stream_app.py

# app.py
import os
import streamlit as st
from dotenv import load_dotenv
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from groq import Groq
import requests

# Load environment variables
load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
NASA_API_KEY = os.getenv("NASA_API_KEY")

# Page config
st.set_page_config(
    page_title="Space AI Assistant",
    page_icon="assets/spacegenai_logo.png",
    layout="wide"
)

# Custom CSS
st.markdown("""
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&display=swap" rel="stylesheet">

<style>
    /* Apply Inter globally */
    * {
        font-family: 'Inter', sans-serif;
    }
    
    /* Subtle animated gradient background */
    .stApp {
        background: linear-gradient(135deg, #0f0f23 0%, #1a1a2e 50%, #0f0f23 100%);
        background-size: 200% 200%;
        animation: subtleGradient 15s ease infinite;
        color: #e2e8f0;
    }
    
    @keyframes subtleGradient {
        0% { background-position: 0% 50%; }
        50% { background-position: 100% 50%; }
        100% { background-position: 0% 50%; }
    }
    
    .main {
        background: transparent;
        color: #e2e8f0;
    }
    
    /* Headers with subtle glow */
    h1 {
        font-family: 'Inter', sans-serif;
        color: #818cf8;
        font-weight: 700;
        font-size: 2.5rem;
        text-shadow: 0 0 20px rgba(129, 140, 248, 0.3);
        animation: softGlow 3s ease-in-out infinite alternate;
    }
    
    @keyframes softGlow {
        from { text-shadow: 0 0 15px rgba(129, 140, 248, 0.2); }
        to { text-shadow: 0 0 25px rgba(129, 140, 248, 0.4); }
    }
    
    h2 {
        font-family: 'Inter', sans-serif;
        color: #a5b4fc;
        font-weight: 600;
        font-size: 2rem;
        text-shadow: 0 0 10px rgba(165, 180, 252, 0.2);
    }
    
    h3 {
        font-family: 'Inter', sans-serif;
        color: #c7d2fe;
        font-weight: 600;
        font-size: 1.5rem;
    }
    
    /* Body text */
    p, div, span, label {
        font-family: 'Inter', sans-serif;
        font-weight: 400;
        font-size: 1rem;
        color: #e2e8f0;
    }
    
    /* Text input with subtle effects */
    .stTextInput>div>div>input {
        background: #1a1a2e;
        color: #e2e8f0;
        border: 1px solid #4c1d95;
        border-radius: 8px;
        padding: 10px;
        font-family: 'Inter', sans-serif;
        transition: all 0.3s ease;
    }
    
    .stTextInput>div>div>input:focus {
        border-color: #6366f1;
        box-shadow: 0 0 15px rgba(99, 102, 241, 0.3);
        background: #1f1f33;
    }
    
    /* Chat input */
    .stChatInputContainer {
        background: rgba(26, 26, 46, 0.8);
        border-top: 1px solid #4c1d95;
        backdrop-filter: blur(10px);
    }
    
    /* Buttons with gradient and hover */
    .stButton>button {
        background: linear-gradient(135deg, #4c1d95 0%, #5b21b6 100%);
        color: white;
        border: none;
        border-radius: 8px;
        padding: 10px 20px;
        font-family: 'Inter', sans-serif;
        font-weight: 600;
        transition: all 0.3s ease;
        box-shadow: 0 2px 10px rgba(76, 29, 149, 0.3);
    }
    
    .stButton>button:hover {
        background: linear-gradient(135deg, #6366f1 0%, #7c3aed 100%);
        transform: translateY(-2px);
        box-shadow: 0 4px 15px rgba(99, 102, 241, 0.4);
    }
    
    /* Chat messages with subtle backdrop */
    .stChatMessage {
        background: rgba(26, 26, 46, 0.6);
        border: 1px solid rgba(76, 29, 149, 0.5);
        border-radius: 10px;
        padding: 15px;
        font-family: 'Inter', sans-serif;
        backdrop-filter: blur(5px);
    }
    
    /* Sidebar with gradient */
    [data-testid="stSidebar"] {
        background: linear-gradient(180deg, #0f0f23 0%, #1a1a2e 100%);
        border-right: 1px solid #4c1d95;
    }
    
    /* Divider with subtle glow */
    hr {
        border: none;
        height: 1px;
        background: linear-gradient(90deg, transparent, #4c1d95, transparent);
        box-shadow: 0 0 5px rgba(76, 29, 149, 0.3);
        margin: 20px 0;
    }
    
    /* Spinner */
    .stSpinner > div {
        border-color: #4c1d95 !important;
        border-top-color: #6366f1 !important;
    }
    
    /* Links */
    a {
        color: #818cf8;
        text-decoration: none;
        transition: all 0.2s;
    }
    
    a:hover {
        color: #a5b4fc;
        text-shadow: 0 0 8px rgba(129, 140, 248, 0.4);
    }
    
    /* Images with subtle border glow */
    .stImage {
        border-radius: 10px;
        border: 1px solid #4c1d95;
        box-shadow: 0 2px 15px rgba(76, 29, 149, 0.2);
        transition: all 0.3s ease;
    }
    
