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ea78b1e bc46ae4 ea78b1e bc46ae4 ea78b1e bc46ae4 ea78b1e bc46ae4 ea78b1e bc46ae4 ea78b1e bc46ae4 ea78b1e bc46ae4 ea78b1e bc46ae4 ea78b1e bc46ae4 ea78b1e bc46ae4 ea78b1e bc46ae4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | # app.py
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
import gradio as gr
import matplotlib.pyplot as plt
# -------------------------------------------
# 1️⃣ Create sample dataset
# -------------------------------------------
np.random.seed(42)
num_samples = 200
distance = np.random.uniform(1, 30, num_samples)
order_size = np.random.randint(1, 10, num_samples)
hour_of_day = np.random.randint(8, 23, num_samples)
# delivery_time = base + 1.2*distance + 2*order_size + 0.5*hour + noise
noise = np.random.normal(0, 5, num_samples)
delivery_time = 5 + 1.2*distance + 2*order_size + 0.5*hour_of_day + noise
df = pd.DataFrame({
"distance": distance,
"order_size": order_size,
"hour_of_day": hour_of_day,
"delivery_time": delivery_time
})
# -------------------------------------------
# 2️⃣ Train linear regression model
# -------------------------------------------
X = df[["distance", "order_size", "hour_of_day"]]
y = df["delivery_time"]
model = LinearRegression()
model.fit(X, y)
# -------------------------------------------
# 3️⃣ Define prediction + graph function
# -------------------------------------------
def predict_and_plot(distance, order_size, hour_of_day):
# Predict delivery time
features = np.array([[distance, order_size, hour_of_day]])
prediction = model.predict(features)[0]
# Create graph: vary distance from 1 to 30, keep other inputs fixed
distances = np.linspace(1, 30, 100)
inputs = np.column_stack((distances, np.full(100, order_size), np.full(100, hour_of_day)))
predicted_times = model.predict(inputs)
# Plot
plt.figure(figsize=(6, 4))
plt.plot(distances, predicted_times, label='Predicted delivery time', color='blue')
plt.scatter([distance], [prediction], color='red', label='Your input', zorder=5)
plt.xlabel('Distance (km)')
plt.ylabel('Estimated delivery time (minutes)')
plt.title('Delivery Time vs Distance')
plt.legend()
plt.tight_layout()
# Save plot to file
plot_path = "delivery_plot.png"
plt.savefig(plot_path)
plt.close()
return f"⏱️ Estimated delivery time: {prediction:.2f} minutes", plot_path
# -------------------------------------------
# 4️⃣ Gradio interface
# -------------------------------------------
iface = gr.Interface(
fn=predict_and_plot,
inputs=[
gr.Number(label="Distance (km)", value=5),
gr.Number(label="Order Size (number of items)", value=3),
gr.Number(label="Hour of Day (24h, e.g., 14 for 2 PM)", value=12)
],
outputs=[
gr.Text(label="Prediction"),
gr.Image(label="Delivery Time vs Distance Graph")
],
title="🚚 Delivery Time Estimator with Graph",
description="Predicts delivery time and shows how it changes with distance (using linear regression)."
)
# -------------------------------------------
# 5️⃣ Launch app
# -------------------------------------------
if __name__ == "__main__":
iface.launch()
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