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import gradio as gr
import pandas as pd
import numpy as np
import yfinance as yf
from sklearn.preprocessing import MinMaxScaler
from tensorflow import keras
# Load your trained model
model = keras.models.load_model('your_model.h5') # Ensure this path is correct
# Function to predict stock prices
def predict_stock_price(stock_ticker, start_date, end_date):
# Fetch data
data = yf.download(stock_ticker, start=start_date, end=end_date)
# Check if data is returned
if data.empty:
return "No data available for the selected dates."
# Preprocess data
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))
# Prepare input for the model
input_data = scaled_data[-60:] # Use the last 60 days of data
input_data = input_data.reshape((1, input_data.shape[0], 1))
# Predict stock prices
prediction = model.predict(input_data)
predicted_price = scaler.inverse_transform(prediction) # Rescale back to original price
return f"Predicted stock price for tomorrow: ${predicted_price[0][0]:.2f}"
# Create the Gradio interface
stock_ticker_input = gr.Dropdown(
choices=["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA"], # Add more tickers as needed
label="Select Stock Ticker"
)
start_date_input = gr.Date(label="Start Date")
end_date_input = gr.Date(label="End Date")
iface = gr.Interface(
fn=predict_stock_price,
inputs=[
stock_ticker_input,
start_date_input,
end_date_input
],
outputs="text",
title="Stock Price Prediction App",
description="Enter the stock ticker and date range to predict the stock price for tomorrow."
)
# Launch the Gradio app
iface.launch() |