app.py

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
from tradingview_ta import TA_Handler, Interval
import yfinance as yf
from datetime import datetime, timedelta
from newsapi import NewsApiClient
import json
import altair as alt

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-2.5-pro",google_api_key = GOOGLE_API_KEY)
#llm_vis = ChatGoogleGenerativeAI(model="gemini-pro-vision",google_api_key = GOOGLE_API_KEY)

def get_tradingview_analysis(symbol, exchange, screener, interval):
    try:
        stock = TA_Handler(
            symbol=symbol,
            screener=screener,
            exchange=exchange,
            interval=interval,
        )
        analysis_summary = stock.get_analysis()
        return analysis_summary
    except Exception as e:
        return {"error": str(e)}

        
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://in.tradingview.com/symbols/NSE-{ticker_user}/news/"
    url4 = f"https://in.tradingview.com/symbols/NSE-{ticker_user}/minds/"

    loader = WebBaseLoader([url1,url2,url3,url4])
    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:
    {input_documents}
    
    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 1,5, 10 weeks.
    7. Suggest at what price we need to buy and hold or sell the stock

    PROVIDE THE DETAILS based on just the FACTS present in the document
    PROVIDE THE DETAILS IN an JSON Object. Stick to the below JSON object
    {{
      "stock_summary": {{
        "company_name": "",
        "ticker": "",
        "exchange": "",
        "description": "",
        "current_price": "",
        "market_cap": "",
        "historical_performance": {{
          "5_day": "",
          "1_month": "",
          "6_months": "",
          "1_year": "",
          "5_years": ""
        }}
      }},
      "evaluation_parameters": {{
        "company_fundamentals": {{
          "assessment": "",
          "key_metrics": {{
            "pe_ratio": "",
            "volume":"",
            "revenue_growth_yoy": "",
            "net_income_growth_yoy": "",
            "eps_growth_yoy": "",
            "dividend_yield": "",
            "balance_sheet": "",
            "return_on_capital": ""
          }}
        }},
        "current_and_future_price_trends": {{
          "assessment": "",
          "historical_trends": "",
          "current_trends": "",
          "technical_analysis_notes": "",
          "technical_indicators":""
        }},
        "news_and_sentiment": {{
          "assessment": "",
          "positive_sentiment": [
            "",
            "",
            ""
          ],
          "negative_sentiment": [
            "",
            "",
            ""
          ]
        }},
        "red_flags": [
          {{
            "flag": "",
            "details": ""
          }},
          {{
            "flag": "",
            "details": ""
          }},
          {{
            "flag": "",
            "details": ""
          }}
        ]
      }},
      "overall_rating": {{
        "rating": "X/10",
        "justification": ""
      }},
      "investment_advice": {{
        "next_1_weeks_outlook": "",
        "next_5_weeks_outlook": "",
        "next_10_weeks_outlook": "",
        "price_action_suggestions": {{
          "buy": "",
          "hold": "",
          "sell": ""
        }}
      }}
    }}
    """
    
    # st.sidebar.subheader('Prompt')
    # user_prompt = st.sidebar.text_area("Enter Prompt",llm_prompt_template)
    #https://huggingface.co/spaces/pradeepodela/Stock-Analyser/blob/main/app.py
    interval = Interval.INTERVAL_1_DAY
    analysis_summary = get_tradingview_analysis(
            symbol=ticker_user,
            exchange="NSE",
            screener="india",
            interval=interval,
        )

    # st.title("Analysis Summary")
    # st.dataframe(analysis_summary.summary)
    # query = f"{ticker_user} stock"

    details = {
        "symbol": ticker_user,
        "exchange": "NSE",
        "screener": "india",
        "interval": interval,
    }
    # st.title("Details")
    # st.dataframe(details)

    # st.title("Oscillator Analysis")
    # st.dataframe(analysis_summary.oscillators)

    # st.title("Moving Averages")
    # st.dataframe(analysis_summary.moving_averages)

    # st.title("Summary")
    # st.dataframe(analysis_summary.summary)

    # st.title("Indicators")
    # st.dataframe(analysis_summary.indicators)

    # Page Title
    st.title(f"📊 TradingView Analysis: {ticker_user} ({details['exchange']})")
    
    # --- Row 1: Details + Summary ---
    col1, col2 = st.columns([1, 3])
    with col1:
        st.subheader("ℹ️ Details")
        st.table(details)  # Using st.table for a concise key-value look
    
    with col2:
        st.subheader("📝 Summary")
        # Create bar chart with Altair
        chart = alt.Chart(analysis_summary.summary).mark_bar().encode(
            x=alt.X('RECOMMENDATION', sort=['BUY', 'NEUTRAL', 'SELL']),  # order if needed
            y='Count',
            color='RECOMMENDATION'
        ).properties(
            width=400,
            height=300,
            title="Recommendation Counts"
        )
        
        st.altair_chart(chart, use_container_width=True)
        st.dataframe(analysis_summary.summary, use_container_width=True)
    
    # --- Row 2: Oscillators + Moving Averages ---
    # col3, col4 = st.columns(2)
    # with col3:
    #     st.subheader("⚙️ Oscillator Analysis")
    #     st.dataframe(analysis_summary.oscillators, use_container_width=True)
    
    # with col4:
    #     st.subheader("📈 Moving Averages")
    #     st.dataframe(analysis_summary.moving_averages, use_container_width=True)
    
