app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from statsmodels.tsa.arima.model import ARIMA
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+
+def forecast_stock(file, model_choice="ARIMA", horizon=30):
+    df = pd.read_csv(file, parse_dates=["Date"])
+    df = df.set_index("Date").asfreq("B")
+    df["Aclose"] = df["Aclose"].interpolate()
+    series = df["Aclose"].dropna()
+
+    if model_choice == "ARIMA":
+        model = ARIMA(series, order=(2,1,2))
+        model_fit = model.fit()
+        forecast = model_fit.forecast(steps=horizon)
+    else:  # LSTM
+        scaler = MinMaxScaler()
+        data = scaler.fit_transform(series.values.reshape(-1,1))
+        X, y = [], []
+        seq_len = 60
+        for i in range(len(data)-seq_len):
+            X.append(data[i:i+seq_len])
+            y.append(data[i+seq_len])
+        X, y = np.array(X), np.array(y)
+
+        model = Sequential([
+            LSTM(50, input_shape=(seq_len,1)),
+            Dense(1)
+        ])
+        model.compile(optimizer="adam", loss="mse")
+        model.fit(X, y, epochs=3, batch_size=32, verbose=0)
+
+        # Forecast
+        last_seq = data[-seq_len:].reshape(1,seq_len,1)
+        preds = []
+        for _ in range(horizon):
+            p = model.predict(last_seq, verbose=0)[0,0]
+            preds.append(p)
+            last_seq = np.append(last_seq[:,1:,:], [[[p]]], axis=1)
+        forecast = scaler.inverse_transform(np.array(preds).reshape(-1,1)).flatten()
+
+    # Plot results
+    plt.figure(figsize=(10,5))
+    plt.plot(series[-200:], label="History")
+    plt.plot(pd.date_range(series.index[-1], periods=horizon+1, freq="B")[1:], forecast, label="Forecast")
+    plt.legend()
+    plt.title(f"{model_choice} Stock Forecast")
+    plt.xlabel("Date")
+    plt.ylabel("Price")
+    plt.tight_layout()
+    return plt
+
+demo = gr.Interface(
+    fn=forecast_stock,
+    inputs=[
+        gr.File(label="Upload Stock CSV"),
+        gr.Radio(["ARIMA","LSTM"], value="ARIMA", label="Model"),
+        gr.Slider(5,60,step=5,label="Forecast Horizon (days)")
+    ],
+    outputs=gr.Plot(label="Forecast Plot"),
+    title="📈 Stock Price Forecasting",
+    description="Upload stock data (CSV with Date,Aclose). Choose ARIMA or LSTM."
+)
+
+if __name__ == "__main__":
+    demo.launch()