Removed app_memory, StockPredictLLM

StockPredictionLLM.xml

@@ -0,0 +1,34 @@
+<mxfile host="app.diagrams.net">
+  <diagram name="Page-1">
+    <mxGraphModel dx="819" dy="641" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+        <mxCell id="2" value="Data Collection" style="rounded=1;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="280" y="20" width="200" height="100" as="geometry" />
+        </mxCell>
+        <mxCell id="3" value="Historical Data&#xa;News Data&#xa;Stock Ticker Information" style="rounded=1;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="320" y="60" width="120" height="80" as="geometry" />
+        </mxCell>
+        <mxCell id="4" value="Data Analyst" style="rounded=1;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="280" y="140" width="200" height="60" as="geometry" />
+        </mxCell>
+        <mxCell id="5" value="Analyzes and preprocesses the data" style="rounded=1;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="320" y="160" width="120" height="40" as="geometry" />
+        </mxCell>
+        <mxCell id="6" value="ARIMA Model (using Historical Data)" style="rounded=1;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="80" y="240" width="200" height="60" as="geometry" />
+        </mxCell>
+        <mxCell id="7" value="Random Forest Model (using News Data)" style="rounded=1;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="480" y="240" width="200" height="60" as="geometry" />
+        </mxCell>
+        <mxCell id="8" value="Prediction Output" style="rounded=1;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="280" y="340" width="200" height="60" as="geometry" />
+        </mxCell>
+        <mxCell id="9" value="Results from ARIMA and Random Forest models" style="rounded=1;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="320" y="360" width="120" height="40" as="geometry" />
+        </mxCell>
+        <mxCell id="10" value="Evaluator" style="rounded=1;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="280" y="440" width="200" height="60" as="geometry" />
+        </mxCell>
+        <mxCell id="11" value="Evaluates the prediction outputs" style="rounded=1;white

app_test3_memory.py

@@ -1,306 +0,0 @@
-from langchain_experimental.agents import create_pandas_dataframe_agent
-from langchain.llms import OpenAI
-import chainlit as cl
-from plotly.subplots import make_subplots
-import utils as u
-from langchain.agents import AgentExecutor, create_openai_tools_agent
-from langchain_core.messages import BaseMessage, HumanMessage
-from langchain_openai import ChatOpenAI
-from langchain_core.output_parsers.openai_functions import JsonOutputFunctionsParser
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from tools import data_analyst
-from tools import stock_sentiment_evalutor
-import functools
-from typing import Annotated
-import operator
-from typing import Sequence, TypedDict
-from langchain.agents import initialize_agent,  Tool
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langgraph.graph import END, StateGraph
-import numpy as np
-import pandas as pd
-from dotenv import load_dotenv
-import os
-import yfinance as yf
-import functools
-from typing import Annotated
-import operator
-from typing import Sequence, TypedDict
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langgraph.graph import END, StateGraph
-from tools import data_analyst, forecasting_expert_arima, forecasting_expert_rf, evaluator, stock_sentiment_evalutor, investment_advisor
-from chainlit.input_widget import Select
-import matplotlib.pyplot as plt
-from langgraph.checkpoint.memory import MemorySaver
-
-load_dotenv()
-HF_ACCESS_TOKEN = os.environ["HF_ACCESS_TOKEN"]
-DAYS_TO_FETCH_NEWS = os.environ["DAYS_TO_FETCH_NEWS"]
-NO_OF_NEWS_ARTICLES_TO_FETCH = os.environ["NO_OF_NEWS_ARTICLES_TO_FETCH"]
-OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
-
-from GoogleNews import GoogleNews
-
-def search_news(stockticker):
-    """Useful to search the internet for news about a given topic and return relevant results."""
-    # Set the number of top news results to return
-    googlenews = GoogleNews()
-    googlenews.set_period('7d')
-    googlenews.get_news(stockticker)
-    result_string=googlenews.get_texts()
-
-    return result_string
-
-
-def create_agent(llm: ChatOpenAI, tools: list, system_prompt: str):
-    # Each worker node will be given a name and some tools.
