import streamlit as st
from langchain_community.document_loaders import WebBaseLoader
from openai import OpenAI
from sentence_transformers import SentenceTransformer

# Initialize session state for OpenAI summary
if 'openai_summary' not in st.session_state:
    st.session_state.openai_summary = None
if 'show_summary' not in st.session_state:
    st.session_state.show_summary = False

def toggle_summary():
    st.session_state.show_summary = not st.session_state.show_summary

# Set page configuration
st.set_page_config(
    page_title="🦜 LangChain Document Explorer",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for better styling
st.markdown("""
    <style>
    .main {
        padding: 2rem;
    }
    .stButton>button {
        width: 100%;
        margin-top: 1rem;
    }
    .css-1d391kg {
        padding: 1rem;
    }
    </style>
""", unsafe_allow_html=True)

# Main title with emoji
st.title("🦜 Webscrapping and Summarizing using OpenAI")
st.markdown("""
    Explore web content with AI-powered analysis and processing.
    Upload a URL to get started!
""")

# Sidebar configuration
with st.sidebar:
    st.header("⚙️ Configuration")
    openai_api_key = st.text_input("OpenAI API Key:", type="password")
    
    st.markdown("---")
    st.markdown("""
    ### 📖 Quick Guide
    1. Enter your OpenAI API key
    2. Input a webpage URL
    3. Explore different analyses in the tabs
    """)
    
    st.markdown("---")
    st.markdown("Made with ❤️ using LangChain 0.3 & Streamlit 1.41.0")

# Main content area
url = st.text_input("🔗 Enter webpage URL:", "https://python.langchain.com/docs/")

# Document loading
docs = None
if url:
    try:
        with st.spinner("Loading webpage..."):
            loader = WebBaseLoader(web_paths=[url])
            docs = loader.load()
        st.success("✅ Webpage loaded successfully!")
    except Exception as e:
        st.error(f"❌ Error loading webpage: {str(e)}")

# Process and display content in tabs
if docs:
    tabs = st.tabs(["📄 Original Content", "🤖 AI Analysis", "📊 Embeddings"])
    
    # Original Content Tab
    with tabs[0]:
        full_text = " ".join([doc.page_content for doc in docs])
        st.markdown("### Original Web Content")
        st.markdown(full_text)
    
    # AI Analysis Tab
    with tabs[1]:
        if openai_api_key:
            st.markdown("### AI Content Analysis")
            
            if st.button("Generate AI Summary", key="generate_summary"):
                try:
                    with st.spinner("Generating AI summary..."):
                        client = OpenAI(api_key=openai_api_key)
                        response = client.chat.completions.create(
                            model="gpt-3.5-turbo",
                            messages=[
                                {"role": "system", "content": "Create a detailed writeup with key points and insights from the following text.  Be grounded in the given text"},
                                {"role": "user", "content": full_text}
                            ],
                            max_tokens=500
                        )
                        st.session_state.openai_summary = response.choices[0].message.content
                
                except Exception as e:
                    st.error(f"❌ Error generating summary: {str(e)}")
            
            # Display OpenAI summary if available
            if st.session_state.openai_summary:
                st.markdown("#### 📝 AI-Generated Summary")
                st.markdown(st.session_state.openai_summary)
        else:
            st.warning("⚠️ Please enter your OpenAI API key in the sidebar to use AI analysis.")
    
    # Embeddings Tab
    with tabs[2]:
        st.markdown("### Document Embeddings")
        try:
            with st.spinner("Generating embeddings..."):
                model = SentenceTransformer('all-MiniLM-L6-v2')
                embeddings = model.encode(full_text)
                
                st.markdown(f"**Embeddings Shape**: {embeddings.shape}")
                st.markdown("#### Embedding Vector Preview")
                st.write(embeddings[:10])  # Show first 10 dimensions
                
                # Visualize embedding statistics
                import numpy as np
                st.markdown("#### Embedding Statistics")
                col1, col2, col3 = st.columns(3)
                with col1:
                    st.metric("Mean", f"{np.mean(embeddings):.4f}")
                with col2:
                    st.metric("Std Dev", f"{np.std(embeddings):.4f}")
                with col3:
                    st.metric("Dimensions", embeddings.shape[0])
                
        except Exception as e:
            st.error(f"❌ Error generating embeddings: {str(e)}")
else:
    st.info("👆 Please enter a URL above to get started!")