app.py

import os
import streamlit as st
import pdfplumber
import requests
import google.generativeai as genai
from bs4 import BeautifulSoup

# LangChain – updated imports
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_pinecone import PineconeVectorStore
from langchain_groq import ChatGroq
from langchain_classic.chains import create_retrieval_chain
from langchain_classic.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.embeddings import Embeddings

# Tools
from langchain_community.tools import DuckDuckGoSearchRun

from pinecone import Pinecone
from dotenv import load_dotenv
import numpy as np
import time
import random
from typing import List
import arxiv
import wikipedia

# Selenium
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.action_chains import ActionChains

from lxml import html
import base64


# Load environment variables
load_dotenv()

# Get API keys from environment variables
groq_key = os.getenv("GROQ_API_KEY")
pinecone_key = os.getenv("PINECONE_API_KEY")
gemini_key = os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY")
genai.configure(api_key=gemini_key)

# Check if all required API keys are available
if not gemini_key:
    st.error("Gemini API key is missing. Please set either GEMINI_API_KEY or GOOGLE_API_KEY environment variable.")

# Initialize theme in session state if it doesn't exist
if 'theme' not in st.session_state:
    st.session_state.theme = 'light'

# Page configuration with modern settings
st.set_page_config(
    page_title="AI Research Assistant",
    page_icon="🔍",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Apply theme based on session state
if st.session_state.theme == 'dark':
    # Dark theme settings
    theme_bg_color = "#0E1117"
    theme_secondary_bg_color = "#262730"
    theme_text_color = "#FAFAFA"
    theme_primary_color = "#FF4B4B"
else:
    # Light theme settings
    theme_bg_color = "#FFFFFF"
    theme_secondary_bg_color = "#F0F2F6"
    theme_text_color = "#31333F"
    theme_primary_color = "#FF4B4B"

# Custom CSS for modern UI with dynamic theme
st.markdown(f"""
<style>
    /* Main container styling */
    .main {{
        padding: 1.5rem;
        background-color: {theme_bg_color};
        color: {theme_text_color};
    }}
    
    /* Header styling */
    h1, h2, h3 {{
        color: {theme_text_color};
        font-weight: 600;
        margin-bottom: 1rem;
    }}
    
    /* Card-like containers */
    .stExpander, div[data-testid="stForm"] {{
        border-radius: 10px;
        border: 1px solid {theme_secondary_bg_color};
        padding: 1rem;
        box-shadow: 0 2px 5px rgba(0,0,0,0.05);
        margin-bottom: 1rem;
        background-color: {theme_secondary_bg_color};
    }}
    
    /* Button styling */
    button[kind="primaryFormSubmit"] {{
        border-radius: 8px;
        background-color: {theme_primary_color};
        transition: all 0.3s ease;
    }}
    button[kind="primaryFormSubmit"]:hover {{
        background-color: {theme_primary_color};
        opacity: 0.8;
        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
    }}
    
    /* Chat message styling */
    [data-testid="stChatMessage"] {{
        border-radius: 10px;
        margin-bottom: 0.5rem;
        padding: 0.5rem;
    }}
    
    /* Sidebar styling */
    [data-testid="stSidebar"] {{
        background-color: {theme_secondary_bg_color};
        border-right: 1px solid {theme_secondary_bg_color};
    }}
    
    /* Success/info/error message styling */
    [data-testid="stSuccessMessage"], [data-testid="stInfoMessage"], [data-testid="stErrorMessage"] {{
        border-radius: 8px;
    }}
    
    /* Input field styling */
    [data-testid="stTextInput"], [data-testid="stTextArea"] {{
        border-radius: 8px;
    }}
    
    /* File uploader styling */
    [data-testid="stFileUploader"] {{
        border-radius: 8px;
        border: 2px dashed {theme_secondary_bg_color};
        padding: 1rem;
    }}
    
    /* Tabs styling */
    .stTabs [data-baseweb="tab-list"] {{
        gap: 8px;
    }}
    .stTabs [data-baseweb="tab"] {{
        border-radius: 8px 8px 0 0;
        padding: 10px 16px;
        background-color: {theme_secondary_bg_color};
    }}
    .stTabs [aria-selected="true"] {{
        background-color: {theme_bg_color};
        border-bottom: 2px solid {theme_primary_color};
    }}
</style>
""", unsafe_allow_html=True)

#-------------------------------------------------------------
# UTILITY FUNCTIONS
#-------------------------------------------------------------

# Gemini Embeddings class
class GeminiEmbeddings(Embeddings):
    def __init__(self, api_key):
        genai.configure(api_key=api_key)
        self.model_name = "models/embedding-001"
    
    def embed_documents(self, texts):
        return [self._convert_to_float32(genai.embed_content(
            model=self.model_name, content=text, task_type="retrieval_document"
        )["embedding"]) for text in texts]
    
    def embed_query(self, text):
        response = genai.embed_content(
            model=self.model_name, content=text, task_type="retrieval_query"
        )
        return self._convert_to_float32(response["embedding"])
    
    @staticmethod
    def _convert_to_float32(embedding):
        return np.array(embedding, dtype=np.float32).tolist()