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            (
-                "system",
-                system_prompt,
-            ),
-            MessagesPlaceholder(variable_name="messages"),
-            MessagesPlaceholder(variable_name="agent_scratchpad"),
-        ]
-    )
-    agent = create_openai_tools_agent(llm, tools, prompt)
-    executor = AgentExecutor(agent=agent, tools=tools)
-    return executor
-
-
-def agent_node(state, agent, name):
-    result = agent.invoke(state)
-    return {"messages": [HumanMessage(content=result["output"], name=name)]}
-
-llm = ChatOpenAI(model="gpt-3.5-turbo")
-
-#======================== AGENTS ==================================
-# The agent state is the input to each node in the graph
-class AgentState(TypedDict):
-    # The annotation tells the graph that new messages will always
-    # be added to the current states
-    messages: Annotated[Sequence[BaseMessage], operator.add]
-    # The 'next' field indicates where to route to next
-    next: str
-
-# DATA ANALYST
-prompt_data_analyst="You are a stock data analyst.\
-                Provide correct stock ticker from Yahoo Finance.\
-                Expected output: stocticker.\
-                Provide it in the following format: >>stockticker>> \
-                for example: >>AAPL>>"
-                
-tools_data_analyst=data_analyst.data_analyst_tools()
-data_agent = create_agent(
-    llm,
-    tools_data_analyst,
-    prompt_data_analyst)
-get_historical_prices = functools.partial(agent_node, agent=data_agent, name="Data_analyst")
-
-#ARIMA Forecasting expert
-prompt_forecasting_expert_arima="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-                You are stock prediction expert, \
-               take historical stock data from message and train the ARIMA model from statsmodels Python library on the last week,then provide prediction for the 'Close' price for the next day.\
-               Give the value for mae_arima to Evaluator.\
-               Expected output:list of predicted prices with predicted dates for a selected stock ticker and mae_arima value.\n
-               <|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
-
-tools_forecasting_expert_arima=forecasting_expert_arima.forecasting_expert_arima_tools()
-code_forecasting_arima = create_agent(
-    llm,
-    tools_forecasting_expert_arima,
-    prompt_forecasting_expert_arima,
-)
-predict_future_prices_arima = functools.partial(agent_node, agent=code_forecasting_arima, name="Forecasting_expert_ARIMA")
-
-# RF  Forecasting expert
-prompt_forecasting_expert_random_forest="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-                You are stock prediction expert, \
-               take historical stock data from message and train the Random forest model from statsmodels Python library on the last week,then provide prediction for the 'Close' price for the next day.\
-               Give the value for mae_rf to Evaluator.\
-               Expected output:list of predicted prices with predicted dates for a selected stock ticker and mae_rf value.\n
-               <|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
-
-tools_forecasting_expert_random_forest=forecasting_expert_rf.forecasting_expert_rf_tools()
-code_forecasting_random_forest = create_agent(
-    llm,
-    tools_forecasting_expert_random_forest,
-    prompt_forecasting_expert_random_forest,
-)
-predict_future_prices_random_forest = functools.partial(agent_node, agent=code_forecasting_random_forest, name="Forecasting_expert_random_forest")
-
-# EVALUATOR
-prompt_evaluator="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-                You are an evaluator retrieve arima_prediction and arima mean average error from forecasting expert arima and rf_prediction and mean average error for random forest from forecasting expert random forest\
-                print final prediction number. 
