init

app.py

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+import gradio as gr
+import yfinance as yf
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler
+# Load your pre-trained Keras model
+model = tf.keras.models.load_model("./best.keras")
+
+# scale the data
+def create_scaler(df):
+    scaler = MinMaxScaler(feature_range=(0,1))
+    scaled_df = scaler.fit_transform(df['Close'].values.reshape(-1, 1))
+    return scaler, scaled_df
+# create input output sequence
+def create_sequence(scaled_df):
+    X, y = [], []
+    window = 60
+    n_future = 1
+    for i in range(len(scaled_df) - window - n_future - 1):
+        X.append(scaled_df[i:i+window])
+        y.append(scaled_df[i+window+n_future])
+    X = np.array(X)
+    y = np.array(y)
+    return X, y
+
+def fetch_and_predict(ticker, period):
+    # Fetch historical stock data using yfinance
+    try:
+        df = yf.download(ticker, period=period)
+        if isinstance(df.columns, pd.MultiIndex):
+            df.columns = df.columns.get_level_values(0)
+    except Exception as e:
+        print("check 2")
+        return f"Error downloading data: {e}"
+    
+    # Check if we have enough data for predictions
+
+    if df.shape[0] < 60:
+        return "Not enough data for predictions. Please select a longer period."
+    
+    # prepare data
+    scaler, df = create_scaler(df)
+    X, y = create_sequence(df)
+    # Predicting stock prices
+    try:
+        print("fine")
+        yhat = model.predict(X)
+    except Exception as e:
+        return f"Error during prediction: {e}"
+    # Plot the predicted prices
+    plt.figure(figsize=(14, 7))
+    plt.plot(y, label='Actual Prices')
+    plt.plot(yhat, label='Predicted Prices')
+    plt.title(f'Stock Price Prediction (LSTM) - [{str(ticker)}]')
+    plt.xlabel('Time')
+    plt.ylabel('Stock Price')
+    plt.legend()
+    plt.xticks(rotation=45)
+    return plt.gcf()
+
+interface = gr.Interface(
+    fn=fetch_and_predict,
+    inputs=[
+        gr.Textbox(label="Stock Ticker", placeholder="Enter stock ticker (e.g., DAL, AAPL)"),
+        gr.Textbox(label="Period", placeholder="Enter period (e.g., '1y')")
+    ],
+    outputs=gr.Plot(),
+    live=False,
+    allow_flagging="never",
+    title="Stock Price Prediction",
+    description="Enter the stock ticker and period, then click the button to fetch data and predict prices.",
+    theme="huggingface",
+)
+
+interface.launch()

best.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b9f5adbd0e6c4bc1bfe8b553596f050976ab95fcd19a4cd4b4f53914441650c3
+size 430225