Update app.py

app.py

@@ -4,6 +4,37 @@ import pandas as pd
 from sklearn.linear_model import LinearRegression
 import plotly.graph_objects as go
 
+markdown_content = """
+# Asset Price Prediction Tool
+
+## Introduction
+This tool uses historical stock price data to predict future prices. It's designed to provide insights into potential price trends based on past performance.
+
+## How to Use
+1. **Enter the Ticker Symbol:** Input the stock ticker (e.g., 'AAPL' for Apple Inc.).
+2. **Select Start and End Dates:** Choose the historical data range for analysis. Dates must be entered in the format YYYY-MM-DD (e.g., 2023-01-01).
+3. **Set Prediction Days:** Decide how many days into the future you want to predict.
+4. **Submit:** Click 'Run' to view the predictions.
+
+## How It Works
+- **Data Fetching:** The tool fetches historical closing prices of the specified asset using `yfinance` for the date range you provide.
+- **Model Training:** It then trains a linear regression model on this data. The model learns the relationship between dates and closing prices during this period.
+- **Making Predictions:** Based on the learned relationship, the model attempts to predict future prices for the number of days you specified.
+
+## Understanding Linear Regression
+- Linear regression is a statistical method that models the relationship between a dependent variable and one or more independent variables.
+- In this tool, the dependent variable is the asset's price, and the independent variable is time (dates).
+- The model assumes a linear relationship (a straight line trend) between dates and prices.
+- It's important to note that this method works best when the relationship between the data points is linear and may not capture complex market dynamics.
+
+## Interpreting Data
+- **Historical Prices:** Displayed as a solid blue line, representing actual past closing prices.
+- **Predicted Prices:** Shown as a solid red line, indicating the model's predictions.
+- **Limitations:** The predictions are based on historical trends and do not account for unforeseen market events or changes in market conditions. They should be used as a guideline rather than definitive financial advice.
+
+Remember, investing in the stock market involves risks, and past performance is not indicative of future results.
+"""
+
 def train_predict_wrapper(ticker, start_date, end_date, prediction_days):
     # Download asset data
     data = yf.download(ticker, start=start_date, end=end_date)
@@ -64,7 +95,8 @@ interface = gr.Interface(
         gr.Textbox(label="End Date (YYYY-MM-DD)"),
         gr.Slider(minimum=1, maximum=30, step=1, label="Prediction Days"),
     ],
-    outputs="plot"
+    outputs="plot",
+    description=markdown_content
 )
 
 # Launch the app