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edd7f40 | 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | import gradio as gr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import joblib
from tensorflow import keras
try:
scaler = joblib.load("scaler_minmax.joblib")
lstm_loaded = keras.models.load_model("lstm_stock_model.keras")
print("Loaded OK!")
except Exception as e:
print(f"Load error: {e} — Check files in folder.")
WINDOW_SIZE = 60
def get_last_known(close_prices_list):
mean_close = np.mean(close_prices_list)
return {
"High": mean_close * 1.05, "Low": mean_close * 0.95, "Open": mean_close,
"Log_Volume": np.log(30000000 + 1)
}
def predict_multi_days(csv_file, days_to_predict):
try:
if csv_file is None:
fig, ax = plt.subplots(figsize=(10,5))
ax.text(0.5, 0.5, 'Upload CSV with Close column', ha='center', va='center', transform=ax.transAxes, fontsize=14)
ax.axis('off')
return None, fig, pd.DataFrame({"Error": ["Upload CSV"]})
df = pd.read_csv(csv_file)
close_col = next((col for col in df.columns if 'close' in col.lower()), None)
if close_col is None:
raise ValueError("No 'Close' column found.")
close_prices_series = df[close_col].dropna()
close_prices_list = close_prices_series.tolist()
close_prices = np.array(close_prices_list, dtype=float).reshape(-1, 1)
if len(close_prices) < WINDOW_SIZE:
raise ValueError(f"Need 60+ prices (got {len(close_prices)}).")
last_60 = close_prices[-WINDOW_SIZE:]
last_known = get_last_known(close_prices_list)
other_features = np.tile(list(last_known.values()), (WINDOW_SIZE, 1))
input_data = np.hstack([last_60, other_features])
scaled_input = scaler.transform(input_data)
window_3d = np.expand_dims(scaled_input, axis=0)
predictions = []
current_window = window_3d.copy()
for _ in range(int(days_to_predict)):
pred_scaled = lstm_loaded.predict(current_window, verbose=0)
pred = scaler.inverse_transform(pred_scaled)
next_close = float(pred[0, 0])
predictions.append(next_close)
new_row = np.append([next_close], list(last_known.values()))
current_window = np.roll(current_window, -1, axis=1)
current_window[0, -1, :] = new_row
fig, ax = plt.subplots(figsize=(12,6))
hist_x = range(1, WINDOW_SIZE + 1)
fut_x = range(WINDOW_SIZE + 1, WINDOW_SIZE + len(predictions) + 1)
ax.plot(hist_x, last_60, label="Last 60 Days", color='blue', lw=2)
ax.plot(fut_x, predictions, label=f"Predicted {days_to_predict} Days", color='red', ls='--', lw=2, marker='o')
ax.set_title(f"AAPL Close Prediction ({days_to_predict} Days)")
ax.set_xlabel("Days")
ax.set_ylabel("Close Price ($)")
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
pred_df = pd.DataFrame({
"Day": [f"Day {i+1}" for i in range(len(predictions))],
"Predicted Close": predictions
})
return predictions[0] if predictions else None, fig, pred_df
except Exception as e:
print(f"Error: {e}")
fig, ax = plt.subplots(figsize=(10,5))
ax.text(0.5, 0.5, f'Error: {str(e)}', ha='center', va='center', fontsize=14)
ax.axis('off')
plt.tight_layout()
return None, fig, pd.DataFrame({"Error": [str(e)]})
with gr.Blocks(title="AAPL Multi-Day Predictor") as demo:
gr.Markdown("# AAPL Multi-Day Stock Predictor")
gr.Markdown("Upload CSV with 'Close' column, choose days—get plot + predictions.")
with gr.Row():
csv_input = gr.File(label="Upload CSV", file_types=[".csv"])
days_slider = gr.Slider(1, 90, value=30, step=1, label="Days to Predict")
submit_btn = gr.Button("Predict", variant="primary")
with gr.Row():
pred_num = gr.Number(label="First Day Prediction")
plot_out = gr.Plot(label="Historical + Future")
table_out = gr.Dataframe(label="Predictions")
submit_btn.click(
fn=predict_multi_days,
inputs=[csv_input, days_slider],
outputs=[pred_num, plot_out, table_out]
)
demo.launch(share=True, quiet=True) |