app.py

import os
import time
import uuid
from typing import List, Tuple, Optional, Dict, Union
import google.generativeai as genai
import gradio as gr
from PIL import Image
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain, SequentialChain

from textwrap import dedent
import google.generativeai as genai


import yfinance as yf
from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
from pypfopt import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns
from pypfopt import plotting
import copy
import numpy as np
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
from datetime import datetime
import datetime


# Tool import
from crewai.tools.gemini_tools import GeminiSearchTools
from langchain.tools.yahoo_finance_news import YahooFinanceNewsTool
from crewai.tools.browser_tools import BrowserTools
from crewai.tools.sec_tools import SECTools

# Google Langchain
from langchain_google_genai import GoogleGenerativeAI

#Crew imports
from crewai import Agent, Task, Crew, Process

# Retrieve API Key from Environment Variable
GOOGLE_AI_STUDIO = os.environ.get('GOOGLE_API_KEY')

# Ensure the API key is available
if not GOOGLE_AI_STUDIO:
    raise ValueError("API key not found. Please set the GOOGLE_AI_STUDIO2 environment variable.")





# LangChain function for company analysis

def company_analysis(api_key: str, company_name: str) -> dict:
    
    os.environ['OPENAI_API_KEY'] = api_key  # Set the OpenAI API key as an environment variable
    llm = ChatOpenAI()
    '''
    # Identify the email's language
    template1 = "Return the language this email is written in:\n{email}.\nONLY return the language it was written in."
    prompt1 = ChatPromptTemplate.from_template(template1)
    chain_1 = LLMChain(llm=llm, prompt=prompt1, output_key="language")

    # Translate the email to English
    template2 = "Translate this email from {language} to English. Here is the email:\n" + email
    prompt2 = ChatPromptTemplate.from_template(template2)
    chain_2 = LLMChain(llm=llm, prompt=prompt2, output_key="translated_email")

    # Provide a summary in English
    template3 = "Create a short summary of this email:\n{translated_email}"
    prompt3 = ChatPromptTemplate.from_template(template3)
    chain_3 = LLMChain(llm=llm, prompt=prompt3, output_key="summary")

    # Provide a reply in English
    template4 = "Reply to the sender of the email giving a plausible reply based on the {summary} and a promise to address issues"
    prompt4 = ChatPromptTemplate.from_template(template4)
    chain_4 = LLMChain(llm=llm, prompt=prompt4, output_key="reply")

    # Provide a translation back to the original language
    template5 = "Translate the {reply} back to the original {language} of the email."
    prompt5 = ChatPromptTemplate.from_template(template5)
    chain_5 = LLMChain(llm=llm, prompt=prompt5, output_key="translated_reply")
    

    seq_chain = SequentialChain(chains=[chain_1, chain_2, chain_3, chain_4, chain_5],
                                input_variables=['email'],
                                output_variables=['language', 'translated_email', 'summary', 'reply', 'translated_reply'],
                                verbose=True)
    '''                            
    return seq_chain(email)







print("google-generativeai:", genai.__version__)

GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")

TITLE_INTRO = """<h1 align="center">Introduction to Financial Manager</h1>"""

TITLE1 = """<h1 align="center">Company Analysis</h1>"""
TITLE2 = """<h1 align="center">Investment Strategy</h1>"""
TITLE3 = """<h1 align="center">Profit Prophet</h1>"""

SUBTITLE = """<h2 align="center">Strategy Agent built with Gemini Pro and Gemini Pro Vision API</h2>"""
GETKEY = """
<div style="text-align: center; display: flex; justify-content: center; align-items: center;">
    <span>Get an API key
        <a href="https://makersuite.google.com/app/apikey">GOOGLE API KEY</a>.
    </span>
</div>
"""


AVATAR_IMAGES = (
    None,
    "https://media.roboflow.com/spaces/gemini-icon.png"
)

movie_script_analysis = ""


