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app.py

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+# Renaming main.py to app.py for Hugging Face compatibility
+import streamlit as st
+import plotly.graph_objects as go
+import yfinance as yf
+import pandas as pd
+from datetime import datetime, timedelta
+from data_fetcher import get_stock_data, get_company_info
+from visualization import create_stock_chart, create_metrics_table
+import styles
+
+def main():
+    # Apply custom styles
+    styles.apply_custom_styles()
+
+    st.title("📈 Stock Analysis Terminal")
+
+    # Stock Symbol Input
+    symbol = st.text_input("Enter Stock Symbol (e.g., AAPL)", "").upper()
+
+    if symbol:
+        try:
+            # Fetch stock data
+            stock_data = get_stock_data(symbol)
+            company_info = get_company_info(symbol)
+
+            # Display company info
+            col1, col2 = st.columns(2)
+            with col1:
+                st.subheader(company_info.get('longName', symbol))
+                st.write(f"Sector: {company_info.get('sector', 'N/A')}")
+            with col2:
+                st.metric(
+                    "Current Price", 
+                    f"${stock_data['Close'].iloc[-1]:.2f}",
+                    f"{((stock_data['Close'].iloc[-1] / stock_data['Close'].iloc[-2]) - 1) * 100:.2f}%"
+                )
+
+            # Stock Chart
+            st.plotly_chart(create_stock_chart(stock_data, symbol), use_container_width=True)
+
+            # Financial Metrics Table
+            metrics_df = create_metrics_table(company_info)
+            st.dataframe(metrics_df, use_container_width=True)
+
+            # Download button for CSV
+            csv = stock_data.to_csv(index=True)
+            st.download_button(
+                label="Download Data as CSV",
+                data=csv,
+                file_name=f"{symbol}_stock_data.csv",
+                mime="text/csv"
+            )
+
+        except Exception as e:
+            st.error(f"Error retrieving data for {symbol}: {str(e)}")
+
+if __name__ == "__main__":
+    main()

auth.py

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+import streamlit as st
+import firebase_admin
+from firebase_admin import credentials, auth
+from firebase_admin import initialize_app
+
+def initialize_firebase():
+    if not firebase_admin._apps:
+        cred = credentials.Certificate(st.secrets["firebase"])
+        initialize_app(cred)
+
+def check_authentication():
+    initialize_firebase()
+    
+    if 'user_authenticated' not in st.session_state:
+        st.session_state.user_authenticated = False
+    
+    if not st.session_state.user_authenticated:
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            email = st.text_input("Email")
+        with col2:
+            password = st.text_input("Password", type="password")
+            
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            if st.button("Login"):
+                try:
+                    user = auth.get_user_by_email(email)
+                    st.session_state.user_authenticated = True
+                    st.experimental_rerun()
+                except:
+                    st.error("Invalid credentials")
+                    
+        with col2:
+            if st.button("Sign Up"):
+                try:
+                    user = auth.create_user(
+                        email=email,
+                        password=password
+                    )
+                    st.success("Account created successfully!")
+                except:
+                    st.error("Could not create account")
+                    
+        return False
+        
+    return True

data_fetcher.py

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+import streamlit as st
+import yfinance as yf
+import pandas as pd
+from datetime import datetime, timedelta
+
+@st.cache_data(ttl=3600)
+def get_stock_data(symbol):
+    """Fetch stock data from Yahoo Finance"""
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=365)
+
+    stock = yf.Ticker(symbol)
+    df = stock.history(start=start_date, end=end_date)
+
+    return df
+
+@st.cache_data(ttl=3600)
+def get_company_info(symbol):
+    """Fetch company information"""
+    stock = yf.Ticker(symbol)
+    return stock.info

integrations.py

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+import streamlit as st
+from google.oauth2.credentials import Credentials
+from google_auth_oauthlib.flow import InstalledAppFlow
+from googleapiclient.discovery import build
+from datetime import datetime, timedelta
+
+def add_to_google_calendar(symbol, company_name):
+    """Add analysis review event to Google Calendar"""
+    creds = Credentials.from_authorized_user_info(st.secrets["google"])
+    service = build('calendar', 'v3', credentials=creds)
+
+    event = {
+        'summary': f'Stock Analysis Review - {symbol}',
+        'description': f'Review financial analysis for {company_name}',
+        'start': {
+            'dateTime': (datetime.now() + timedelta(days=1)).isoformat(),
+            'timeZone': 'UTC',
+        },
+        'end': {
+            'dateTime': (datetime.now() + timedelta(days=1, hours=1)).isoformat(),
+            'timeZone': 'UTC',
+        },
+    }
+
+    service.events().insert(calendarId='primary', body=event).execute()
+
+def update_google_sheet(stock_data, symbol):
+    """Export stock data to Google Sheets"""
+    creds = Credentials.from_authorized_user_info(st.secrets["google"])
+    service = build('sheets', 'v4', credentials=creds)
+
+    spreadsheet_id = st.secrets["google_sheet_id"]
+    range_name = f'{symbol}!A1'
+
+    values = [stock_data.columns.tolist()] + stock_data.reset_index().values.tolist()
+    body = {'values': values}
+
+    service.spreadsheets().values().update(
+        spreadsheetId=spreadsheet_id,
+        range=range_name,
+        valueInputOption='RAW',
+        body=body
+    ).execute()