    # # --- Row 3: Indicators ---
    # st.subheader("🔍 Indicators")
    # st.dataframe(analysis_summary.indicators, use_container_width=True)

    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="input_documents")
        
        # res = stuff_chain.invoke(docs)
        res = stuff_chain.invoke({"input_documents": 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. PROVIDE THE DETAILS based on just the FACTS present and PROVIDE THE DETAILS IN an JSON Object",
        #         },
        #         {"type": "image_url", "image_url": "chart_t1.jpg"},
        #     ]
        # )
        # message = llm_vis.invoke([hmessage])
        
        # st.write(message.content)
        # st.write(res["output_text"])
        data = json.loads(res["output_text"])
        # Header Info
        st.markdown(f"### {data['stock_summary']['company_name']} ({data['stock_summary']['ticker']}) | {data['stock_summary']['exchange']}")
        st.markdown(f"**Description**: {data['stock_summary']['description']}")
        
        # === Row 1: Price and Market Cap ===
        row1 = st.columns(3)
        row1[0].metric("💰 Current Price", data["stock_summary"]["current_price"])
        row1[1].metric("🏢 Market Cap", data["stock_summary"]["market_cap"])
        row1[2].metric("⭐ Rating", data["overall_rating"]["rating"])
        
        # === Row 2: Historical Performance ===
        st.subheader("📊 Historical Performance")
        perf_cols = st.columns(len(data["stock_summary"]["historical_performance"]))
        for i, (k, v) in enumerate(data["stock_summary"]["historical_performance"].items()):
            perf_cols[i].metric(k.replace("_", " ").title(), v)
        
        # === Row 3: Fundamentals ===
        st.subheader("📘 Company Fundamentals")
        row3 = st.columns(4)
        metrics = data["evaluation_parameters"]["company_fundamentals"]["key_metrics"]
        row3[0].metric("P/E Ratio", metrics["pe_ratio"])
        row3[1].metric("EPS YoY", metrics["eps_growth_yoy"])
        row3[2].metric("Revenue YoY", metrics["revenue_growth_yoy"])
        row3[3].metric("Dividend Yield", metrics["dividend_yield"])
        
        row3b = st.columns(4)
        row3b[0].metric("Net Income YoY", metrics["net_income_growth_yoy"])
        row3b[1].metric("Volume", metrics["volume"])
        row3b[2].metric("Return on Capital", metrics["return_on_capital"])
        row3b[3].metric("Balance Sheet", metrics["balance_sheet"])
        
        st.info(data["evaluation_parameters"]["company_fundamentals"]["assessment"])
        
        # === Row 4: Trends and Technicals ===
        st.subheader("📈 Trends & Technical Analysis")
        row4 = st.columns(3)
        row4[0].markdown(f"**Historical Trends:** {data['evaluation_parameters']['current_and_future_price_trends']['historical_trends']}")
        row4[1].markdown(f"**Current Trends:** {data['evaluation_parameters']['current_and_future_price_trends']['current_trends']}")
        row4[2].markdown(f"**Technical Indicators:** {data['evaluation_parameters']['current_and_future_price_trends']['technical_indicators']}")
        
        st.success(data["evaluation_parameters"]["current_and_future_price_trends"]["assessment"])
        st.caption(f"📝 Notes: {data['evaluation_parameters']['current_and_future_price_trends']['technical_analysis_notes']}")
        
        # === Row 5: Sentiment ===
        st.subheader("📰 News & Sentiment")
        sentiment_cols = st.columns(2)
        with sentiment_cols[0]:
            st.success("👍 Positive Sentiment")
            for s in data["evaluation_parameters"]["news_and_sentiment"]["positive_sentiment"]:
                st.write(f"✅ {s}")
        with sentiment_cols[1]:
            st.error("👎 Negative Sentiment")
            for s in data["evaluation_parameters"]["news_and_sentiment"]["negative_sentiment"]:
                st.write(f"❌ {s}")
        st.info(data["evaluation_parameters"]["news_and_sentiment"]["assessment"])
        
        # === Row 6: Red Flags ===
        st.subheader("🚩 Red Flags")
        red_flag_cols = st.columns(3)
        for i, flag in enumerate(data["evaluation_parameters"]["red_flags"]):
            red_flag_cols[i].warning(f"**{flag['flag']}**\n{flag['details']}")
        
        # === Row 7: Investment Advice ===
        st.subheader("💡 Investment Advice")
        advice_cols = st.columns(3)
        advice = data["investment_advice"]
        advice_cols[0].markdown(f"**Next 1 Week**\n{advice['next_1_weeks_outlook']}")
        advice_cols[1].markdown(f"**Next 5 Weeks**\n{advice['next_5_weeks_outlook']}")
        advice_cols[2].markdown(f"**Next 10 Weeks**\n{advice['next_10_weeks_outlook']}")
        
        action_cols = st.columns(3)
        action_cols[0].success(f"**Buy:** {advice['price_action_suggestions']['buy']}")
        action_cols[1].info(f"**Hold:** {advice['price_action_suggestions']['hold']}")
        action_cols[2].error(f"**Sell:** {advice['price_action_suggestions']['sell']}")
        
        # === Footer ===
        st.markdown("---")
        st.caption("Generated by AI-powered financial analysis dashboard.")
    else:
        st.warning(f"Failed to retrieve image. Status code: {response.status_code}")
        st.warning("Response:", response.text)