-                Next, compare prediction price and current price to provide reccommendation if he should buy/sell/hold the stock. \
-                 Expected output: one value for the prediction, explain why you have selected this value, reccommendation  buy or sell stock and why.\
-                  <|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
-
-tools_evaluate=evaluator.evaluator_tools()
-code_evaluate = create_agent(
-    llm,
-    tools_evaluate,
-    prompt_evaluator,
-)
-evaluate = functools.partial(agent_node, agent=code_evaluate, name="Evaluator")
-
-#Stock Sentiment Evaluator
-prompt_sentiment_evaluator="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-                You are a stock sentiment evaluator, that takes in a stock ticker and 
-                then using your StockSentimentAnalysis tool retrieve news for the stock based on the configured data range starting today and their corresponding sentiment,
-                alongwith the most dominant sentiment for the stock\
-                Expected output: List ALL stock news and their sentiment from the StockSentimentAnalysis tool response, and the dominant sentiment for the stock also in StockSentimentAnalysis tool response as is without change\
-                Also ensure you use the tool only once and do not make changes to messages
-                Also you are not to change the response from the tool\
-                  <|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
-
-tools_sentiment_evaluator=stock_sentiment_evalutor.sentimental_analysis_tools()
-sentiment_evaluator = create_agent(
-    llm,
-    tools_sentiment_evaluator,
-    prompt_sentiment_evaluator,
-)
-evaluate_sentiment = functools.partial(agent_node, agent=sentiment_evaluator, name="Sentiment_Evaluator")
-
-# Investment advisor
-prompt_inv_advisor="""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-                Provide personalized investment advice and recommendations.\
-                Consider user input message for the latest news on the stock.\
-                Provide overall sentiment of the news Positive/Negative/Neutral, and recommend if  the user should invest in such stock.\
-                MUST finish the analysis with a summary on the latest news from the user input on the stock!\
-                  <|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
-
-tools_reccommend=investment_advisor.investment_advisor_tools()
-
-code_inv_advisor = create_agent(
-    llm,
-    tools_reccommend,
-    prompt_inv_advisor,
-)
-
-reccommend = functools.partial(agent_node, agent=code_inv_advisor, name="Investment_advisor")
-
-workflow_data = StateGraph(AgentState)
-workflow_data.add_node("Data_analyst", get_historical_prices)
-workflow_data.set_entry_point("Data_analyst")
-graph_data=workflow_data.compile()
-
-workflow = StateGraph(AgentState)
-#workflow.add_node("Data_analyst", get_historical_prices)
-workflow.add_node("Forecasting_expert_random_forest", predict_future_prices_random_forest)
-workflow.add_node("Forecasting_expert_ARIMA", predict_future_prices_arima)
-workflow.add_node("Evaluator", evaluate)
-
-
-# Finally, add entrypoint
-workflow.set_entry_point("Forecasting_expert_random_forest")
-workflow.add_edge("Forecasting_expert_random_forest","Forecasting_expert_ARIMA")
-workflow.add_edge("Forecasting_expert_ARIMA","Evaluator")
-workflow.add_edge("Evaluator",END)
-graph = workflow.compile()
-
-#Print graph
-#graph.get_graph().print_ascii()
-
-memory = MemorySaver()
-workflow_news = StateGraph(AgentState)
-workflow_news.add_node("Investment_advisor", reccommend)
-workflow_news.set_entry_point("Investment_advisor")
-workflow_news.add_edge("Investment_advisor",END)
-graph_news = workflow_news.compile(checkpointer=memory)
-
-from langchain_core.runnables import RunnableConfig
-@cl.on_chat_start
-async def on_chat_start():
-    cl.user_session.set("counter", 0)
-    # Sending an image with the local file path
-    elements = [
-    cl.Image(name="image1", display="inline", path="./good_day.jpg",size="large")
-    ]
-    await cl.Message(content="Hello there, Welcome to ##StockSavyy!", elements=elements).send()
