Spaces:
Runtime error
Runtime error
File size: 2,310 Bytes
8cfefb8 633fb1d 8cfefb8 |
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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
import pandas as pd
import numpy as np
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
from sklearn.preprocessing import MinMaxScaler
app = FastAPI()
class StockRequest(BaseModel):
stock_name: str
# Define file paths (assuming the structure is already known)
STOCK_FILE_PATHS = {
"TSLA": "TSLA_data.csv",
"AAPL": "AAPL_data.csv",
"AMZN": "AMZN_data.csv",
"MSFT": "MSFT_data.csv",
}
def load_and_preprocess_data(filepath):
df = pd.read_csv(filepath)
df['Close'] = df['Close'].astype(float) # Ensure data is float
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(df['Close'].values.reshape(-1, 1))
return scaled_data, scaler
def prepare_datasets(data, seq_len):
x, y = [], []
for i in range(seq_len, len(data)):
x.append(data[i-seq_len:i, 0])
y.append(data[i, 0])
x, y = np.array(x), np.array(y)
x = np.reshape(x, (x.shape[0], x.shape[1], 1))
return x, y
@app.post("/LSTM_Predict")
async def predict(stock_request: StockRequest):
stock_name = stock_request.stock_name
if stock_name not in STOCK_FILE_PATHS:
raise HTTPException(status_code=422, detail="Invalid Stock Name")
filepath = STOCK_FILE_PATHS[stock_name]
data, scaler = load_and_preprocess_data(filepath)
seq_len = 60 # Sequence length for LSTM
x_train, y_train = prepare_datasets(data[:int(0.8 * len(data))], seq_len)
x_test = prepare_datasets(data[int(0.8 * len(data)):], seq_len)[0] # Only x_test needed
# Define and compile the model
model = Sequential([
LSTM(128, return_sequences=True, input_shape=(seq_len, 1)),
Dropout(0.2),
LSTM(64, return_sequences=False),
Dropout(0.2),
Dense(1)
])
model.compile(optimizer='adam', loss='mean_squared_error')
# Fit model
model.fit(x_train, y_train, epochs=5, batch_size=32)
# Predicting
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions) # Inverse transform to get actual values
predict_prices = predictions.flatten().tolist() # Convert to list for JSON response
return {"prediction": predict_prices}
|