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.env

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+GROQ_API_KEY=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

app.py

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+import faiss
+import numpy as np
+import yfinance as yf
+import os
+import streamlit as st
+from dotenv import load_dotenv
+from sentence_transformers import SentenceTransformer
+from groq import Groq
+
+# Load environment variables
+load_dotenv()
+groq_api_key = os.getenv("GROQ_API_KEY")
+if not groq_api_key:
+    st.error("GROQ_API_KEY is missing. Set it in Railway's environment variables.")
+    st.stop()
+
+client = Groq(api_key=groq_api_key)
+
+# Initialize session state for dark mode (default: True)
+if "dark_mode" not in st.session_state:
+    st.session_state.dark_mode = True
+
+# Icon switch
+icon = "🔆" if st.session_state.dark_mode else "🌙"
+
+# Custom CSS to remove container and position icon in the top-right corner
+st.markdown(
+    """
+    <style>
+    .stButton > button {
+        background: none !important;
+        border: none !important;
+        box-shadow: none !important;
+        font-size: 24px !important;
+        position: absolute !important;
+        top: 10px !important;
+        right: 10px !important;
+        cursor: pointer !important;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Toggle button (tap to switch modes)
+if st.button(icon, key="dark_mode_toggle"):
+    st.session_state.dark_mode = not st.session_state.dark_mode
+    st.rerun()
+
+# Apply styles for dark & light modes
+if st.session_state.dark_mode:
+    st.markdown(
+        """
+        <style>
+        body, .stApp { background-color: #0A192F; color: #E0E5EC; font-family: 'Segoe UI', sans-serif; }
+        h1, h2, h3, p, label { color: white !important; }
+        .stTextInput > div > div > input {
+            background-color: #112240; 
+            color: #E0E5EC; 
+            border-radius: 8px; 
+            padding: 10px; 
+            border: 2px solid transparent;
+            box-shadow: 0px 0px 10px rgba(255, 255, 255, 0.2);
+        }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+else:
+    st.markdown(
+        """
+        <style>
+        body, .stApp { background-color: #ffffff; color: #333; }
+        h1, h2, h3, p, label { color: #333 !important; }
+        .stTextInput > div > div > input {
+            background-color: #f8f9fa; 
+            color: #333; 
+            border-radius: 8px; 
+            padding: 10px; 
+            border: 1px solid #ccc;
+            box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1);
+        }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+
+# Hide Streamlit UI elements
+st.markdown(
+    """
+    <style>
+        #MainMenu {visibility: hidden;}
+        footer {visibility: hidden;}
+        header {visibility: hidden;}
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Load Sentence-Transformer Model (22MB)
+embedding_model = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L3-v2")
+
+def get_embeddings(text):
+    """Fetch embeddings using the local MiniLM model."""
+    return np.array(embedding_model.encode(text))
+
+def get_stock_info(symbol: str) -> dict:
+    """Retrieve stock information and description from Yahoo Finance."""
+    try:
+        data = yf.Ticker(symbol)
+        stock_info = data.info
+        description = stock_info.get("longBusinessSummary", "No description available.")
+        return {"symbol": symbol, "description": description}
+    except Exception as e:
+        return {"symbol": symbol, "description": f"Error retrieving stock info: {str(e)}"}
+
+# Determine embedding size dynamically
+test_embedding = get_embeddings("Test")
+d = test_embedding.shape[0]
+
+# Initialize FAISS index
+index = faiss.IndexFlatL2(d)
+stock_metadata = {}
+
+def store_stock_embeddings(stock_list):
+    """Store stock embeddings in FAISS index."""
+    global stock_metadata
+    vectors = []
+    metadata = []
+
+    for stock in stock_list:
+        description = stock["description"]
+        symbol = stock["symbol"]
+        embedding = get_embeddings(description)
+
+        if np.any(embedding):  
+            vectors.append(embedding)
+            metadata.append({"symbol": symbol, "description": description})
+
+    if vectors:
+        index.add(np.array(vectors))
+        for i, meta in enumerate(metadata):
+            stock_metadata[len(stock_metadata)] = meta
+
+def find_similar_stocks(query):
+    """Find similar stocks based on query embedding."""
+    if index.ntotal == 0:
+        return []
+
+    query_embedding = get_embeddings(query).reshape(1, -1)
+    D, I = index.search(query_embedding, k=10)
+    return [stock_metadata[idx] for idx in I[0] if idx in stock_metadata]
+
+def analyze_stocks(query, stocks):
+    """Generate stock analysis using Groq's Llama model."""
+    if not stocks:
+        return "No relevant stocks found."
+
+    context = "\n".join([f"Symbol: {s['symbol']}, Description: {s['description']}" for s in stocks])
+    prompt = f"""
+    You are a financial assistant. Analyze the following stocks based on the given query: {query}.
+
+    Stock data:
+    {context}
+
+    Provide insights based on performance, trends, and any notable aspects.
+    """
+
+    try:
+        response = client.chat.completions.create(
+            model="llama-3.2-11b-vision-preview",
+            messages=[{"role": "user", "content": prompt}]
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Error generating analysis: {str(e)}"
+
+# Load stock data at startup
+default_stocks = ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA"]
+stock_data = [get_stock_info(symbol) for symbol in default_stocks]
+store_stock_embeddings(stock_data)
+
+# Streamlit UI
+st.title('Stock Analysis Dashboard')
+
+with st.container():
+    query = st.text_input('Ask About Stocks:', '')
+
+    if st.button('Get Stock Info'):
+        stocks = find_similar_stocks(query)
+        analysis = analyze_stocks(query, stocks)
+
+        st.markdown("### Stock Insights:")
+        st.markdown(f"<div class='stMarkdown'>{analysis}</div>", unsafe_allow_html=True)
+
+        st.markdown("---")
+        if not stocks:
+            st.error("No relevant stocks found.")

requirements.txt

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+yfinance
+faiss-cpu
+groq
+python-dotenv
+numpy
+requests
+scikit-learn
+streamlit
+sentence-transformers
+asyncio