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+Absolutely! Let's simplify the project to create a Minimum Viable Product (MVP). The MVP will focus on the core functionality: fetching historical stock data, storing it in a SQLite database, and providing a simple web interface to display the actual and predicted stock prices.
+
+### Simplified Project Structure
+
+```
+stock_prediction/
+├── data/
+│   └── stock_data.db
+├── scripts/
+│   └── fetch_stock_data.py
+├── app/
+│   └── stock_prediction_app.py
+├── environment.yml
+└── requirements.txt
+```
+
+### Simplified `fetch_stock_data.py`
+
+This script fetches historical stock data and stores it in a SQLite database.
+
+```python
+import argparse
+import yfinance as yf
+import pandas as pd
+import sqlite3
+import os
+
+def fetch_stock_data(symbol, start_date, end_date, db_path):
+    try:
+        conn = sqlite3.connect(db_path)
+        data = yf.download(symbol, start=start_date, end=end_date)
+        table_name = f"{symbol}_prices"
+        data.to_sql(name=table_name, con=conn, if_exists="replace")
+        conn.close()
+        print(f"Data for {symbol} stored in the database.")
+    except sqlite3.Error as e:
+        print(f"Error while storing data for {symbol}: {e}")
+    except Exception as e:
+        print(f"Error while fetching data for {symbol}: {e}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Fetch historical stock data from Yahoo Finance.")
+    parser.add_argument("--symbol", type=str, default="^GSPC", help="Stock symbol (default: ^GSPC)")
+    parser.add_argument("--start_date", type=str, default="2000-01-01", help="Start date (default: 2000-01-01)")
+    parser.add_argument("--end_date", type=str, default="2023-05-31", help="End date (default: 2023-05-31)")
+    args = parser.parse_args()
+
+    symbol = args.symbol
+    start_date = args.start_date
+    end_date = args.end_date
+
+    db_path = os.path.join(os.path.dirname(__file__), '../data/stock_data.db')
+    fetch_stock_data(symbol, start_date, end_date, db_path)
+```
+
+### Simplified `stock_prediction_app.py`
+
+This Dash application fetches data from the SQLite database and displays it. The prediction functionality is kept simple.
+
+```python
+import dash
+import dash_core_components as dcc
+import dash_html_components as html
+from dash.dependencies import Input, Output
+import pandas as pd
+import sqlite3
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+import plotly.graph_objects as go
+import os
+
+app = dash.Dash(__name__)
+
+app.layout = html.Div([
+    html.H1("Stock Price Prediction"),
+    html.Div([
+        html.Label("Select Stock Symbol"),
+        dcc.Dropdown(
+            id="stock-dropdown",
+            options=[{"label": "S&P 500", "value": "^GSPC"},
+                     {"label": "Dow Jones", "value": "^DJI"},
+                     {"label": "Nasdaq", "value": "^IXIC"}],
+            value="^GSPC"
+        )
+    ]),
+    html.Div([
+        dcc.Graph(id="stock-graph")
+    ])
+])
+
+@app.callback(Output("stock-graph", "figure"),
+              [Input("stock-dropdown", "value")])
+def update_graph(stock_symbol):
+    try:
+        db_path = os.path.join(os.path.dirname(__file__), "../data/stock_data.db")
+        conn = sqlite3.connect(db_path)
+        data = pd.read_sql_query(f"SELECT Date, Close FROM {stock_symbol}_prices", conn)
+        conn.close()
+
+        scaler = MinMaxScaler(feature_range=(0, 1))
+        scaled_data = scaler.fit_transform(data["Close"].values.reshape(-1, 1))
+
+        lookback = 60
+        X, y = [], []
+        for i in range(lookback, len(scaled_data)):
+            X.append(scaled_data[i - lookback:i, 0])
+            y.append(scaled_data[i, 0])
+        X, y = np.array(X), np.array(y)
+        X = np.reshape(X, (X.shape[0], X.shape[1], 1))
+
+        model = Sequential()
+        model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
+        model.add(LSTM(units=50))
+        model.add(Dense(1))
+        model.compile(loss="mean_squared_error", optimizer="adam")
+        model.fit(X, y, epochs=1, batch_size=32)
+
+        last_data = scaled_data[-lookback:]
+        X_test = np.array([last_data])
+        X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
+        predicted_prices = model.predict(X_test)
+        predicted_prices = scaler.inverse_transform(predicted_prices)
+
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=data["Date"], y=data["Close"], name="Actual Price"))
+        fig.add_trace(go.Scatter(x=data["Date"][-len(predicted_prices):], y=predicted_prices.flatten(), name="Predicted Price"))
+        fig.update_layout(title=f"{stock_symbol} Stock Price Prediction", xaxis_title="Date", yaxis_title="Price")
+
+        return fig
+    except sqlite3.Error as e:
+        print(f"Error while fetching data for {stock_symbol}: {e}")
+        return go.Figure()
+    except Exception as e:
+        print(f"Error while updating graph for {stock_symbol}: {e}")
+        return go.Figure()
+
+if __name__ == "__main__":
+    app.run_server(debug=True)
+```
+
+### `environment.yml`:
+
+```yaml
+name: stock_prediction
+channels:
+  - defaults
+dependencies:
+  - python=3.9
+  - pip
+  - pip:
+    - -r requirements.txt
+```
+
+### `requirements.txt`:
+
+```
+dash
+pandas
+numpy
+yfinance
+scikit-learn
+tensorflow
+keras
+plotly
+```
+
+### Instructions for Running the MVP
+
+1. **Create the Conda Environment**:
+
+   ```bash
+   conda env create -f environment.yml
+   ```
+
+2. **Activate the Conda Environment**:
+
+   ```bash
+   conda activate stock_prediction
+   ```
+
+3. **Fetch Historical Stock Data**:
+
+   ```bash
+   python scripts/fetch_stock_data.py --symbol ^GSPC --start_date 2000-01-01 --end_date 2023-05-31
+   ```
+
+4. **Run the Dash Application**:
+
+   ```bash
+   python app/stock_prediction_app.py
+   ```
+
+5. **Open the Web Application**:
+
+   Open your web browser and go to `http://localhost:8050` to access the application. Select a stock symbol from the dropdown menu to see the actual and predicted stock prices on the graph.
+
+This simplified version focuses on the essential features and ensures that the core functionalities work properly. Once this MVP is stable, you can gradually add more features and improvements.
+
+---
+
 Here's the complete project structure and code in one document:
 
 Project Structure: