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requirements.txt

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+streamlit_chat
+streamlit~=1.23.1
+langchain~=0.0.225
+sentence_transformers
+utils~=1.0.1
+cohere~=4.11.2
+openai~=0.27.8
+pypdf2~=3.0.1
+tiktoken~=0.4.0
+PyPDF2~=3.0.1
+langchain~=0.0.231
+chromadb~=0.3.27
+yfinance~=0.2.25
+yahooquery~=2.3.1
+google-search-results~=2.4.2
+fpdf~=1.7.2
+pypdf~=3.12.1

streamlit.py

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+import streamlit as st
+
+# Import necessary libraries
+import os
+import requests
+import json
+import yfinance as yf
+from yahooquery import Ticker
+from fpdf import FPDF
+from typing import List, Union
+import re
+
+# Import components from langchain and other libraries
+from langchain.agents import load_tools, initialize_agent, create_csv_agent, AgentType, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
+from langchain.agents.agent_toolkits import create_vectorstore_agent, VectorStoreToolkit, VectorStoreInfo
+from langchain.llms import OpenAI, Cohere
+from langchain.document_loaders import PyPDFLoader, TextLoader, DirectoryLoader
+from langchain.vectorstores import Chroma
+from langchain.embeddings import CohereEmbeddings, OpenAIEmbeddings
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.chains import RetrievalQA
+from langchain.evaluation.qa import QAEvalChain
+from langchain.prompts import StringPromptTemplate
+from langchain.tools.python.tool import PythonREPLTool
+from langchain.python import PythonREPL
+from langchain import LLMMathChain, SerpAPIWrapper, LLMChain
+from langchain.schema import AgentAction, AgentFinish, OutputParserException
+
+# Set up Streamlit header and sidebar
+st.header('FinAI')
+mod = None
+with st.sidebar:
+    with st.form('Cohere/OpenAI'):
+        # User selects the model (OpenAI/Cohere) and enters API keys
+        model = st.radio('Choose OpenAI/Cohere', ('OpenAI', 'Cohere'))
+        api_key = st.text_input('Enter API key', type="password")
+        serpAI_key = st.text_input('Enter SERPAIAPI key', type="password")
+        submitted = st.form_submit_button("Submit")
+
+# Check if API key is provided and set up the language model accordingly
+if api_key:
+    if model == 'OpenAI':
+        os.environ["OPENAI_API_KEY"] = api_key
+        llm = OpenAI(temperature=0.3)
+        mod = 'OpenAI'
+        os.environ["SERPAPI_API_KEY"] = serpAI_key
+    elif model == 'Cohere':
+        os.environ["Cohere_API_KEY"] = api_key
+        llm = Cohere(cohere_api_key=api_key)
+        mod = 'Cohere'
+        os.environ["SERPAPI_API_KEY"] = serpAI_key
+
+# Helper function to get company news from SERP API
+def get_company_news(company_name):
+    # Set the parameters for the SERP API request
+    params = {
+        "engine": "google",
+        "tbm": "nws",
+        "q": company_name,
+        "api_key": os.environ["SERPAPI_API_KEY"],
+    }
+
+    # Send the request and get the response data in JSON format
+    response = requests.get('https://serpapi.com/search', params=params)
+    data = response.json()
+
+    return data.get('news_results')
+
+# Helper function to write news data to a file
+def write_news_to_file(news, filename):
+    with open(filename, 'w') as file:
+        for news_item in news:
+            if news_item is not None:
+                title = news_item.get('title', 'No title')
+                link = news_item.get('link', 'No link')
+                date = news_item.get('date', 'No date')
+                file.write(f"Title: {title}\n")
+                file.write(f"Link: {link}\n")
+                file.write(f"Date: {date}\n\n")
+
+# Helper function to get stock evolution data from Yahoo Finance API
+def get_stock_evolution(company_name, period="1y"):
+    # Get the stock information using yfinance
+    stock = yf.Ticker(company_name)
+
+    # Get historical market data for the specified period
+    hist = stock.history(period=period)
+
+    # Convert the DataFrame to a string with a specific format
+    data_string = hist.to_string()
+
+    # Save the historical market data to a CSV file
+    hist.to_csv('stocks_data.csv')
+
+    # Get financials data
+    fin = stock.get_financials()
+    fin.to_csv('fin_data.csv')
+
+    # Append the string to the "investment.txt" file
+    with open("investment.txt", "a") as file:
+        file.write(f"\nStock Evolution for {company_name}:\n")
+        file.write(data_string)
+        file.write("\n")
+
+# Helper function to get financial statements from Yahoo Finance API
+def get_financial_statements(ticker):
+    # Create a Ticker object
+    company = Ticker(ticker)
+
+    # Get financial data for balance sheet, cash flow, income statement, and valuation measures
+    balance_sheet = company.balance_sheet().to_string()
+    cash_flow = company.cash_flow(trailing=False).to_string()
