Update app.py

app.py

@@ -56,38 +56,18 @@ nutritional_data = {
     }
 }
 
-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_nutrition_in_city(city, target_calories, target_protein, max_cost):
+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: Minimize cost
-    c = costs
+    # Objective function: Maximize nutritional value (calories + protein)
+    c = [-1 * (cal + prot) for cal, prot in zip(calories, proteins)]  # Minimize negative of nutrition for maximization
     
-    # Inequality constraints (A_ub @ x <= b_ub)
-    A_ub = [
-        [-cal for cal in calories],  # -calories to ensure sum(calories * x) >= target_calories
-        [-prot for prot in proteins],  # -protein to ensure sum(proteins * x) >= target_protein
-        costs  # to ensure sum(costs * x) <= max_cost
-    ]
-    b_ub = [-target_calories, -target_protein, max_cost]
+    # 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]
@@ -98,11 +78,11 @@ def optimize_nutrition_in_city(city, target_calories, target_protein, max_cost):
     if result.success:
         selected_dishes = [dish for i, dish in enumerate(nutritional_data) if result.x[i] > 1e-5]
         quantities = result.x
-        total_cost = result.fun
-        total_calories = sum(cal * q for cal, q in zip(calories, quantities))
-        total_protein = sum(prot * q for prot, q in zip(proteins, quantities))
+        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"### Selected Dishes in {city} (Quantities):\n"
+        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"
@@ -113,50 +93,30 @@ def optimize_nutrition_in_city(city, target_calories, target_protein, max_cost):
         
         return result_str
     else:
-        return f"No feasible solution found for {city} that meets the nutritional goals within the cost constraints."
+        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("# Cost vs. Nutrition Trade-off Optimization in Different Cities")
+        gr.Markdown("# Daily Budget Optimization for Best Nutrition")
         
-        # User inputs for city, nutritional targets, and cost limit
+        # User inputs for city and daily budget
         city_selector = gr.Dropdown(choices=cities, label="Select City")
-        energy_target = gr.Number(label="Target Energy (kcal)", value=500)
-        protein_target = gr.Number(label="Target Protein (g)", value=25)
-        cost_limit = gr.Number(label="Maximum Cost (₹)", value=300)
-        
-        toggle_dishes_button = gr.Button("Display Dishes")
-        optimize_button = gr.Button("Optimize")
+        budget_input = gr.Number(label="Daily Budget (₹)", value=300)
         
-        dishes_output = gr.Markdown(label="Available Dishes")
+        optimize_button = gr.Button("Optimize Nutrition")
         optimization_output = gr.Markdown(label="Optimization Results")
         
-        # Function to toggle the display of dishes
-        dishes_visible = False
-        
-        def toggle_dishes(city):
-            nonlocal dishes_visible
-            dishes_visible = not dishes_visible
-            if dishes_visible:
-                toggle_dishes_button.label = "Hide Dishes"
-                return display_dishes_in_city(city)
-            else:
-                toggle_dishes_button.label = "Display Dishes"
-                return ""
-        
         # Function to handle optimization
-        def run_optimization(city, target_calories, target_protein, max_cost):
-            return optimize_nutrition_in_city(city, target_calories, target_protein, max_cost)
+        def run_optimization(city, daily_budget):
+            return optimize_dishes_for_budget(city, daily_budget)
         
-        toggle_dishes_button.click(fn=toggle_dishes, inputs=[city_selector], outputs=dishes_output)
-        optimize_button.click(fn=run_optimization, inputs=[city_selector, energy_target, protein_target, cost_limit], outputs=optimization_output)
+        optimize_button.click(fn=run_optimization, inputs=[city_selector, budget_input], outputs=optimization_output)
         
-        gr.Row([city_selector, energy_target, protein_target, cost_limit])
-        gr.Row(toggle_dishes_button, optimize_button)
-        gr.Row(dishes_output)
+        gr.Row([city_selector, budget_input])
+        gr.Row(optimize_button)
         gr.Row(optimization_output)
     
     return demo