Add app and requirements

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,111 @@
+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)  

lstm_stock_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:98c34ac4dc66616fde9e27a93a884c97579dcbae471946357118d5e7378cb5a3
+size 669510

requirements.txt

@@ -0,0 +1,11 @@
+pandas>=2.3
+numpy==1.26.4
+matplotlib==3.9.2
+joblib==1.4.2
+tensorflow>=2.20
+scikit-learn==1.5.2
+prophet==1.1.5
+huggingface-hub==0.24.6
+gradio==4.44.1
+
+

scaler_minmax.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b1d97a205f035ef231abefa233ae6d95ad0c2137a2291fee264571ddca4b7ed
+size 1167