+    income_statement = company.income_statement().to_string()
+    valuation_measures = str(company.valuation_measures)  # This one might already be a dictionary or string
+
+    # Write data to "investment.txt" file
+    with open("investment.txt", "a") as file:
+        file.write("\nBalance Sheet\n")
+        file.write(balance_sheet)
+        file.write("\nCash Flow\n")
+        file.write(cash_flow)
+        file.write("\nIncome Statement\n")
+        file.write(income_statement)
+        file.write("\nValuation Measures\n")
+        file.write(valuation_measures)
+
+# Helper function to fetch data from different sources and store it in "investment.txt" file
+def get_data(company_name, company_ticker, period="1y", filename="investment.txt"):
+    news = get_company_news(company_name)
+    if news:
+        write_news_to_file(news, filename)
+    else:
+        print("No news found.")
+
+    get_stock_evolution(company_ticker, period)
+
+    get_financial_statements(company_ticker)
+
+# Helper function to call the language model for financial analysis based on user request
+def financial_analyst(request):
+    # Print the request received from the user
+    print(f"Received request: {request}")
+
+    # Use OpenAI GPT-3.5 Turbo model to analyze the request and generate a response
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-16k",
+        messages=[{
+            "role": "user",
+            "content": f"Given the user request, what is the company name and the company stock ticker ?: {request}?"
+        }],
+        functions=[{
+            "name": "get_data",
+            "description": "Get financial data on a specific company for investment purposes",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "company_name": {
+                        "type": "string",
+                        "description": "The name of the company",
+                    },
+                    "company_ticker": {
+                        "type": "string",
+                        "description": "The ticker of the stock of the company"
+                    },
+                    "period": {
+                        "type": "string",
+                        "description": "The period of analysis"
+                    },
+                    "filename": {
+                        "type": "string",
+                        "description": "The filename to store data"
+                    }
+                },
+                "required": ["company_name", "company_ticker"],
+            },
+        }],
+        function_call={"name": "get_data"},
+    )
+
+    # Extract the arguments and company information from the response
+    message = response["choices"][0]["message"]
+    if message.get("function_call"):
+        arguments = json.loads(message["function_call"]["arguments"])
+        company_name = arguments["company_name"]
+        company_ticker = arguments["company_ticker"]
+
+        # Call the function to fetch and store financial data
+        get_data(company_name, company_ticker)
+
+        # Read the contents of the "investment.txt" file for the response
+        with open("investment.txt", "r") as file:
+            content = file.read()[:14000]
+
+        # Use OpenAI GPT-3.5 Turbo model again to provide a detailed investment thesis
+        second_response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo-16k",
+            messages=[
+                {
+                    "role": "user",
+                    "content": request
+                },
+                message,
+                {
+                    "role": "system",
+                    "content": """write a detailed investment thesis to answer
+                      the user request. Provide numbers to justify
+                      your assertions, a lot ideally. Never mention
+                      something like this:
+                      However, it is essential to consider your own risk
+                      tolerance, financial goals, and time horizon before
+                      making any investment decisions. It is recommended
+                      to consult with a financial advisor or do further
+                      research to gain more insights into the company's
+                      fundamentals and market trends. The user
+                      already knows that"""
+                },
+                {
+                    "role": "assistant",
+                    "content": content,
+                },
+            ],
+        )
+
+        return second_response["choices"][0]["message"]["content"]
+
+# Helper function to generate a PDF with text and images
+def generate_pdf(text, image_paths):
+    # Create a FPDF object
+    pdf = FPDF()
+
+    # Add a page
+    pdf.add_page()
+
+    # Set style and size of font for the PDF
+    pdf.set_font("Arial", size=12)
+
+    # Set left and right margins
