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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() | |