app.py

import gradio as gr
import pickle
import numpy as np

# Load your model (update the path to your downloaded model)
model_path = "model.pkl"  # Change this to the path of your trained model
with open(model_path, 'rb') as f:
    model = pickle.load(f)

# Function to make predictions
def predict_calories(age, height, weight, gender, job_type, goal):
    # You can customize this function based on how you trained the model
    # Convert input features into a numpy array or data structure required by the model
    # Here's a basic example assuming your model expects numeric features
    
    gender_map = {"muž": 0, "žena": 1}  # Example gender encoding
    job_type_map = {
        'Študent': 0,
        'Sedavé': 1,
        'Málo pohybu (do 2500 krokov)': 2,
        'Stredne pohybu (do 5000 krokov)': 3,
        'Veľa pohybu (do 10000 krokov)': 4,
    }

    # Prepare the input data in the format your model expects
    input_data = np.array([[age, height, weight, gender_map.get(gender, 0), job_type_map.get(job_type, 0), goal]])

    # Predict using the loaded model
    prediction = model.predict(input_data)
    return f"Recommended Daily Calories: {prediction[0]:.2f} kcal"

# Define Gradio interface
interface = gr.Interface(
    fn=predict_calories,
    inputs=[
        gr.Slider(minimum=18, maximum=100, label="Age", default=25),
        gr.Slider(minimum=120, maximum=250, label="Height (cm)", default=170),
        gr.Slider(minimum=30, maximum=200, label="Weight (kg)", default=70),
        gr.Radio(["muž", "žena"], label="Gender", default="muž"),
        gr.Dropdown(
            ["Študent", "Sedavé", "Málo pohybu (do 2500 krokov)", "Stredne pohybu (do 5000 krokov)", "Veľa pohybu (do 10000 krokov)"],
            label="Job Type",
            default="Sedavé"
        ),
        gr.Radio(["Znížiť váhu", "Udržať váhu", "Pribrať na váhe"], label="Goal", default="Udržať váhu"),
    ],
    outputs="text",
    live=True,
)

# Launch the interface
interface.launch()