Spaces:
Sleeping
Sleeping
File size: 1,839 Bytes
dd8d615 |
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 |
from fastapi import FastAPI, HTTPException, APIRouter
import pandas as pd
import numpy as np
from keras.models import load_model
from pydantic import BaseModel
# Load the model
model = load_model("app/lstm_model.h5")
# Define the FastAPI app
router = APIRouter()
class PredictionInput(BaseModel):
input_data: dict
def preprocess_and_predict(input_data):
input_data = input_data.drop(columns=['COMPANY', 'PRICE OF LAST TRANSACTION'])
# Load the pre-trained model
timesteps = model.input_shape[1]
features = model.input_shape[2]
# Ensure input_data has the correct number of features
input_data = input_data.iloc[:, :features]
# Handle missing values and normalize
input_data = input_data.fillna(0)
max_value = input_data.max().max()
input_data_normalized = input_data / max_value
# Check if there are enough rows for timesteps
if len(input_data) < timesteps:
raise ValueError(f"Input data must have at least {timesteps} rows for prediction.")
# Reshape the data
input_data_reshaped = np.array([input_data_normalized.values[-timesteps:]])
input_data_reshaped = input_data_reshaped.reshape(1, timesteps, features)
# Predict
predictions = model.predict(input_data_reshaped)
predictions_denormalized = predictions * max_value
return predictions_denormalized.round()[0][0]
# API endpoint
@router.post("/predict/")
async def predict(payload: PredictionInput):
try:
input_data = payload.input_data
dataframe = pd.DataFrame.from_dict(input_data)
return {"prediction": preprocess_and_predict(input_data=dataframe)}
except ValueError as e:
raise HTTPException(status_code=422, detail=str(e))
except Exception as e:
raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}") |