test

tests/test_api_predict.py

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+import pytest
+import pandas as pd
+import requests
+
+# Define base URL for the FastAPI application
+BASE_URL = "http://127.0.0.1:8000"
+
+# Define sample device specifications
+device_specs = {
+    "battery_power": 3000,
+    "blue": 1,
+    "clock_speed": 2.0,
+    "dual_sim": 0,
+    "fc": 5.0,
+    "four_g": 1,
+    "int_memory": 64.0,
+    "m_dep": 0.4,
+    "mobile_wt": 150.0,
+    "n_cores": 8.0,
+    "pc": 12.0,
+    "px_height": 1920.0,
+    "px_width": 1080.0,
+    "ram": 4.0,
+    "sc_h": 5.5,
+    "sc_w": 2.5,
+    "talk_time": 10.0,
+    "three_g": 1,
+    "touch_screen": 1,
+    "wifi": 1
+}
+
+
+def test_predict_price():
+    """
+    Test the predict price endpoint.
+
+    Steps:
+    1. Define device specifications and device ID.
+    2. Send a POST request to the predict price endpoint with the device specifications.
+    3. Validate that the response status code is 200 (OK).
+    4. Parse the response JSON and validate that it contains the expected fields.
+    5. Print the predicted price.
+    """
+    # Define device ID
+    device_id = 1
+
+    # Send POST request to predict price
+    response = requests.post(f"{BASE_URL}/predict/{device_id}", json=device_specs)
+
+    # Check if request was successful (status code 200)
+    assert response.status_code == 200
+
+    # Parse response JSON
+    data = pd.DataFrame(response.json())
+
+    # Validate response fields
+    assert "device_id" in data.columns
+    assert "predicted_price" in data.columns
+
+    # Print predicted price
+    print(f"Predictions\n{data[['device_id', 'predicted_price']].to_markdown()} ")
+
+
+if __name__ == "__main__":
+    pytest.main()