+    pdf.set_left_margin(20)
+    pdf.set_right_margin(20)
+
+    # Add multi-cell with line break for the text
+    pdf.multi_cell(0, 10, text)
+
+    # Move to the next line after the text
+    pdf.ln()
+
+    # Add a new page for the images
+    pdf.add_page()
+
+    # Add the first image to the PDF
+    pdf.image(image_paths[0], x=20, y=pdf.get_y(), w=175)
+
+    # Calculate the y-coordinate for the second image
+    second_image_y = pdf.get_y() + 150
+
+    # Add the second image to the PDF
+    pdf.image(image_paths[1], x=20, y=second_image_y, w=175)
+
+    # Save the PDF with the given file name
+    pdf.output("output.pdf")
+
+# Helper function to generate graphs using langchain and save as images
+def graphs(path, prompt):
+    agent = create_csv_agent(
+        OpenAI(temperature=0),
+        path,
+        verbose=True,
+        agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+    )
+    agent.run(prompt)
+
+
+# Category 1: Revenue and Sales
+revenue_sales_questions = [
+    "What was the total revenue generated by the company during the fiscal year?",
+    "How does the revenue of the current year compare to the previous year?",
+    "Which product/service contributed the most to the company's revenue?",
+    "Did the company experience any significant changes in sales volume or pricing?",
+    "Are there any notable trends or patterns in the revenue growth of the company over the past few years?",
+    "What were the geographical regions or markets where the company generated the highest revenue?",
+    "Has the company introduced any new revenue streams or business lines?",
+    "Were there any extraordinary events or factors that affected the company's revenue performance?",
+    "How does the revenue composition of the company compare to its competitors in the industry?",
+    "Are there any forecasts or projections for future revenue growth provided in the report?"
+]
+
+# Category 2: Expenses and Costs
+expenses_costs_questions = [
+    "What were the major expense categories for the company during the fiscal year?",
+    "How do the expenses of the current year compare to the previous year?",
+    "Did the company implement any cost-saving measures or efficiency improvements?",
+    "Were there any significant changes in the cost of raw materials or production inputs?",
+    "How does the company's expense ratio compare to industry benchmarks?",
+    "Did the company incur any one-time or non-recurring expenses during the year?",
+    "Are there any trends or patterns in the company's cost structure over the past few years?",
+    "Has the company invested in research and development (R&D) or capital expenditures?",
+    "What were the employee-related costs and benefits provided by the company?",
+    "Are there any forecasts or projections for future cost management initiatives provided in the report?"
+]
+
+# Category 3: Profitability and Financial Ratios
+profitability_ratios_questions = [
+    "What was the net profit or net income generated by the company during the fiscal year?",
+    "How does the profitability of the current year compare to the previous year?",
+    "What is the company's gross profit margin and how has it changed over time?",
+    "Did the company experience any changes in operating profit or operating margin?",
+    "What is the return on assets (ROA) and return on equity (ROE) for the company?",
+    "Has the company improved its profitability compared to its competitors in the industry?",
+    "Are there any trends or patterns in the company's profitability ratios over the past few years?",
+    "Did the company face any challenges or risks that impacted its profitability?",
+    "How does the company's profitability ratios compare to industry benchmarks?",
+    "Are there any forecasts or projections for future profitability provided in the report?"
+]
+
+# Category 4: Cash Flow and Liquidity
+cash_flow_liquidity_questions = [
+    "What was the operating cash flow generated by the company during the fiscal year?",
+    "How does the cash flow from operations of the current year compare to the previous year?",
+    "Did the company experience any significant changes in its working capital management?",
+    "What were the major sources and uses of cash for the company during the year?",
+    "Has the company made any significant investments or divestments during the year?",
+    "How does the company's cash conversion cycle compare to industry benchmarks?",
+    "Are there any trends or patterns in the company's cash flow statement over the past few years?",
+    "What is the company's current ratio and quick ratio for assessing liquidity?",
+    "Did the company undertake any debt financing or equity financing activities?",
+    "Are there any forecasts or projections for future cash flow or liquidity provided in the report?"