IMAGE_CACHE_DIRECTORY = "/tmp"
IMAGE_WIDTH = 512
CHAT_HISTORY = List[Tuple[Optional[Union[Tuple[str], str]], Optional[str]]]


def preprocess_stop_sequences(stop_sequences: str) -> Optional[List[str]]:
    if not stop_sequences:
        return None
    return [sequence.strip() for sequence in stop_sequences.split(",")]


def preprocess_image(image: Image.Image) -> Optional[Image.Image]:
    image_height = int(image.height * IMAGE_WIDTH / image.width)
    return image.resize((IMAGE_WIDTH, image_height))


def cache_pil_image(image: Image.Image) -> str:
    image_filename = f"{uuid.uuid4()}.jpeg"
    os.makedirs(IMAGE_CACHE_DIRECTORY, exist_ok=True)
    image_path = os.path.join(IMAGE_CACHE_DIRECTORY, image_filename)
    image.save(image_path, "JPEG")
    return image_path


def preprocess_chat_history(
    history: CHAT_HISTORY
) -> List[Dict[str, Union[str, List[str]]]]:
    messages = []
    for user_message, model_message in history:
        if isinstance(user_message, tuple):
            pass
        elif user_message is not None:
            messages.append({'role': 'user', 'parts': [user_message]})
        if model_message is not None:
            messages.append({'role': 'model', 'parts': [model_message]})
    return messages


def upload(files: Optional[List[str]], chatbot: CHAT_HISTORY) -> CHAT_HISTORY:
    for file in files:
        image = Image.open(file).convert('RGB')
        image = preprocess_image(image)
        image_path = cache_pil_image(image)
        chatbot.append(((image_path,), None))
    return chatbot


def user(text_prompt: str, chatbot: CHAT_HISTORY):
    if text_prompt:
        chatbot.append((text_prompt, None))
    return "", chatbot


def bot(
    google_key: str,
    files: Optional[List[str]],
    temperature: float,
    max_output_tokens: int,
    stop_sequences: str,
    top_k: int,
    top_p: float,
    chatbot: CHAT_HISTORY
):
    if len(chatbot) == 0:
        return chatbot

    google_key = google_key if google_key else GOOGLE_API_KEY
    if not google_key:
        raise ValueError(
            "GOOGLE_API_KEY is not set. "
            "Please follow the instructions in the README to set it up.")

    genai.configure(api_key=google_key)
    generation_config = genai.types.GenerationConfig(
        temperature=temperature,
        max_output_tokens=max_output_tokens,
        stop_sequences=preprocess_stop_sequences(stop_sequences=stop_sequences),
        top_k=top_k,
        top_p=top_p)

    if files:
        text_prompt = [chatbot[-1][0]] \
            if chatbot[-1][0] and isinstance(chatbot[-1][0], str) \
            else []
        image_prompt = [Image.open(file).convert('RGB') for file in files]
        model = genai.GenerativeModel('gemini-pro-vision')
        response = model.generate_content(
            text_prompt + image_prompt,
            stream=True,
            generation_config=generation_config)
    else:
        messages = preprocess_chat_history(chatbot)
        model = genai.GenerativeModel('gemini-pro')
        response = model.generate_content(
            messages,
            stream=True,
            generation_config=generation_config)

    # streaming effect
    chatbot[-1][1] = ""
    for chunk in response:
        for i in range(0, len(chunk.text), 10):
            section = chunk.text[i:i + 10]
            chatbot[-1][1] += section
            time.sleep(0.01)
            yield chatbot


google_key_component = gr.Textbox(
    label="GOOGLE API KEY",
    value="",
    type="password",
    placeholder="...",
    info="You have to provide your own GOOGLE_API_KEY for this app to function properly",
    visible=GOOGLE_API_KEY is None
)
chatbot_component = gr.Chatbot(
    label='Gemini Pro Vision',
    bubble_full_width=False,
    avatar_images=AVATAR_IMAGES,
    scale=2,
    height=400
)
text_prompt_component = gr.Textbox(value=movie_script_analysis,
   show_label=False, autofocus=True, scale=8, lines=8
)




upload_button_component = gr.UploadButton(
    label="Upload Images", file_count="multiple", file_types=["image"], scale=1
)
run_button_component = gr.Button(value="Run", variant="primary", scale=1)

run_button_analysis = gr.Button(value="Run", variant="primary", scale=1)



