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Update src/streamlit_app.py
Browse files- src/streamlit_app.py +30 -27
src/streamlit_app.py
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import streamlit as st
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import pandas as pd
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import os
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import asyncio
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from llama_index.llms.openai import OpenAI
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from llama_index.core.agent import ReActAgent
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from llama_index.core.workflow import Context
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from llama_index.core.tools import FunctionTool
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# --- Import tools class ---
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from tools import PokemonAdvisorTools
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@@ -26,54 +25,58 @@ def initialize_agent():
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advisor.get_data_shape
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]
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tools = [FunctionTool.from_defaults(fn=func) for func in tool_methods]
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llm = OpenAI(model="gpt-4o-mini")
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system_prompt = """### ROLE
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You are the **cAsh, Pokemon Investment Advisor**, an expert algorithmic trading assistant for the Pokémon TCG market.
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You rely **strictly** on data. You do not guess. You do not hallucinate prices.
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### TOOL USAGE PROTOCOL
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1. **Verify First:** If a user asks about a card, ALWAYS use `get_card_info` first to ensure it exists.
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2. **Safety Check:** BEFORE recommending ANY card, you MUST call `assess_risk_volatility` (default to 6 months).
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3. **Refining Search:** If the user corrects you (e.g., "Not that card"), use `get_card_info` again with the corrected name or use `analyze_set_performance` to broaden the scope.
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4. **Profit Hunting:** If a user asks "What should I buy?", use `find_grading_opportunities` or `get_market_movers`.
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### TONE
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Professional, objective, concise, and user-friendly.
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"""
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agent = ReActAgent(
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tools=tools,
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llm=llm,
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verbose=True,
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timeout=120
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)
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agent
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print("agent has initialized")
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# --- 2. Page Configuration ---
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st.set_page_config(page_title="cAsh Robo-Advisor", page_icon="🤖", layout="wide")
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# --- 3. Chat Logic Function ---
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"""Handles the
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st.session_state.messages.append({"role": "user", "content": user_message})
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# Placeholder for the assistant's response while it thinks
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with st.chat_message("assistant"):
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response_placeholder = st.empty()
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# --- 4. Sidebar & UI Styling ---
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st.title("🤖 cAsh Robo-Advisor")
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@@ -87,6 +90,7 @@ if "messages" not in st.session_state:
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st.sidebar.header("Example Queries")
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examples = [
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"What are the top 3 grading opportunities right now?",
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"What cards are trending down?",
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"Show me profitable cards by Tomokazu Komiya."
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]
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# Sidebar helper: clicking an example sends it immediately
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for ex in examples:
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if st.sidebar.button(ex):
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# --- 5. Main Chat Interface ---
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# Display existing messages
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# User Input
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if prompt := st.chat_input("Ask your Pokemon Quants advisor..."):
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# Clear the "user" UI immediately then run logic
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with st.chat_message("user"):
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st.markdown(prompt)
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asyncio.run(process_chat(prompt))
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import streamlit as st
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import pandas as pd
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import os
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from llama_index.llms.openai import OpenAI
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from llama_index.core.agent import ReActAgent
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from llama_index.core.tools import FunctionTool
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from llama_index.core.llms import ChatMessage
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# --- Import tools class ---
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from tools import PokemonAdvisorTools
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advisor.get_data_shape
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]
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tools = [FunctionTool.from_defaults(fn=func) for func in tool_methods]
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llm = OpenAI(model="gpt-4o-mini", temperature=0.1)
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system_prompt = """### ROLE
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You are the **cAsh, Pokemon Investment Advisor**, an expert algorithmic trading assistant for the Pokémon TCG market.
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You rely **strictly** on data. You do not guess. You do not hallucinate prices.
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### TOOL USAGE PROTOCOL
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1. **Verify First:** If a user asks about a card, ALWAYS use `get_card_info` first to ensure it exists.
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2. **Safety Check:** BEFORE recommending ANY card, you MUST call `assess_risk_volatility` (default to 6 months).
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3. **Refining Search:** If the user corrects you (e.g., "Not that card"), use `get_card_info` again with the corrected name or use `analyze_set_performance` to broaden the scope.
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4. **Profit Hunting:** If a user asks "What should I buy?", use `find_grading_opportunities` or `get_market_movers`.
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### TONE
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Professional, objective, concise, and user-friendly.
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"""
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agent = ReActAgent.from_tools(
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tools=tools,
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llm=llm,
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verbose=True,
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max_iterations=10
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)
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# Set system prompt via chat history
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agent.chat_history = [ChatMessage(role="system", content=system_prompt)]
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return agent
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agent = initialize_agent()
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# --- 2. Page Configuration ---
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st.set_page_config(page_title="cAsh Robo-Advisor", page_icon="🤖", layout="wide")
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# --- 3. Chat Logic Function ---
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def process_chat(user_message):
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"""Handles the synchronous call to the agent and updates session state."""
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st.session_state.messages.append({"role": "user", "content": user_message})
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# Placeholder for the assistant's response while it thinks
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with st.chat_message("assistant"):
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response_placeholder = st.empty()
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with st.spinner("Thinking..."):
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try:
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# Execute the agent chat
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response = agent.chat(user_message)
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final_text = str(response)
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response_placeholder.markdown(final_text)
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st.session_state.messages.append({"role": "assistant", "content": final_text})
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except Exception as e:
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error_msg = f"⚠️ **Agent Error:** {str(e)}"
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response_placeholder.markdown(error_msg)
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st.error(f"Full error: {repr(e)}")
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# --- 4. Sidebar & UI Styling ---
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st.title("🤖 cAsh Robo-Advisor")
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st.sidebar.header("Example Queries")
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examples = [
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"What are the top 3 grading opportunities right now?",
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"Is investing in Charizard VMAX risky?",
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"What cards are trending down?",
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"Show me profitable cards by Tomokazu Komiya."
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]
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# Sidebar helper: clicking an example sends it immediately
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for ex in examples:
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if st.sidebar.button(ex):
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process_chat(ex)
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st.rerun()
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# --- 5. Main Chat Interface ---
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# Display existing messages
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# User Input
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if prompt := st.chat_input("Ask your Pokemon Quants advisor..."):
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with st.chat_message("user"):
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st.markdown(prompt)
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process_chat(prompt)
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