Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from scipy.optimize import linprog
+
+# Data: Nutritional Information 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": 40
+    },
+    "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
+    },
+    "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
+    },
+    "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
+    },
+    "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": 20
+    },
+    "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
+    },
+    "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": 70
+    },
+    "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": 50
+    },
+    "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": 40
+    },
+    "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": 30
+    }
+}
+
+def optimize_nutrition(target_calories, target_protein, max_cost):
+    """Optimizes the selection of dishes to meet nutritional goals."""
+    # Objective: Minimize the total deviation from nutritional goals
+    # Constraints: Total cost <= max_cost, total nutritional values should try to meet or exceed targets
+
+    dishes = list(nutritional_data.keys())
+    
+    # Coefficients for the linear programming (negative values for maximization)
+    c = [-nutritional_data[dish]['Energy (kcal)'] for dish in dishes] + \
+        [-nutritional_data[dish]['Protein (g)'] for dish in dishes]
+    
+    # Inequality constraints matrix
+    A_ub = [
+        [nutritional_data[dish]['Energy (kcal)'] for dish in dishes] + \
+        [0 for _ in dishes],  # Total calories
+        [0 for _ in dishes] + [nutritional_data[dish]['Protein (g)'] for dish in dishes]  # Total protein
+    ]
+    
+    # Inequality constraints vector
+    b_ub = [target_calories, target_protein]
+    
+    # Equality constraints (cost)
+    A_eq = [[nutritional_data[dish]['Cost'] for dish in dishes]]
+    b_eq = [max_cost]
+    
+    # Bounds for decision variables (dish quantities, we assume only integers are allowed)
+    bounds = [(0, 1) for _ in range(len(dishes) * 2)]
+    
+    # Linear programming to optimize the nutritional intake
+    result = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq=b_eq, bounds=bounds, method='simplex')
+    
+    if result.success:
+        quantities = result.x[:len(dishes)]
+        selected_dishes = {dishes[i]: quantities[i] for i in range(len(dishes)) if quantities[i] > 0.1}
+        return f"Selected Dishes: {selected_dishes}"
+    else:
+        return "No optimal solution found with the given constraints."
+
+# Gradio Interface
+def create_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Nutritional Optimization Tool")
+        
+        # Input widgets for nutritional targets
+        target_calories = gr.Number(label="Target Calories", value=2000)
+        target_protein = gr.Number(label="Target Protein (g)", value=50)
+        max_cost = gr.Number(label="Maximum Cost (₹)", value=500)
+        
+        # Output widget
+        optimization_output = gr.Textbox(label="Optimization Result")
+        
+        # Optimization function to run on button click
+        def run_optimization(target_calories, target_protein, max_cost):
+            return optimize_nutrition(target_calories, target_protein, max_cost)
+        
+        optimize_button = gr.Button("Optimize")
+        optimize_button.click(fn=run_optimization, inputs=[target_calories, target_protein, max_cost], outputs=optimization_output)
+        
+        gr.Row(target_calories, target_protein, max_cost, optimize_button, optimization_output)
+    
+    return demo
+
+# Launch the interface
+demo = create_interface()
+demo.launch(share=True)