app.py

import gradio as gr
import pandas as pd
import yfinance as yf
from huggingface_hub import InferenceClient

# Initialize the Inference Client for the chatbot
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Function for the chatbot response
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    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 = ""

    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
        response += token
        yield response

# Function for the trading screener
def trading_screener(price_threshold, volume_threshold):
    stocks = ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA"]
    data = []

    for stock in stocks:
        ticker = yf.Ticker(stock)
        hist = ticker.history(period="1d")
        current_price = hist['Close'].iloc[-1]
        avg_volume = hist['Volume'].mean()
        data.append({"Stock": stock, "Price": current_price, "Avg Volume": avg_volume})

    df = pd.DataFrame(data)
    filtered_df = df[(df['Price'] > price_threshold) & (df['Avg Volume'] > volume_threshold)]
    return filtered_df

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Trading Screener and Chatbot")
    
    # Trading Screener Section
    with gr.Row():
        with gr.Column():
            gr.Markdown("## Trading Screener")
            price_threshold = gr.Number(label="Price Threshold", value=100.0)
            volume_threshold = gr.Number(label="Volume Threshold", value=1000000)
            submit_btn = gr.Button("Run Screener")
            output_df = gr.Dataframe(label="Filtered Stocks")
        
        submit_btn.click(fn=trading_screener, inputs=[price_threshold, volume_threshold], outputs=output_df)

    # Chatbot Section
    gr.Markdown("## Chatbot")
    chat_interface = 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)",
            ),
        ],
    )

# Launch the app
if __name__ == "__main__":
    demo.launch()