Create app.py

app.py

@@ -1,64 +1,102 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# app.py
+# This script creates an interactive dashboard for viewing an investment portfolio using Gradio.
+# It fetches real-time stock prices via yfinance and displays portfolio metrics, including a table and pie chart.
+# To deploy on Hugging Face Spaces:
+# 1. Create a new Space on Hugging Face.
+# 2. Upload this file as app.py.
+# 3. Add a requirements.txt file with the following content:
+#    gradio
+#    yfinance
+#    pandas
+#    matplotlib
+# 4. The Space will automatically install the dependencies and run the app.
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
+import gradio as gr
+import yfinance as yf
+import pandas as pd
+import matplotlib.pyplot as plt
+from io import BytesIO
+import base64
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+# Define the portfolio data
+portfolio = {
+    'Ticker': ['TSLA', 'PLTR', 'SOUN'],
+    'Shares': [200, 490, 652],
+    'Avg Cost': [245.0, 52.0, 7.5]
+}
 
-        response += token
-        yield response
+def get_current_prices(tickers):
+    """
+    Fetch the latest closing prices for the given tickers.
+    """
+    data = yf.download(tickers, period='1d')['Close']
+    return data.iloc[-1]
 
+def generate_pie_chart(values, labels):
+    """
+    Generate a pie chart as a base64-encoded image.
+    """
+    fig, ax = plt.subplots()
+    ax.pie(values, labels=labels, autopct='%1.1f%%', startangle=90)
+    ax.set_title('Portfolio Allocation by Current Value')
+    ax.axis('equal')  # Equal aspect ratio ensures the pie is circular.
+    
+    buf = BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    img_base64 = base64.b64encode(buf.read()).decode('utf-8')
+    plt.close(fig)
+    return f"data:image/png;base64,{img_base64}"
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+def display_portfolio():
+    """
+    Compute portfolio metrics and generate outputs.
+    """
+    df = pd.DataFrame(portfolio)
+    try:
+        prices = get_current_prices(df['Ticker'].tolist())
+        df['Current Price'] = prices.values
+    except Exception as e:
+        return f"Error fetching prices: {str(e)}", "", "N/A", "N/A"
+    
+    df['Cost Basis'] = df['Shares'] * df['Avg Cost']
+    df['Current Value'] = df['Shares'] * df['Current Price']
+    df['Gain/Loss'] = df['Current Value'] - df['Cost Basis']
+    df['Gain/Loss %'] = (df['Gain/Loss'] / df['Cost Basis']) * 100
+    
+    total_cost = df['Cost Basis'].sum()
+    total_value = df['Current Value'].sum()
+    total_gain = total_value - total_cost
+    
+    # Format the table as HTML
+    table_html = df.to_html(index=False, float_format=lambda x: f'{x:.2f}')
+    
+    # Generate pie chart
+    pie_img = generate_pie_chart(df['Current Value'], df['Ticker'])
+    
+    total_value_str = f"${total_value:.2f}"
+    total_gain_str = f"${total_gain:.2f} ({(total_gain / total_cost * 100):.2f}%)"
+    
+    return table_html, pie_img, total_value_str, total_gain_str
 
+# Create the Gradio interface
+with gr.Blocks(title="Investment Portfolio Dashboard") as demo:
+    gr.Markdown("# Investment Portfolio Dashboard")
+    gr.Markdown("View your portfolio metrics with real-time stock prices.")
+    
+    refresh_btn = gr.Button("Refresh Data")
+    
+    table_output = gr.HTML(label="Portfolio Table")
+    pie_output = gr.Image(label="Portfolio Allocation", type="pil")  # Will display base64 image
+    total_value_output = gr.Textbox(label="Total Portfolio Value")
+    total_gain_output = gr.Textbox(label="Total Gain/Loss")
+    
+    refresh_btn.click(
+        fn=display_portfolio,
+        inputs=[],
+        outputs=[table_output, pie_output, total_value_output, total_gain_output]
+    )
 
+# Launch the demo (automatically handled in Hugging Face Spaces)
 if __name__ == "__main__":
     demo.launch()