Update app.py

app.py

@@ -1,90 +1,53 @@
-import numpy as np
+import gradio as gr
 import pandas as pd
+import numpy as np
 import yfinance as yf
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import layers
-import gradio as gr
-import matplotlib.pyplot as plt
 from sklearn.preprocessing import MinMaxScaler
-import datetime
-
-# Function to fetch and preprocess data
-def fetch_data(ticker, start_date, end_date):
-    data = yf.download(ticker, start=start_date, end=end_date)
-    data = data[['Close']]
-    return data
-
-# Function to create datasets for training
-def create_dataset(data, time_step=1):
-    X, y = [], []
-    for i in range(len(data) - time_step - 1):
-        a = data[i:(i + time_step), 0]
-        X.append(a)
-        y.append(data[i + time_step, 0])
-    return np.array(X), np.array(y)
-
-# Function to build and train the LSTM model
-def train_model(data, time_step=10):
-    # Scale the data
-    scaler = MinMaxScaler(feature_range=(0, 1))
-    scaled_data = scaler.fit_transform(data)
-
-    # Create datasets
-    X, y = create_dataset(scaled_data, time_step)
-    X = X.reshape(X.shape[0], X.shape[1], 1)  # Reshape for LSTM input
-
-    # Split the data into training and testing sets
-    split = int(len(X) * 0.8)
-    X_train, X_test = X[:split], X[split:]
-    y_train, y_test = y[:split], y[split:]
-
-    # Build the LSTM model
-    model = keras.Sequential([
-        layers.LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1)),
-        layers.LSTM(50, return_sequences=False),
-        layers.Dense(1)
-    ])
-
-    model.compile(optimizer='adam', loss='mean_squared_error')
-    model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=1)
-
-    return model, scaler
-
-# Function to predict the stock price
-def predict_price(ticker, start_date, end_date):
-    data = fetch_data(ticker, start_date, end_date)
-    model, scaler = train_model(data.values)
-    last_30_days = data['Close'][-30:].values
-    last_30_days_scaled = scaler.transform(last_30_days.reshape(-1, 1))
-
-    X_test = []
-    X_test.append(last_30_days_scaled)
-    X_test = np.array(X_test)
-    X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)
-
-    predicted_price = model.predict(X_test)
-    predicted_price = scaler.inverse_transform(predicted_price)
-
-    return predicted_price[0][0]
+from tensorflow import keras
 
-# UI for Gradio
-def stock_prediction(ticker, start_date, end_date):
-    predicted_price = predict_price(ticker, start_date, end_date)
-    return f"Predicted price for {ticker} on {end_date}: ${predicted_price:.2f}"
+# Load your trained model
+model = keras.models.load_model('your_model.h5')
+
+# 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)
+    
+    # Preprocess data
+    scaler = MinMaxScaler()
+    scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))
+    
+    # Prepare input for the model
+    # This assumes your model expects a certain shape of input
+    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
-ticker_input = gr.inputs.Dropdown(
-    choices=["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA", "FB", "NFLX", "NVDA", "BA", "DIS"],
+stock_ticker_input = gr.Dropdown(
+    choices=["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA"],
     label="Select Stock Ticker"
 )
-start_date_input = gr.inputs.Date(label="Start Date")
-end_date_input = gr.inputs.Date(label="End Date")
 
-gr.Interface(
-    fn=stock_prediction,
-    inputs=[ticker_input, start_date_input, end_date_input],
+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",  # Ensure this line is correctly formatted
+    title="Stock Price Prediction App",
     description="Enter the stock ticker and date range to predict the stock price."
-).launch()
+)
+
+# Launch the Gradio app
+iface.launch()