temperature_component = gr.Slider(
    minimum=0,
    maximum=1.0,
    value=0.4,
    step=0.05,
    label="Temperature",
    info=(
        "Temperature controls the degree of randomness in token selection. Lower "
        "temperatures are good for prompts that expect a true or correct response, "
        "while higher temperatures can lead to more diverse or unexpected results. "
    ))
max_output_tokens_component = gr.Slider(
    minimum=1,
    maximum=2048,
    value=1024,
    step=1,
    label="Token limit",
    info=(
        "Token limit determines the maximum amount of text output from one prompt. A "
        "token is approximately four characters. The default value is 2048."
    ))
stop_sequences_component = gr.Textbox(
    label="Add stop sequence",
    value="",
    type="text",
    placeholder="STOP, END",
    info=(
        "A stop sequence is a series of characters (including spaces) that stops "
        "response generation if the model encounters it. The sequence is not included "
        "as part of the response. You can add up to five stop sequences."
    ))
top_k_component = gr.Slider(
    minimum=1,
    maximum=40,
    value=32,
    step=1,
    label="Top-K",
    info=(
        "Top-k changes how the model selects tokens for output. A top-k of 1 means the "
        "selected token is the most probable among all tokens in the model’s "
        "vocabulary (also called greedy decoding), while a top-k of 3 means that the "
        "next token is selected from among the 3 most probable tokens (using "
        "temperature)."
    ))
top_p_component = gr.Slider(
    minimum=0,
    maximum=1,
    value=1,
    step=0.01,
    label="Top-P",
    info=(
        "Top-p changes how the model selects tokens for output. Tokens are selected "
        "from most probable to least until the sum of their probabilities equals the "
        "top-p value. For example, if tokens A, B, and C have a probability of .3, .2, "
        "and .1 and the top-p value is .5, then the model will select either A or B as "
        "the next token (using temperature). "
    ))

user_inputs = [
    text_prompt_component,
    chatbot_component
]


bot_inputs = [
    google_key_component,
    upload_button_component,
    temperature_component,
    max_output_tokens_component,
    stop_sequences_component,
    top_k_component,
    top_p_component,
    chatbot_component
]





# Gmix ++++++++++++++++++++++++++++++++++++++++++++++++

#Crew imports
from crewai import Agent, Task, Crew, Process

run_button_crewai = gr.Button(value="Run", variant="primary", scale=1)
research_topic_component = gr.Textbox(lines=2, placeholder="Enter Research Topic Here...")

# Set gemini_llm
gemini_llm = GoogleGenerativeAI(model="gemini-pro", google_api_key=GOOGLE_AI_STUDIO)

def crewai_process(research_topic):
    # Define your agents with roles and goals
    researcher = Agent(
        role='Senior Research Analyst',
        goal=f'Uncover cutting-edge developments in {research_topic}',
        backstory="""You are a Senior Research Analyst at a leading think tank.
        Your expertise lies in identifying emerging trends. You have a knack for dissecting complex data and presenting
        actionable insights.""",
        verbose=True,
        allow_delegation=False,
        llm = gemini_llm,
        tools=[
                GeminiSearchTools.gemini_search
      ]

    )
    writer = Agent(
        role='Tech Content Strategist',
        goal='Craft compelling content on tech advancements',
        backstory="""You are a renowned Tech Social Media Content Writer and Strategist, known for your insightful
        and engaging articles on technology and innovation. With a deep understanding of
        the tech industry and how people are impacted by it, you transform complex concepts into compelling narratives.""",
        verbose=True,
        allow_delegation=True,
        llm = gemini_llm
     
        # Add tools and other optional parameters as needed
    )

    # Create tasks for your agents
    task1 = Task(
        description=f"""Conduct a comprehensive analysis of the latest advancements in {research_topic}.
        Compile your findings in a detailed report. Your final answer MUST be a full analysis report""",
        agent=researcher
    )

    task2 = Task(
        description="""Using the insights from the researcher's report, develop an engaging blog
        post that highlights the most significant advancements.
        Your post should be informative yet accessible, catering to a tech-savvy audience.
        Aim for a narrative that captures the essence of these breakthroughs and their
        implications for the future. Your final answer MUST be the full blog post of at least 3 paragraphs.""",
        agent=writer
    )

    # Instantiate your crew with a sequential process
    crew = Crew(
        agents=[researcher, writer],
        tasks=[task1, task2],
        verbose=2,
        process=Process.sequential
    )