+]
+# Define the options for the dropdown menu
+categories = [
+    "Revenue and Sales",
+    "Expenses and Costs",
+    "Profitability and Financial Ratios",
+    "Cash Flow and Liquidity"
+]
+# Create a textbox to enter company's name
+company_name = st.text_input("Enter the company's name:")
+
+uploaded_file = st.file_uploader(f"Upload an Annual Report of {company_name} if available (PDF).", type=['pdf'])
+toolkit = None
+if uploaded_file is not None: 
+        st.write("File uploaded successfully!")
+        file_contents = uploaded_file.read()
+        save_path = uploaded_file.name
+        with open(save_path, "wb") as f:
+            f.write(file_contents)
+        print(save_path)
+        loader = PyPDFLoader(save_path) #Step 1.1
+        documents = loader.load()
+        #1.2
+        text_splitter = CharacterTextSplitter(chunk_size=2000, chunk_overlap=0) #Splitting the text and creating chunks
+        docs = text_splitter.split_documents(documents)
+        if(mod=="OpenAI"):
+            embeddings = OpenAIEmbeddings()
+        if(mod=="Cohere"):
+            embeddings = CohereEmbeddings(cohere_api_key=api_key)
+        store = Chroma.from_documents(docs,embeddings)
+        vectorstore_info = VectorStoreInfo(
+            name="starbucks",
+            description="Starbucks financials",
+            vectorstore=store,
+        )
+        # llm = OpenAI(temperature=0.3)
+        toolkit = VectorStoreToolkit(llm=llm,vectorstore_info=vectorstore_info)
+
+
+# Create a dropdown using the `selectbox` function
+selected_category = st.selectbox("Select a category:", categories)
+
+if(selected_category=="Revenue and Sales"):
+    selected_ques= st.selectbox("Select a category:", revenue_sales_questions)
+if(selected_category=="Expenses and Costs"):
+    selected_ques= st.selectbox("Select a category:", expenses_costs_questions)
+if(selected_category=="Profitability and Financial Ratios"):
+    selected_ques= st.selectbox("Select a category:", profitability_ratios_questions)
+if(selected_category=="Cash Flow and Liquidity"):
+    selected_ques= st.selectbox("Select a category:", cash_flow_liquidity_questions)
+
+# st.write(selected_ques)
+
+ans=[]
+if (st.button("Submit")):
+    output_res=[]
+
+    st.write("Company Name: " + company_name)
+
+    # llm = OpenAI(temperature=0.3)
+    tools = load_tools(["serpapi", "llm-math"], llm=llm)
+    agent = initialize_agent(llm = llm,
+                    toolkit = toolkit,
+                    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+                    tools = tools,
+                    verbose=True)
+    if(mod=='OpenAI'):
+        ans = financial_analyst(company_name)
+        st.write("Question asked by the user is " + selected_ques)
+        response = agent.run(selected_ques+f" Consider {ans}")
+        st.write(response)
+        st.write("Report")
+        st.write(ans)
+        prompt2="Make a line graph with Date as the x label and Closing Value as the y label and save the graph as an image file name of file as 'img2.png'"
+        prompt1="Make a line graph, x axis lables should be rotation = 90, save the graph as an image file and name of file as 'img1.png'"
+        graphs('stocks_data.csv',prompt1)
+        graphs('fin_data.csv',prompt2)
+        image_path = ['img1.png','img2.png']
+        generate_pdf(ans,image_path)
+        st.write("PDF generated successfully! Click below to download.")
+        # Download link
+        with open("output.pdf", "rb") as f:
+            st.download_button("Download PDF", f.read(), file_name="output.pdf", mime="application/pdf")
+    else:
+        try:
+            st.write("Question asked by the user is " + selected_ques)
+            response = agent.run(f"As a financial data analyst, your task is to thoroughly analyze \
+            the annual financial report of a company and provide accurate answers based solely on the data presented \
+            in the document. It is important to strictly adhere to the information provided in the report and \
+            refrain from making any assumptions or speculations. \
+            If necessary, you may utilize appropriate tools and formulas to derive the required answers.\
+            prompt = {selected_ques}")
+            print(response)
+            # response = agent.run(selected_ques)
+            st.write(response)
+        except:
+            st.write("Cohere Key Cannot give out the desired outputs. Pls provide OpenAI key for better results or try again!")