dishes(city): | |
| return display_dishes_in_city(city) | |
| # Function to handle optimization | |
| def run_optimization(city, daily_budget): | |
| return optimize_dishes_for_budget(city, daily_budget) | |
| show_all_dishes_button.click(fn=show_all_dishes, inputs=[city_selector], outputs=all_dishes_output) | |
| optimize_button.click(fn=run_optimization, inputs=[city_selector, budget_input], outputs=optimization_output) | |
| gr.Row([city_selector, budget_input]) | |
| gr.Row(optimize_button) | |
| gr.Row(optimization_output) | |
| gr.Row(show_all_dishes_button) | |
| gr.Row(all_dishes_output) | |
| return demo | |
| # Launch the interface | |
| demo = create_interface() | |
| demo.launch(share=True) | |