    # Get your crew to work!
    result = crew.kickoff()
    
    return result






# Portfolio Analysis +++++++++++++++++++++++++++++++++++

def plot_cum_returns(data, title):    
    daily_cum_returns = 1 + data.dropna().pct_change()
    daily_cum_returns = daily_cum_returns.cumprod()*100
    fig = px.line(daily_cum_returns, title=title)
    return fig

def plot_efficient_frontier_and_max_sharpe(mu, S): 
    # Optimize portfolio for max Sharpe ratio and plot it out with efficient frontier curve
    ef = EfficientFrontier(mu, S)
    fig, ax = plt.subplots(figsize=(6,4))
    ef_max_sharpe = copy.deepcopy(ef)
    plotting.plot_efficient_frontier(ef, ax=ax, show_assets=False)
    # Find the max sharpe portfolio
    ef_max_sharpe.max_sharpe(risk_free_rate=0.02)
    ret_tangent, std_tangent, _ = ef_max_sharpe.portfolio_performance()
    ax.scatter(std_tangent, ret_tangent, marker="*", s=100, c="r", label="Max Sharpe")
    # Generate random portfolios with random weights
    n_samples = 1000
    w = np.random.dirichlet(np.ones(ef.n_assets), n_samples)
    rets = w.dot(ef.expected_returns)
    stds = np.sqrt(np.diag(w @ ef.cov_matrix @ w.T))
    sharpes = rets / stds
    ax.scatter(stds, rets, marker=".", c=sharpes, cmap="viridis_r")
    # Output
    ax.legend()
    return fig

def output_results(start_date, end_date, tickers_string):
    tickers = tickers_string.split(',')
    
    # Get Stock Prices
    stocks_df = yf.download(tickers, start=start_date, end=end_date)['Adj Close']
    
    # Plot Individual Stock Prices
    fig_indiv_prices = px.line(stocks_df, title='Price of Individual Stocks')
        
    # Plot Individual Cumulative Returns
    fig_cum_returns = plot_cum_returns(stocks_df, 'Cumulative Returns of Individual Stocks Starting with $100')
    
    # Calculatge and Plot Correlation Matrix between Stocks
    corr_df = stocks_df.corr().round(2)
    fig_corr = px.imshow(corr_df, text_auto=True, title = 'Correlation between Stocks')

    # Calculate expected returns and sample covariance matrix for portfolio optimization later
    mu = expected_returns.mean_historical_return(stocks_df)
    S = risk_models.sample_cov(stocks_df)

    # Plot efficient frontier curve
    fig_efficient_frontier = plot_efficient_frontier_and_max_sharpe(mu, S)

    # Get optimized weights
    ef = EfficientFrontier(mu, S)
    ef.max_sharpe(risk_free_rate=0.04)
    weights = ef.clean_weights()
    expected_annual_return, annual_volatility, sharpe_ratio = ef.portfolio_performance()
    
    expected_annual_return, annual_volatility, sharpe_ratio = '{}%'.format((expected_annual_return*100).round(2)), \
    '{}%'.format((annual_volatility*100).round(2)), \
    '{}%'.format((sharpe_ratio*100).round(2))
    
    weights_df = pd.DataFrame.from_dict(weights, orient = 'index')
    weights_df = weights_df.reset_index()
    weights_df.columns = ['Tickers', 'Weights']

    # Calculate returns of portfolio with optimized weights
    stocks_df['Optimized Portfolio'] = 0
    for ticker, weight in weights.items():
        stocks_df['Optimized Portfolio'] += stocks_df[ticker]*weight

    # Plot Cumulative Returns of Optimized Portfolio
    fig_cum_returns_optimized = plot_cum_returns(stocks_df['Optimized Portfolio'], 'Cumulative Returns of Optimized Portfolio Starting with $100')

    return  fig_cum_returns_optimized, weights_df, fig_efficient_frontier, fig_corr,   \
            expected_annual_return, annual_volatility, sharpe_ratio, fig_indiv_prices, fig_cum_returns