main.py

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+import streamlit as st
+import plotly.graph_objects as go
+import yfinance as yf
+import pandas as pd
+from datetime import datetime, timedelta
+from data_fetcher import get_stock_data, get_company_info
+from visualization import create_stock_chart, create_metrics_table
+import styles
+
+def main():
+    # Apply custom styles
+    styles.apply_custom_styles()
+
+    st.title("📈 Stock Analysis Terminal")
+
+    # Stock Symbol Input
+    symbol = st.text_input("Enter Stock Symbol (e.g., AAPL)", "").upper()
+
+    if symbol:
+        try:
+            # Fetch stock data
+            stock_data = get_stock_data(symbol)
+            company_info = get_company_info(symbol)
+
+            # Display company info
+            col1, col2 = st.columns(2)
+            with col1:
+                st.subheader(company_info.get('longName', symbol))
+                st.write(f"Sector: {company_info.get('sector', 'N/A')}")
+            with col2:
+                st.metric(
+                    "Current Price", 
+                    f"${stock_data['Close'].iloc[-1]:.2f}",
+                    f"{((stock_data['Close'].iloc[-1] / stock_data['Close'].iloc[-2]) - 1) * 100:.2f}%"
+                )
+
+            # Stock Chart
+            st.plotly_chart(create_stock_chart(stock_data, symbol), use_container_width=True)
+
+            # Financial Metrics Table
+            metrics_df = create_metrics_table(company_info)
+            st.dataframe(metrics_df, use_container_width=True)
+
+            # Download button for CSV
+            csv = stock_data.to_csv(index=True)
+            st.download_button(
+                label="Download Data as CSV",
+                data=csv,
+                file_name=f"{symbol}_stock_data.csv",
+                mime="text/csv"
+            )
+
+        except Exception as e:
+            st.error(f"Error retrieving data for {symbol}: {str(e)}")
+
+if __name__ == "__main__":
+    main()

pyproject.toml

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+[project]
+name = "repl-nix-workspace"
+version = "0.1.0"
+description = "Add your description here"
+requires-python = ">=3.11"
+dependencies = [
+    "firebase-admin>=6.6.0",
+    "google-api-python-client>=2.159.0",
+    "google-auth>=2.37.0",
+    "google-auth-oauthlib>=1.2.1",
+    "huggingface-hub>=0.27.1",
+    "pandas>=2.2.3",
+    "plotly>=5.24.1",
+    "streamlit>=1.41.1",
+    "telegram>=0.0.1",
+    "yfinance>=0.2.52",
+]

requirements.txt

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+streamlit
+yfinance
+pandas
+plotly
+firebase-admin
+google-api-python-client
+google-auth-oauthlib
+huggingface-hub
+firebase-admin
+google-api-python-client
+google-auth
+google-auth-oauthlib
+huggingface-hub
+pandas
+plotly
+streamlit
+yfinance

styles.py

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+import streamlit as st
+
+def apply_custom_styles():
+    """Apply custom CSS styles to the application"""
+    st.markdown("""
+        <style>
+        .stApp {
+            max-width: 1200px;
+            margin: 0 auto;
+        }
+        
+        .stTextInput > div > div > input {
+            background-color: #1E1E1E;
+            color: #FFFFFF;
+            border: 1px solid #4A4A4A;
+        }
+        
+        .stDataFrame {
+            background-color: #1E1E1E;
+        }
+        
+        .stButton > button {
+            background-color: #FF4B4B;
+            color: white;
+            border: none;
+            border-radius: 4px;
+            padding: 0.5rem 1rem;
+        }
+        
+        .stButton > button:hover {
+            background-color: #FF6B6B;
+        }
+        
+        h1, h2, h3 {
+            color: #FFFFFF;
+        }
+        
+        .metric-card {
+            background-color: #262730;
+            padding: 1rem;
+            border-radius: 8px;
+            margin: 0.5rem 0;
+        }
+        </style>
+    """, unsafe_allow_html=True)

visualization.py

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+import plotly.graph_objects as go
+import pandas as pd
+
+def create_stock_chart(stock_data, symbol):
+    """Create an interactive stock price chart"""
+    fig = go.Figure()
+    
+    fig.add_trace(
+        go.Candlestick(
+            x=stock_data.index,
+            open=stock_data['Open'],
+            high=stock_data['High'],
+            low=stock_data['Low'],
+            close=stock_data['Close'],
+            name=symbol
+        )
+    )
+    
+    fig.update_layout(
+        title=f"{symbol} Stock Price",
+        yaxis_title="Price (USD)",
+        template="plotly_dark",
+        xaxis_rangeslider_visible=False
+    )
+    
+    return fig
+
+def create_metrics_table(company_info):
+    """Create a table of key financial metrics"""
+    metrics = {
+        'Metric': [
+            'Market Cap', 'P/E Ratio', 'Forward P/E',
+            'PEG Ratio', 'Dividend Yield', 'Beta'
+        ],
+        'Value': [
+            company_info.get('marketCap', 'N/A'),
+            company_info.get('trailingPE', 'N/A'),
+            company_info.get('forwardPE', 'N/A'),
+            company_info.get('pegRatio', 'N/A'),
+            company_info.get('dividendYield', 'N/A'),
+            company_info.get('beta', 'N/A')
+        ]
+    }
+    
+    return pd.DataFrame(metrics)