diff --git a/week8/community_contributions/muthama/price_is_right_final.py b/week8/community_contributions/muthama/price_is_right_final.py
new file mode 100644
index 0000000..eb32e6b
--- /dev/null
+++ b/week8/community_contributions/muthama/price_is_right_final.py
@@ -0,0 +1,164 @@
+import logging
+import queue
+import threading
+import time
+import gradio as gr
+import plotly.graph_objects as go
+
+from deal_agent_framework import DealAgentFramework
+# from agents.deals import Opportunity, Deal
+from log_utils import reformat
+
+
+
+class QueueHandler(logging.Handler):
+    def __init__(self, log_queue):
+        super().__init__()
+        self.log_queue = log_queue
+
+    def emit(self, record):
+        self.log_queue.put(self.format(record))
+
+def html_for(log_data):
+    output = '<br>'.join(log_data[-18:])
+    return f"""
+    <div id="scrollContent" style="height: 400px; overflow-y: auto; border: 1px solid #ccc; background-color: #222229; padding: 10px;">
+    {output}
+    </div>
+    """
+
+def setup_logging(log_queue):
+    handler = QueueHandler(log_queue)
+    formatter = logging.Formatter(
+        "[%(asctime)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S %z",
+    )
+    handler.setFormatter(formatter)
+    logger = logging.getLogger()
+    logger.addHandler(handler)
+    logger.setLevel(logging.INFO)
+                
+
+class App:
+
+    def __init__(self):    
+        self.agent_framework = None
+
+    def get_agent_framework(self):
+        if not self.agent_framework:
+            self.agent_framework = DealAgentFramework()
+            self.agent_framework.init_agents_as_needed()
+        return self.agent_framework
+
+    def run(self):
+        with gr.Blocks(title="The Price is Right", fill_width=True) as ui:
+            
+            log_data = gr.State([])
+            
+            def table_for(opps):
+                return [[opp.deal.product_description, f"${opp.deal.price:.2f}", f"${opp.estimate:.2f}", f"${opp.discount:.2f}", opp.deal.url] for opp in opps]
+
+            def update_output(log_data, log_queue, result_queue):
+                def live_table():
+                    return table_for(self.get_agent_framework().memory)
+
+                final_result = None
+                while True:
+                    try:
+                        message = log_queue.get_nowait()
+                        log_data.append(reformat(message))
+                        yield log_data, html_for(log_data), live_table()
+                        continue
+                    except queue.Empty:
+                        pass
+
+                    try:
+                        final_result = result_queue.get_nowait()
+                        yield log_data, html_for(log_data), (final_result or live_table())
+                        break
+                    except queue.Empty:
+                        time.sleep(0.05)
+
+            def get_plot():
+                documents, vectors, colors = DealAgentFramework.get_plot_data(max_datapoints=1000)
+                # Create the 3D scatter plot
+                fig = go.Figure(data=[go.Scatter3d(
+                    x=vectors[:, 0],
+                    y=vectors[:, 1],
+                    z=vectors[:, 2],
+                    mode='markers',
+                    marker=dict(size=2, color=colors, opacity=0.7),
+                )])
+                
+                fig.update_layout(
+                    scene=dict(xaxis_title='x', 
+                               yaxis_title='y', 
+                               zaxis_title='z',
+                               aspectmode='manual',
+                               aspectratio=dict(x=2.2, y=2.2, z=1),  # Make x-axis twice as long
+                               camera=dict(
+                                   eye=dict(x=1.6, y=1.6, z=0.8)  # Adjust camera position
+                               )),
+                    height=400,
+                    margin=dict(r=5, b=1, l=5, t=2)
+                )
+
+                return fig
+        
+            def do_run():
+                new_opportunities = self.get_agent_framework().run()
+                table = table_for(new_opportunities)
+                return table
+
+            def run_with_logging(initial_log_data):
+                log_queue = queue.Queue()
+                result_queue = queue.Queue()
+                setup_logging(log_queue)
+
+                def worker():
+                    result = do_run()  # this updates memory during execution
+                    result_queue.put(result)
+
+                thread = threading.Thread(target=worker, daemon=True)
+                thread.start()
+
+                for log_data, output_html, table in update_output(initial_log_data, log_queue, result_queue):
+                    yield log_data, output_html, table
+
+            def do_select(selected_index: gr.SelectData):
+                opportunities = self.get_agent_framework().memory
+                row = selected_index.index[0]
+                opportunity = opportunities[row]
+                self.get_agent_framework().planner.messenger.alert(opportunity)
+        
+            with gr.Row():
+                gr.Markdown('<div style="text-align: center;font-size:24px"><strong>The Price is Right</strong> - Autonomous Agent Framework that hunts for deals</div>')
+            with gr.Row():
+                gr.Markdown('<div style="text-align: center;font-size:14px">A proprietary fine-tuned LLM deployed on Modal and a RAG pipeline with a frontier model collaborate to send push notifications with great online deals.</div>')
+            with gr.Row():
+                opportunities_dataframe = gr.Dataframe(
+                    headers=["Deals found so far", "Price", "Estimate", "Discount", "URL"],
+                    wrap=True,
+                    column_widths=[6, 1, 1, 1, 3],
+                    row_count=10,
+                    col_count=5,
+                    max_height=400,
+                )
+            with gr.Row():
+                with gr.Column(scale=1):
+                    logs = gr.HTML()
+                with gr.Column(scale=1):
+                    plot = gr.Plot(value=get_plot(), show_label=False)
+        
+            ui.load(run_with_logging, inputs=[log_data], outputs=[log_data, logs, opportunities_dataframe])
+
+            timer = gr.Timer(value=300, active=True)
+            timer.tick(run_with_logging, inputs=[log_data], outputs=[log_data, logs, opportunities_dataframe])
+
+            opportunities_dataframe.select(do_select)
+        
+        ui.launch(share=False, inbrowser=True)
+
+if __name__=="__main__":
+    App().run()
+    
\ No newline at end of file
diff --git a/week8/community_contributions/muthama/week8_exercise_solution-Stephen.ipynb b/week8/community_contributions/muthama/week8_exercise_solution-Stephen.ipynb
new file mode 100644
index 0000000..75da0cf
--- /dev/null
+++ b/week8/community_contributions/muthama/week8_exercise_solution-Stephen.ipynb
@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a71ed017-e1b0-4299-88b3-f0eb05adc4df",
+   "metadata": {},
+   "source": [
+    "# The Price is Right\n",
+    "\n",
+    "The final step is to build a User Interface\n",
+    "\n",
+    "We will use more advanced aspects of Gradio - building piece by piece."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b77940b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "133.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import modal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6449363f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!modal deploy -m pricer_service2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c67160e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Pricer = modal.Cls.from_name(\"pricer-service\", \"Pricer\")\n",
+    "pricer = Pricer()\n",
+    "reply = pricer.price.remote(\"Quadcast HyperX condenser mic, connects via usb-c to your computer for crystal clear audio\")\n",
+    "print(reply)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48506465-1c7a-433f-a665-b277a8b4665c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python price_is_right_final.py"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}