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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, validator
import pandas as pd
import pickle, uvicorn, os, logging
app = FastAPI()
# Configure logging
logging.basicConfig(level=logging.INFO)
# Define filepath for ml_components.pkl
ML_COMPONENTS_FILEPATH = os.path.join("assets", "ml", "ml_components.pkl")
# Load machine learning model and other components
with open(ML_COMPONENTS_FILEPATH, "rb") as file:
ml_components = pickle.load(file)
# preprocessor = ml_components["preprocessor"]
pipeline = ml_components["pipeline"]
class DeviceSpecs(BaseModel):
"""
Device specifications.
- battery_power: Total energy a battery can store in one time measured in mAh
- blue: Has Bluetooth or not (0 for False, 1 for True)
- clock_speed: The speed at which the microprocessor executes instructions
- dual_sim: Has dual sim support or not (0 for False, 1 for True)
- fc: Front Camera megapixels
- four_g: Has 4G or not (0 for False, 1 for True)
- int_memory: Internal Memory in Gigabytes
- m_dep: Mobile Depth in cm
- mobile_wt: Weight of mobile phone
- n_cores: Number of cores of the processor
- pc: Primary Camera megapixels
- px_height: Pixel Resolution Height
- px_width: Pixel Resolution Width
- ram: Random Access Memory in Megabytes
- sc_h: Screen Height of mobile in cm
- sc_w: Screen Width of mobile in cm
- talk_time: longest time that a single battery charge will last when you are
- three_g: Has 3G or not (0 for False, 1 for True)
- touch_screen: Has touch screen or not (0 for False, 1 for True)
- wifi: Has wifi or not (0 for False, 1 for True)
"""
battery_power: float
blue: int
clock_speed: float
dual_sim: int
fc: float
four_g: int
int_memory: float
m_dep: float
mobile_wt: float
n_cores: float
pc: float
px_height: float
px_width: float
ram: float
sc_h: float
sc_w: float
talk_time: float
three_g: int
touch_screen: int
wifi: int
@validator("blue", "dual_sim", "four_g", "three_g", "touch_screen", "wifi")
def validate_boolean(cls, v):
# Ensure the values are either 0 or 1
if v not in (0, 1):
raise ValueError("Value must be 0 or 1")
return v
@app.post("/predict/{device_id}")
async def predict_price(device_id: int, specs: DeviceSpecs):
"""
Predict the price of a device based on its specifications.
Args:
device_id (int): The ID of the device.
specs (DeviceSpecs): The device specifications.
Returns:
dict: A dictionary containing the input data and predicted price.
"""
try:
logging.info(f"Input request received...")
# Preprocess the data
data = pd.DataFrame([{"device_id": device_id, **specs.dict()}])
logging.info(f"Input as a dataframe\n{data.to_markdown()}\n")
# Predict price
data["predicted_price"] = pipeline.predict(data)
logging.info(
f"Predictions made\n{data[['device_id', 'predicted_price']].to_markdown()}\n"
)
# Return input data and predicted price
return data.to_dict("records")
except Exception as e:
logging.error(
f"An error occurred while processing prediction for device ID {device_id}: {str(e)}"
)
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
uvicorn.run(app, host="127.0.0.1", port=8000, reload=True)
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