Update app.py

app.py

@@ -5,40 +5,60 @@ 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, "Cost": 50
+        "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, "Cost": 60
+        "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, "Cost": 150
+        "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, "Cost": 100
+        "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, "Cost": 30
+        "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, "Cost": 80
+        "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, "Cost": 50
+        "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, "Cost": 60
+        "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, "Cost": 70
+        "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, "Cost": 40
+        "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 optimize_nutrition(target_calories, target_protein, max_cost):
-    # Extracting cost, calories, and protein data
-    costs = [nutritional_data[dish]["Cost"] for dish in nutritional_data]
+def optimize_nutrition_in_city(city, target_calories, target_protein, max_cost):
+    # 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]
     
@@ -46,7 +66,6 @@ def optimize_nutrition(target_calories, target_protein, max_cost):
     c = costs
     
     # Inequality constraints (A_ub @ x <= b_ub)
-    # Ensuring that the total calories and protein are at least the target values
     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
@@ -67,7 +86,7 @@ def optimize_nutrition(target_calories, target_protein, max_cost):
         total_calories = sum(cal * q for cal, q in zip(calories, quantities))
         total_protein = sum(prot * q for prot, q in zip(proteins, quantities))
         
-        result_str = f"### Selected Dishes (Quantities):\n"
+        result_str = f"### Selected Dishes in {city} (Quantities):\n"
         for dish, qty in zip(selected_dishes, quantities):
             if qty > 1e-5:
                 result_str += f"- **{dish}**: {qty:.2f} portions\n"
@@ -78,14 +97,17 @@ def optimize_nutrition(target_calories, target_protein, max_cost):
         
         return result_str
     else:
-        return "No feasible solution found that meets the nutritional goals within the cost constraints."
+        return f"No feasible solution found for {city} that meets the nutritional goals within the cost constraints."
 
 # Gradio Interface
 def create_interface():
+    cities = ["Chennai", "Bengaluru", "Hyderabad", "New Delhi"]
+    
     with gr.Blocks() as demo:
-        gr.Markdown("# Cost vs. Nutrition Trade-off Optimization")
+        gr.Markdown("# Cost vs. Nutrition Trade-off Optimization in Different Cities")
         
-        # User inputs for nutritional targets and cost limit
+        # User inputs for city, nutritional targets, and cost limit
+        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)
@@ -94,12 +116,12 @@ def create_interface():
         optimization_output = gr.Markdown(label="Optimization Results")
         
         # Function to handle optimization
-        def run_optimization(target_calories, target_protein, max_cost):
-            return optimize_nutrition(target_calories, target_protein, max_cost)
+        def run_optimization(city, target_calories, target_protein, max_cost):
+            return optimize_nutrition_in_city(city, target_calories, target_protein, max_cost)
         
-        optimize_button.click(fn=run_optimization, inputs=[energy_target, protein_target, cost_limit], outputs=optimization_output)
+        optimize_button.click(fn=run_optimization, inputs=[city_selector, energy_target, protein_target, cost_limit], outputs=optimization_output)
         
-        gr.Row([energy_target, protein_target, cost_limit])
+        gr.Row([city_selector, energy_target, protein_target, cost_limit])
         gr.Row(optimize_button, optimization_output)
     
     return demo