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import pandas as pd
import numpy as np
import gradio as gr
from utils import distcalculate
from model import prepare_data, train_models
# Load and preprocess data
df = pd.read_csv("deliverytime.txt")
# Calculate distance for each row using the haversine formula
df["distance"] = [
distcalculate(row["Restaurant_latitude"], row["Restaurant_longitude"],
row["Delivery_location_latitude"], row["Delivery_location_longitude"])
for _, row in df.iterrows()
]
# Prepare training and test data
xtrain, xtest, ytrain, ytest = prepare_data(df)
# Train the models and select Gradient Boosting
models = train_models(xtrain, ytrain)
gb_model = models["GradientBoosting"] # Correct key name
# Prediction function
def predict_time_taken(age, rating, distance):
input_data = np.array([[age, rating, distance]])
prediction = gb_model.predict(input_data)
return f"{prediction[0]:.2f} minutes"
# Gradio interface
demo = gr.Interface(
fn=predict_time_taken,
inputs=[
gr.Number(label="Delivery Person Age", value=30),
gr.Slider(minimum=1.0, maximum=5.0, step=0.1, label="Delivery Person Rating", value=4.5),
gr.Number(label="Distance (km)", value=3.0)
],
outputs="text",
title="Food Delivery Time Predictor",
description="Enter delivery details to predict the estimated delivery time.",
)
# Run the app
if __name__ == "__main__":
demo.launch() |