import streamlit as st
import os
import getpass
from langchain import PromptTemplate
from langchain import hub
from langchain.docstore.document import Document
from langchain.document_loaders import WebBaseLoader
from langchain.schema import StrOutputParser
from langchain.schema.prompt_template import format_document
from langchain.schema.runnable import RunnablePassthrough
import google.generativeai as genai
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.llm import LLMChain
from langchain.chains import StuffDocumentsChain
from langchain_core.messages import HumanMessage
import requests
st.set_page_config(layout="wide")

GOOGLE_API_KEY=os.environ['GOOGLE_API_KEY']

st.title('Stock Market Insights')
st.sidebar.image("https://myndroot.com/wp-content/uploads/2023/12/Gemini-Dext.jpg",width =100)
st.sidebar.markdown("The App uses **Google Gemini API** for Text and Vision along with 🦜️🔗 LangChain")
st.sidebar.info("Know more about [NSE Tickers](https://www.google.com/search?q=nse+tickers+list&sca_esv=a6c39f4d03c5324c&sca_upv=1&rlz=1C1GCEB_enIN1011IN1011&sxsrf=ADLYWILQPbew-0SrvUUWpI8Y29_uOOgbvA%3A1716470016765&ei=AEFPZp-zLvzHp84P_ZWtuA0&oq=NSE+Tickers+&gs_lp=Egxnd3Mtd2l6LXNlcnAiDE5TRSBUaWNrZXJzICoCCAAyBRAAGIAEMggQABgWGAoYHjIGEAAYFhgeMgYQABgWGB4yBhAAGBYYHjIGEAAYFhgeMgYQABgWGB4yBhAAGBYYHjILEAAYgAQYhgMYigUyCxAAGIAEGIYDGIoFSIIbUL0PWL0PcAF4AZABAJgB8QKgAfECqgEDMy0xuAEByAEA-AEBmAICoAKKA8ICChAAGLADGNYEGEeYAwCIBgGQBgiSBwUxLjMtMaAHtQU&sclient=gws-wiz-serp)")

st.sidebar.info("Know more about [Charts](https://chart-img.com/)")
ticker_user = st.text_input("Enter Ticker for NSE Stocks","")
gemini_embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")

llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro-latest",google_api_key = GOOGLE_API_KEY)
llm_vis = ChatGoogleGenerativeAI(model="gemini-pro-vision",google_api_key = GOOGLE_API_KEY)
if ticker_user!="":
    url1 = f"https://www.google.com/finance/quote/{ticker_user}:NSE?hl=en"
    url2 = f"https://in.tradingview.com/symbols/NSE-{ticker_user}/"
    # url3 = f"https://www.nseindia.com/get-quotes/equity?symbol={ticker_user}"

    loader = WebBaseLoader([url1,url2])
    docs = loader.load()
    
    
    st.divider()
    # llm_prompt_template = """You are an expert Stock Market Trader for stock market insights based on fundamental, analytical, profit based and company financials.
    # Based on the context below
    # {context}, Summarize the stock based on Historical data based on fundamental, price, news, sentiment , any red flags and suggest rating of the Stock in a 1 to 10 Scale"""
    
    llm_prompt_template = """You are an expert Stock Market Trader specializing in stock market insights derived from fundamental analysis, analytical trends, profit-based evaluations, and detailed company financials. Using your expertise, please analyze the stock based on the provided context below.
    
    Context:
    {context}
    
    Task:
    Summarize the stock based on its historical and current data.
    Evaluate the stock on the following parameters:
    1. Company Fundamentals: Assess the stock's intrinsic value, growth potential, and financial health.
    2. Current & Future Price Trends: Analyze historical price movements and current price trends.
    3. News and Sentiment: Review recent news articles, press releases, and social media sentiment.
    4. Red Flags: Identify any potential risks or warning signs.
    5. Provide a rating for the stock on a scale of 1 to 10.
    6. Advise if the stock is a good buy for the next 2 weeks.
    7. Suggest at what price we need to buy and hold or sell
    """
    
    st.sidebar.subheader('Prompt')
    # user_prompt = st.sidebar.text_area("Enter Prompt",llm_prompt_template)
    
    url = "https://api.chart-img.com/v2/tradingview/advanced-chart"
    api_key = "l0iUFRSeqC9z7nDPTd1hnafPh2RrdcEy6rl6tNqV"
    headers = {
        "x-api-key": api_key,
        "content-type": "application/json"
    }
    data = {
        "height": 400,
        "theme": "light",
        "interval": "1D",
        "session": "extended",
        "symbol": f"NSE:{ticker_user}"
    }
    
    response = requests.post(url, headers=headers, json=data)
    
    if response.status_code == 200:
        with open("chart_t1.jpg", "wb") as f:
            f.write(response.content)
            
        st.image("chart_t1.jpg", caption='')

        llm_prompt = PromptTemplate.from_template(llm_prompt_template)
    
        llm_chain = LLMChain(llm=llm,prompt=llm_prompt)
        stuff_chain = StuffDocumentsChain(llm_chain=llm_chain,document_variable_name="context")
        
        
        res = stuff_chain.invoke(docs)

        #create the humanmassage propmt templete with the image file 
        hmessage = HumanMessage(
            content=[
                {
                    "type": "text",
                    "text": "Based on the Image, suggest a BUY and SELL Strategy along with Risk based approach using Stop loss/Target price. Suggest different Candlestick \
                            pattern based strategies which could be usefull",
                },
                {"type": "image_url", "image_url": "chart_t1.jpg"},
            ]
        )
        message = llm_vis.invoke([hmessage])
        
        st.write(message.content)
        st.write(res["output_text"])
    
        # print("Image saved as chart-img-02.png")
    else:
        st.warning(f"Failed to retrieve image. Status code: {response.status_code}")
        st.warning("Response:", response.text)