Update app.py

app.py

@@ -1,27 +1,26 @@
-# delivery_time_estimator.py
+# 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
 # -------------------------------------------
 
-# Let's say delivery time depends on distance (km), order size (items), and hour of day
 np.random.seed(42)
 num_samples = 200
 
-distance = np.random.uniform(1, 30, num_samples)          # 1–30 km
-order_size = np.random.randint(1, 10, num_samples)        # 1–9 items
-hour_of_day = np.random.randint(8, 23, num_samples)       # deliveries between 8 AM and 10 PM
+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)
 
-# Hypothetical formula: delivery_time = 5 + 1.2*distance + 2*order_size + 0.5*hour + noise
+# 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
 
-# Build DataFrame
 df = pd.DataFrame({
     "distance": distance,
     "order_size": order_size,
@@ -40,29 +39,57 @@ model = LinearRegression()
 model.fit(X, y)
 
 # -------------------------------------------
-# 3️⃣ Define Gradio interface
+# 3️⃣ Define prediction + graph function
 # -------------------------------------------
 
-def predict_delivery_time(distance, order_size, hour_of_day):
+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]
-    return f"⏱️ Estimated delivery time: {prediction:.2f} minutes"
 
-# Create Gradio interface
+    # 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_delivery_time,
+    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 format, e.g., 14 for 2 PM)", value=12)
+        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")
     ],
-    outputs="text",
-    title="🚚 Delivery Time Estimator",
-    description="Enter distance, order size, and hour to predict estimated delivery time using linear regression."
+    title="🚚 Delivery Time Estimator with Graph",
+    description="Predicts delivery time and shows how it changes with distance (using linear regression)."
 )
 
 # -------------------------------------------
-# 4️⃣ Launch app (locally or on Hugging Face Spaces)
+# 5️⃣ Launch app
 # -------------------------------------------
 
 if __name__ == "__main__":