    .stImage:hover {
        box-shadow: 0 4px 20px rgba(99, 102, 241, 0.3);
    }
    
    /* Columns */
    [data-testid="column"] {
        background: rgba(26, 26, 46, 0.3);
        border-radius: 10px;
        padding: 15px;
        backdrop-filter: blur(5px);
    }
    
    /* Alert boxes */
    .stAlert {
        background: rgba(76, 29, 149, 0.15);
        border: 1px solid #4c1d95;
        border-radius: 8px;
        backdrop-filter: blur(10px);
    }
    
    /* Code blocks */
    code {
        background: #1a1a2e;
        color: #818cf8;
        padding: 2px 6px;
        border-radius: 4px;
        border: 1px solid rgba(76, 29, 149, 0.5);
        font-family: 'Monaco', monospace;
    }
    
    /* Scrollbar */
    ::-webkit-scrollbar {
        width: 10px;
    }
    
    ::-webkit-scrollbar-track {
        background: #1a1a2e;
    }
    
    ::-webkit-scrollbar-thumb {
        background: #4c1d95;
        border-radius: 5px;
    }
    
    ::-webkit-scrollbar-thumb:hover {
        background: #6366f1;
    }
</style>
""", unsafe_allow_html=True)

# Initialize session state
if 'messages' not in st.session_state:
    st.session_state.messages = []

if 'vectorstore' not in st.session_state:
    with st.spinner("🔄 Loading AI models..."):
        embeddings = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-MiniLM-L6-v2"
        )
        st.session_state.vectorstore = FAISS.load_local(
            "embeddings/faiss_index",
            embeddings,
            allow_dangerous_deserialization=True
        )
        st.session_state.groq_client = Groq(api_key=GROQ_API_KEY)

# Functions
def get_nasa_apod():
    """Fetch today's Astronomy Picture of the Day"""
    try:
        url = f"https://api.nasa.gov/planetary/apod?api_key={NASA_API_KEY}"
        response = requests.get(url)
        data = response.json()
        return {
            'title': data.get('title'),
            'url': data.get('url'),
            'explanation': data.get('explanation'),
            'media_type': data.get('media_type', 'image')
        }
    except Exception as e:
        return None

def search_knowledge_base(query, k=3):
    """Search FAISS index for relevant information"""
    docs = st.session_state.vectorstore.similarity_search(query, k=k)
    context = "\n\n".join([doc.page_content for doc in docs])
    return context

def generate_response(user_question, context):
    """Generate response using Groq LLM"""
    system_prompt = """You are a helpful space and astronomy assistant. 
    Use the provided context to answer questions about space, NASA, astronomy, and related topics.
    Be conversational, enthusiastic, and educational. Keep responses concise but informative."""
    
    user_prompt = f"""Context from knowledge base:
{context}

User question: {user_question}

Please provide a helpful and engaging answer."""

    chat_completion = st.session_state.groq_client.chat.completions.create(
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}
        ],
        model="llama-3.3-70b-versatile",  # ← Change this line
        temperature=0.7,
        max_tokens=500
    )
    
    return chat_completion.choices[0].message.content

# App Layout

st.title("Space AI Assistant")
st.markdown("### Your personal guide to the cosmos powered by GenAI + NASA")

# Sidebar
with st.sidebar:
    st.header("🌌 Features")
    st.markdown("""
    - **Chat** about space, astronomy, NASA
    - **RAG-powered** answers from curated space data
    - **NASA APOD** - Astronomy Picture of the Day
    - **Powered by** Groq LLaMA 3.1
    """)
    
    st.divider()
    
    if st.button("📸 Show Today's APOD", use_container_width=True):
        with st.spinner("Fetching APOD..."):
            apod = get_nasa_apod()
            if apod:
                st.session_state.show_apod = apod
    
    st.divider()
    
    if st.button("🗑️ Clear Chat", use_container_width=True):
        st.session_state.messages = []
        st.rerun()

# Display APOD if requested
if 'show_apod' in st.session_state and st.session_state.show_apod:
    apod = st.session_state.show_apod
    st.subheader(f"📸 {apod['title']}")
    
    col1, col2 = st.columns([2, 1])
    
    with col1:
        if apod['media_type'] == 'image':
            st.image(apod['url'], use_container_width=True)
        else:
            st.video(apod['url'])
    
    with col2:
        st.markdown(apod['explanation'])
    
    st.divider()

# Chat interface
st.subheader("💬 Chat with the Space Assistant")

# Display chat messages
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Chat input
if prompt := st.chat_input("Ask me anything about space..."):
    # Add user message
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)
    
    # Generate response
    with st.chat_message("assistant"):
        with st.spinner("🤔 Thinking..."):
            # Search knowledge base
            context = search_knowledge_base(prompt)
            
            # Generate response
            response = generate_response(prompt, context)
            
            st.markdown(response)
    
    # Add assistant response to history
    st.session_state.messages.append({"role": "assistant", "content": response})

# Footer
st.divider()
st.markdown("""
<div style='text-align: center; color: #60a5fa;'>
    <p>🌟 Built with Streamlit • Powered by Groq LLaMA 3.1 • NASA API Integration</p>
    <p>GenAI Project with RAG (Retrieval-Augmented Generation)</p>
</div>
""", unsafe_allow_html=True)