Create functionality

functionality

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+import yfinance as yf
+import pandas as pd
+import numpy as np
+import plotly.graph_objs as go
+from prophet import Prophet
+import gradio as gr
+
+# Function to fetch stock data
+def fetch_stock_data(ticker, start_date, end_date):
+    stock_data = yf.download(ticker, start=start_date, end=end_date)
+    return stock_data
+
+# Train the model using Prophet
+def train_prophet_model(stock_data):
+    df = stock_data.reset_index()[['Date', 'Close']]
+    df.columns = ['ds', 'y']
+    
+    model = Prophet()
+    model.fit(df)
+    
+    future = model.make_future_dataframe(periods=90)  # Predict for the next 3 months
+    forecast = model.predict(future)
+    return forecast
+
+# Function to display stock information and prediction
+def predict_stock(ticker, start_date, end_date):
+    # Fetch stock data
+    stock_data = fetch_stock_data(ticker, start_date, end_date)
+    
+    # Train the model and get predictions
+    forecast = train_prophet_model(stock_data)
+    
+    # Calculate metrics: % change, highest, lowest
+    start_price = stock_data['Close'].iloc[0]
+    current_price = stock_data['Close'].iloc[-1]
+    percentage_change = ((current_price - start_price) / start_price) * 100
+    highest_price = stock_data['Close'].max()
+    lowest_price = stock_data['Close'].min()
+    
+    # Buy/Sell prediction (if price is predicted to increase, suggest Buy)
+    predicted_next_value = forecast['yhat'].iloc[-1]
+    suggestion = "Buy" if predicted_next_value > current_price else "Sell"
+    
+    # Plot historical and predicted prices
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=stock_data.index, y=stock_data['Close'], mode='lines', name='Historical'))
+    fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Predicted'))
+    
+    return fig, percentage_change, highest_price, lowest_price, suggestion
+
+# Define Gradio UI components
+tickers = ['AAPL', 'GOOG', 'MSFT', 'AMZN', 'TSLA', 'META', 'NVDA', 'NFLX', 'BABA', 'ORCL']
+start_date = gr.components.DatePicker(label="Start Date", value="2023-01-01")
+end_date = gr.components.DatePicker(label="End Date", value="2023-09-30")
+ticker_dropdown = gr.components.Dropdown(choices=tickers, label="Stock Ticker")
+
+# Gradio function to wrap the prediction
+def gradio_interface(ticker, start_date, end_date):
+    fig, percentage_change, highest_price, lowest_price, suggestion = predict_stock(ticker, start_date, end_date)
+    return fig, f"{percentage_change:.2f}%", f"{highest_price:.2f}", f"{lowest_price:.2f}", suggestion
+
+# Gradio interface and launch
+output = [gr.components.Plot(label="Stock Price Plot"), gr.components.Textbox(label="Percentage Change"),
+          gr.components.Textbox(label="Highest Price"), gr.components.Textbox(label="Lowest Price"),
+          gr.components.Textbox(label="Prediction (Buy/Sell)")]
+
+demo = gr.Interface(fn=gradio_interface, inputs=[ticker_dropdown, start_date, end_date], outputs=output, title="Stock Predictor App")
+
+demo.launch()
+
+