# Interface =============================================


    
with gr.Blocks() as demo:

    with gr.Tab("Introduction"):
        gr.HTML(TITLE_INTRO)
    
    with gr.Tab("Your Portfolio"):
        gr.HTML(TITLE_INTRO)
        
        run_button_crewai.click(
            fn=crewai_process, 
            inputs=research_topic_component, 
            outputs="text"
        )
       
        
    with gr.Tab("Portfolio Analysis"):
        with gr.Blocks() as app:
            with gr.Row():
                gr.HTML("<h1>Bohmian's Stock Portfolio Optimizer</h1>")
            
            with gr.Row():
                start_date = gr.Textbox("2013-01-01", label="Start Date")
                end_date = gr.Textbox(datetime.datetime.now().date(), label="End Date")
            
            with gr.Row():        
                tickers_string = gr.Textbox("MA,META,V,AMZN,JPM,BA", 
                                            label='Enter all stock tickers to be included in portfolio separated \
                                            by commas WITHOUT spaces, e.g. "MA,META,V,AMZN,JPM,BA"')
                btn = gr.Button("Get Optimized Portfolio")
               
            with gr.Row():
                gr.HTML("<h3>Optimizied Portfolio Metrics</h3>")
                
            with gr.Row():
                expected_annual_return = gr.Text(label="Expected Annual Return")
                annual_volatility = gr.Text(label="Annual Volatility")
                sharpe_ratio = gr.Text(label="Sharpe Ratio")            
           
            with gr.Row():        
                fig_cum_returns_optimized = gr.Plot(label="Cumulative Returns of Optimized Portfolio (Starting Price of $100)")
                weights_df = gr.DataFrame(label="Optimized Weights of Each Ticker")
                
            with gr.Row():
                fig_efficient_frontier = gr.Plot(label="Efficient Frontier")
                fig_corr = gr.Plot(label="Correlation between Stocks")
            
            with gr.Row():
                fig_indiv_prices = gr.Plot(label="Price of Individual Stocks")
                fig_cum_returns = gr.Plot(label="Cumulative Returns of Individual Stocks Starting with $100")
        
            btn.click(fn=output_results, inputs=[start_date, end_date, tickers_string], 
                      outputs=[fig_cum_returns_optimized, weights_df, fig_efficient_frontier, fig_corr,   \
                    expected_annual_return, annual_volatility, sharpe_ratio, fig_indiv_prices, fig_cum_returns])



    
    with gr.Tab("Company Analysis"):
        gr.HTML(TITLE1)
        run_button_analysis.click(
        fn=company_analysis,
        inputs=[
            gr.Textbox(label="Enter your OpenAI API Key:", type="password"),
            gr.Textbox(label="Enter the Company Name:")
        ],
        outputs=[
            gr.Textbox(label="Language"),
            gr.Textbox(label="Summary"),
            gr.Textbox(label="Translated Email"),
            gr.Textbox(label="Reply in English"),
            gr.Textbox(label="Reply in Original Language")
        ]
        )

      
    with gr.Tab("Investment Strategy"):    
        gr.HTML(TITLE2)
        gr.HTML(SUBTITLE)
        gr.HTML(GETKEY)
        with gr.Column():
            google_key_component.render()
            chatbot_component.render()
            with gr.Row():
                text_prompt_component.render()
                upload_button_component.render()
                run_button_component.render()
            with gr.Accordion("Parameters", open=False):
                temperature_component.render()
                max_output_tokens_component.render()
                stop_sequences_component.render()
                with gr.Accordion("Advanced", open=False):
                    top_k_component.render()
                    top_p_component.render()
    
        run_button_component.click(
            fn=user,
            inputs=user_inputs,
            outputs=[text_prompt_component, chatbot_component],
            queue=False
        ).then(
            fn=bot, inputs=bot_inputs, outputs=[chatbot_component],
        )
    
        text_prompt_component.submit(
            fn=user,
            inputs=user_inputs,
            outputs=[text_prompt_component, chatbot_component],
            queue=False
        ).then(
            fn=bot, inputs=bot_inputs, outputs=[chatbot_component],
        )
    
        upload_button_component.upload(
            fn=upload,
            inputs=[upload_button_component, chatbot_component],
            outputs=[chatbot_component],
            queue=False
        )

    with gr.Tab("Profit Prophet"):
        gr.HTML(TITLE3)

        with gr.Row():
            with gr.Column(scale=1):
                gr.Image(value = "resources/holder.png")
            with gr.Column(scale=1):
                gr.Image(value = "resources/holder.png")
 

        
demo.queue(max_size=99).launch(debug=False, show_error=True)