Update app.py

app.py

@@ -1,9 +1,6 @@
 import pandas as pd
 import gradio as gr
-import lightgbm as lgb
-import plotly.graph_objects as go
 import numpy as np
-from sklearn.model_selection import train_test_split
 
 # Load dataset
 DATA_PATH = "/mnt/data/AAPL_stock_data_finalversion (1).csv"
@@ -13,7 +10,7 @@ data = pd.read_csv(DATA_PATH)
 data['Date'] = pd.to_datetime(data['Date'])
 data['DateNumeric'] = data['Date'].map(pd.Timestamp.toordinal)
 
-# Define prediction function
+# Define a basic prediction function
 def stock_analysis(start_date, end_date):
     try:
         # Filter data by date range
@@ -22,81 +19,39 @@ def stock_analysis(start_date, end_date):
         if filtered_data.empty:
             return "No data available for the given date range.", None, None
 
-        # Prepare data for forecasting
-        X = filtered_data[['DateNumeric']]
-        y = filtered_data['Close']
+        # Simple linear trend for predictions
+        dates_numeric = filtered_data['DateNumeric'].values
+        prices = filtered_data['Close'].values
 
-        # Train-test split
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+        # Calculate slope and intercept for linear trend
+        slope, intercept = np.polyfit(dates_numeric, prices, 1)
 
-        # Prepare LightGBM dataset
-        train_data = lgb.Dataset(X_train, label=y_train)
-
-        # LightGBM parameters
-        params = {
-            'objective': 'regression',
-            'metric': 'rmse',
-            'boosting_type': 'gbdt',
-            'num_leaves': 31,
-            'learning_rate': 0.05,
-            'verbose': -1
-        }
-
-        # Train LightGBM model
-        model = lgb.train(params, train_data, num_boost_round=100)
-
-        # Predict future stock prices (next 30 days)
+        # Predict next 30 business days
         last_date = filtered_data['Date'].iloc[-1]
         future_dates = pd.date_range(last_date, periods=30, freq='B')
-        future_dates_numeric = future_dates.map(pd.Timestamp.toordinal).values.reshape(-1, 1)
-        future_predictions = model.predict(future_dates_numeric)
+        future_dates_numeric = future_dates.map(pd.Timestamp.toordinal)
+        future_predictions = slope * future_dates_numeric + intercept
 
-        # Add trend or variation to predictions based on historical data
-        historical_trend = np.gradient(filtered_data['Close'].values)
-        trend_mean = np.mean(historical_trend) if len(historical_trend) > 0 else 0
-        future_predictions = future_predictions + np.linspace(0, trend_mean * 30, len(future_predictions))
-
-        # Create a DataFrame for predictions
+        # Create DataFrame for predictions
         future_df = pd.DataFrame({
             'Date': future_dates,
-            'Predicted Close Price': future_predictions.flatten()
+            'Predicted Close Price': future_predictions
         })
 
-        # Create a plot using Plotly
-        fig = go.Figure()
-
-        # Add historical data to the plot
-        fig.add_trace(go.Scatter(
-            x=filtered_data['Date'], 
-            y=filtered_data['Close'], 
-            mode='lines', 
-            name='Historical Close Prices'
-        ))
-
-        # Add predicted data to the plot
-        fig.add_trace(go.Scatter(
-            x=future_df['Date'], 
-            y=future_df['Predicted Close Price'], 
-            mode='lines', 
-            name='Predicted Close Prices'
-        ))
-
-        fig.update_layout(
-            title="Stock Prices: Historical and Predicted",
-            xaxis_title="Date",
-            yaxis_title="Close Price (USD)",
-            legend_title="Legend",
-            template="plotly_white"
-        )
-
-        return "Analysis completed!", fig, future_df
+        # Combine historical and future data for graphing
+        graph_data = pd.concat([
+            filtered_data[['Date', 'Close']].rename(columns={'Close': 'Price'}),
+            future_df.rename(columns={'Predicted Close Price': 'Price'})
+        ])
+
+        return "Analysis completed!", graph_data.to_dict(orient='list'), future_df
 
     except Exception as e:
         return f"An error occurred: {str(e)}", None, None
 
 # Define Gradio app
 with gr.Blocks() as app:
-    gr.Markdown("# Stock Price Prediction App (Using Uploaded Dataset)")
+    gr.Markdown("# Simple Stock Price Prediction App")
 
     with gr.Row():
         start_date_input = gr.Textbox(label="Start Date", placeholder="YYYY-MM-DD")
@@ -105,13 +60,13 @@ with gr.Blocks() as app:
     analyze_button = gr.Button("Analyze")
 
     output_message = gr.Textbox(label="Message", interactive=False)
-    stock_plot = gr.Plot(label="Stock Price Plot")
+    stock_table = gr.Dataframe(label="Historical and Predicted Prices")
     prediction_table = gr.Dataframe(label="Future Predictions")
 
     analyze_button.click(
         stock_analysis,
         inputs=[start_date_input, end_date_input],
-        outputs=[output_message, stock_plot, prediction_table]
+        outputs=[output_message, stock_table, prediction_table]
     )
 
 # Launch the app