Update app.py

app.py

@@ -1,12 +1,12 @@
 import gradio as gr
+import pandas as pd
+import yfinance as yf
 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
-"""
+# 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]],
@@ -35,30 +35,58 @@ def respond(
         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
 
-"""
-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)",
-        ),
-    ],
-)
+# 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()
+    demo.launch()