-    await cl.Message(content="Tell me the stockticker you want me to analyze.").send()
-
-@cl.on_message
-async def main(message: cl.Message):
-    #"what is the weather in sf"
-    counter = cl.user_session.get("counter")
-    counter += 1
-    cl.user_session.set("counter", counter)
-    await cl.Message(content=f"You sent {counter} message(s)!").send()
-    if counter==1:
-        inputs = {"messages": [HumanMessage(content=message.content)]}
-        
-        res_data = graph_data.invoke(inputs, config=RunnableConfig(callbacks=[
-            cl.LangchainCallbackHandler(
-                to_ignore=["ChannelRead", "RunnableLambda", "ChannelWrite", "__start__", "_execute"]
-                # can add more into the to_ignore: "agent:edges", "call_model"
-                # to_keep=
-
-            )]))
-        #print(res_data)
-        await cl.Message(content=res_data["messages"][-1].content).send()
-        #print('ticker',str(res_data).split(">>"))
-        if len(str(res_data).split(">>")[1])<10:
-            stockticker=(str(res_data).split(">>")[1])
-        else:
-            stockticker=(str(res_data).split(">>")[0])
-        #print('ticker1',stockticker)
-        print('here')
-        df=u.get_stock_price(stockticker)
-        df_history=u.historical_stock_prices(stockticker,90)
-        df_history_to_msg1=eval(str(list((pd.DataFrame(df_history['Close'].values.reshape(1, -1)[0]).T).iloc[0,:])))
-        inputs_all = {"messages": [HumanMessage(content=(f"Predict {stockticker}, historical prices are: {df_history_to_msg1}."))]}
-        #print(inputs_all)
-        df_history=pd.DataFrame(df_history)
-        df_history['stockticker']=np.repeat(stockticker,len(df_history))
-        df_history.to_csv('df_history.csv')
-
-        res = graph.invoke(inputs_all, config=RunnableConfig(callbacks=[
-            cl.LangchainCallbackHandler(
-                to_ignore=["ChannelRead", "RunnableLambda", "ChannelWrite", "__start__", "_execute"]
-                # can add more into the to_ignore: "agent:edges", "call_model"
-                # to_keep=
-
-            )]))
-        await cl.Message(content= res["messages"][-2].content + '\n\n' + res["messages"][-1].content).send()
-        
-    df_history=pd.read_csv('df_history.csv')
-    stockticker=str(df_history['stockticker'][0])
-    df_search=search_news(stockticker)
-    with open('search_news.txt', 'w') as a:
-            a.write(str(df_search[0:10]))
-    file = open("search_news.txt", "r")
-    df_search = file.read()
-    print(stockticker)
-
-    config = {"configurable": {"thread_id": "1"}}
-    inputs_news = {"messages": [HumanMessage(content=(f"Summarize articles for {stockticker} to write 2 sentences about following articles: {df_search}."))]}
-    k=0
-    for event in graph_news.stream(inputs_news, config, stream_mode="values"):
-            k+=1
-            if k>1:
-                await cl.Message(content=event["messages"][-1].content).send()
-        
-
-    if counter==1:
-        df=u.historical_stock_prices(stockticker,90)
-        df=u.calculate_MACD(df, fast_period=12, slow_period=26, signal_period=9)
-        fig = u.plot_macd2(df)
-        
-        if fig:
-            elements = [cl.Pyplot(name="plot", figure=fig, display="inline",size="large"),
-            ]
-            await cl.Message(
-                content="Here is the MACD plot",
-                elements=elements,
-            ).send()
-        else:
-            await cl.Message(
-                content="Failed to generate the MACD plot."
-            ).send()
-
-
-
-    

chainlit.md

@@ -1 +1,20 @@
-# Welcome to AskAnyQuery Bot!!
+---
+title: StockSavvy
+emoji: 📉
+colorFrom: pink
+colorTo: yellow
+sdk: docker
+pinned: false
+app_port: 7860
+---
+
+## 🤖 StockSavvy
+
+> Forecast and analyze stocks and make $$$!!!. Ask me anything about stocks.
+
+##  Data from open-source data: Yahoo finance + Sentiment analysis.
+LangGraph/Langchain/RAG/Chainlit/OpenAI
+---
+
+
+> :wave: Code originates mainly from the amazing AI Makerspace Bootcamp!!! For more see   [https://github.com/sanjeevl10/StockSavvyFinal]