Add week 8 exercise
This commit is contained in:
muthama
164
week8/community_contributions/muthama/price_is_right_final.py
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164
week8/community_contributions/muthama/price_is_right_final.py
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import logging
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import queue
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import threading
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import time
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import gradio as gr
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import plotly.graph_objects as go
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from deal_agent_framework import DealAgentFramework
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# from agents.deals import Opportunity, Deal
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from log_utils import reformat
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class QueueHandler(logging.Handler):
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def __init__(self, log_queue):
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super().__init__()
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self.log_queue = log_queue
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def emit(self, record):
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self.log_queue.put(self.format(record))
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def html_for(log_data):
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output = '<br>'.join(log_data[-18:])
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return f"""
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<div id="scrollContent" style="height: 400px; overflow-y: auto; border: 1px solid #ccc; background-color: #222229; padding: 10px;">
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{output}
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</div>
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"""
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def setup_logging(log_queue):
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handler = QueueHandler(log_queue)
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formatter = logging.Formatter(
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"[%(asctime)s] %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S %z",
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)
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handler.setFormatter(formatter)
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logger = logging.getLogger()
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logger.addHandler(handler)
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logger.setLevel(logging.INFO)
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class App:
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def __init__(self):
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self.agent_framework = None
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def get_agent_framework(self):
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if not self.agent_framework:
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self.agent_framework = DealAgentFramework()
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self.agent_framework.init_agents_as_needed()
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return self.agent_framework
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def run(self):
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with gr.Blocks(title="The Price is Right", fill_width=True) as ui:
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log_data = gr.State([])
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def table_for(opps):
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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]
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def update_output(log_data, log_queue, result_queue):
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def live_table():
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return table_for(self.get_agent_framework().memory)
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final_result = None
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while True:
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try:
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message = log_queue.get_nowait()
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log_data.append(reformat(message))
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yield log_data, html_for(log_data), live_table()
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continue
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except queue.Empty:
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pass
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try:
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final_result = result_queue.get_nowait()
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yield log_data, html_for(log_data), (final_result or live_table())
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break
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except queue.Empty:
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time.sleep(0.05)
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def get_plot():
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documents, vectors, colors = DealAgentFramework.get_plot_data(max_datapoints=1000)
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# Create the 3D scatter plot
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fig = go.Figure(data=[go.Scatter3d(
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x=vectors[:, 0],
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y=vectors[:, 1],
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z=vectors[:, 2],
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mode='markers',
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marker=dict(size=2, color=colors, opacity=0.7),
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)])
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fig.update_layout(
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scene=dict(xaxis_title='x',
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yaxis_title='y',
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zaxis_title='z',
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aspectmode='manual',
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aspectratio=dict(x=2.2, y=2.2, z=1), # Make x-axis twice as long
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camera=dict(
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eye=dict(x=1.6, y=1.6, z=0.8) # Adjust camera position
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)),
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height=400,
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margin=dict(r=5, b=1, l=5, t=2)
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)
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return fig
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def do_run():
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new_opportunities = self.get_agent_framework().run()
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table = table_for(new_opportunities)
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return table
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def run_with_logging(initial_log_data):
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log_queue = queue.Queue()
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result_queue = queue.Queue()
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setup_logging(log_queue)
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def worker():
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result = do_run() # this updates memory during execution
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result_queue.put(result)
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thread = threading.Thread(target=worker, daemon=True)
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thread.start()
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for log_data, output_html, table in update_output(initial_log_data, log_queue, result_queue):
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yield log_data, output_html, table
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def do_select(selected_index: gr.SelectData):
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opportunities = self.get_agent_framework().memory
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row = selected_index.index[0]
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opportunity = opportunities[row]
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self.get_agent_framework().planner.messenger.alert(opportunity)
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with gr.Row():
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gr.Markdown('<div style="text-align: center;font-size:24px"><strong>The Price is Right</strong> - Autonomous Agent Framework that hunts for deals</div>')
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with gr.Row():
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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>')
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with gr.Row():
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opportunities_dataframe = gr.Dataframe(
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headers=["Deals found so far", "Price", "Estimate", "Discount", "URL"],
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wrap=True,
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column_widths=[6, 1, 1, 1, 3],
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row_count=10,
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col_count=5,
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max_height=400,
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)
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with gr.Row():
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with gr.Column(scale=1):
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logs = gr.HTML()
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with gr.Column(scale=1):
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plot = gr.Plot(value=get_plot(), show_label=False)
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ui.load(run_with_logging, inputs=[log_data], outputs=[log_data, logs, opportunities_dataframe])
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timer = gr.Timer(value=300, active=True)
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timer.tick(run_with_logging, inputs=[log_data], outputs=[log_data, logs, opportunities_dataframe])
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opportunities_dataframe.select(do_select)
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ui.launch(share=False, inbrowser=True)
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if __name__=="__main__":
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App().run()
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@@ -0,0 +1,88 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a71ed017-e1b0-4299-88b3-f0eb05adc4df",
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"metadata": {},
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"source": [
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"# The Price is Right\n",
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"\n",
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"The final step is to build a User Interface\n",
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"\n",
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"We will use more advanced aspects of Gradio - building piece by piece."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b77940b8",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"133.0\n"
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]
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}
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],
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"source": [
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"import modal"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6449363f",
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"metadata": {},
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"outputs": [],
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"source": [
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"!modal deploy -m pricer_service2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3c67160e",
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"metadata": {},
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"outputs": [],
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"source": [
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"Pricer = modal.Cls.from_name(\"pricer-service\", \"Pricer\")\n",
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"pricer = Pricer()\n",
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"reply = pricer.price.remote(\"Quadcast HyperX condenser mic, connects via usb-c to your computer for crystal clear audio\")\n",
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"print(reply)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "48506465-1c7a-433f-a665-b277a8b4665c",
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"metadata": {},
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"outputs": [],
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"source": [
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"!python price_is_right_final.py"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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