# PDF handling functions
def extract_text_from_pdf(pdf_path):
    text = ""
    try:
        with pdfplumber.open(pdf_path) as pdf:
            for page in pdf.pages:
                extracted_text = page.extract_text()
                if extracted_text:
                    text += extracted_text + "\n"
        return text.strip()
    except Exception as e:
        st.error(f"Error extracting text from PDF: {e}")
        return ""

def read_data_from_doc(uploaded_file):
    docs = []
    with pdfplumber.open(uploaded_file) as pdf:
        for i, page in enumerate(pdf.pages):
            text = page.extract_text() or ""
            tables = page.extract_tables()
            table_text = "\n".join([
                "\n".join(["\t".join(cell if cell is not None else "" for cell in row) for row in table])
                for table in tables if table
            ]) if tables else ""
            images = page.images
            image_text = f"[{len(images)} image(s) detected]" if images else ""
            content = f"{text}\n\n{table_text}\n\n{image_text}".strip()
            if content:
                docs.append(Document(page_content=content, metadata={"page": i + 1}))
    return docs

def make_chunks(docs, chunk_len=1000, chunk_overlap=200):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=chunk_len, chunk_overlap=chunk_overlap
    )
    chunks = text_splitter.split_documents(docs)
    return [Document(page_content=chunk.page_content, metadata=chunk.metadata) for chunk in chunks]

# Gemini model functions
def get_gemini_model(model_name="gemini-2.5-flash", temperature=0.4):
    return genai.GenerativeModel(model_name)

def get_generation_config(temperature=0.4):
    return {
        "temperature": temperature,
        "top_p": 1,
        "top_k": 1,
        "max_output_tokens": 2048,
    }

def get_safety_settings():
    return [
        {"category": category, "threshold": "BLOCK_NONE"}
        for category in [
            "HARM_CATEGORY_HARASSMENT",
            "HARM_CATEGORY_HATE_SPEECH",
            "HARM_CATEGORY_SEXUALLY_EXPLICIT",
            "HARM_CATEGORY_DANGEROUS_CONTENT",
        ]
    ]

def generate_gemini_response(model, prompt):
    response = model.generate_content(
        prompt,
        generation_config=get_generation_config(),
        safety_settings=get_safety_settings()
    )
    if response.candidates and len(response.candidates) > 0:
        return response.candidates[0].content.parts[0].text
    return ''

def summarize_text(text):
    model = get_gemini_model()
    prompt_text = f"Summarize the following research paper very concisely:\n{text[:5000]}"  # Truncate to 5000 chars
    summary = generate_gemini_response(model, prompt_text)
    return summary

#-------------------------------------------------------------
# RESEARCH ASSISTANT MODULE
#-------------------------------------------------------------

def download_pdf(pdf_url, save_path="temp_paper.pdf"):
    try:
        response = requests.get(pdf_url)
        if response.status_code == 200:
            with open(save_path, "wb") as file:
                file.write(response.content)
            return save_path
    except Exception as e:
        st.error(f"Error downloading PDF: {e}")
    return None

def search_arxiv(query, max_results=2):
    client = arxiv.Client()
    search = arxiv.Search(query=query, max_results=max_results, sort_by=arxiv.SortCriterion.Relevance)
    
    arxiv_docs = []
    
    for result in client.results(search):
        pdf_link = next((link.href for link in result.links if 'pdf' in link.href), None)
        
        # Download, extract, and summarize PDF if link exists
        if pdf_link:
            with st.spinner(f"Processing arXiv paper: {result.title}"):
                pdf_path = download_pdf(pdf_link)
                if pdf_path:
                    text = extract_text_from_pdf(pdf_path)
                    summary = summarize_text(text)
                    # Clean up downloaded file
                    if os.path.exists(pdf_path):
                        os.remove(pdf_path)
                else:
                    summary = "PDF could not be downloaded."
        else:
            summary = "No PDF available."

        content = f"""
        **Title:** {result.title}
        **Authors:** {', '.join(author.name for author in result.authors)}
        **Published:** {result.published.strftime('%Y-%m-%d')}
        **Abstract:** {result.summary}
        **PDF Summary:** {summary}
        **PDF Link:** {pdf_link if pdf_link else 'Not available'}
        """

        arxiv_docs.append(Document(page_content=content, metadata={"source": "arXiv", "title": result.title}))
    
    return arxiv_docs

def search_wikipedia(query, max_results=2):
    try:
        page_titles = wikipedia.search(query, results=max_results)
        wiki_docs = []
        for title in page_titles:
            try:
                with st.spinner(f"Processing Wikipedia article: {title}"):
                    page = wikipedia.page(title)
                    wiki_docs.append(Document(
                        page_content=page.content[:2000], 
                        metadata={"source": "Wikipedia", "title": title}
                    ))
            except (wikipedia.exceptions.DisambiguationError, wikipedia.exceptions.PageError) as e:
                st.warning(f"Error retrieving Wikipedia page {title}: {e}")
        return wiki_docs
    except Exception as e:
        st.error(f"Error searching Wikipedia: {e}")
        return []

class ResearchAssistant:
    def __init__(self):
        # Initialize LLM
        self.llm = ChatGroq(
            api_key=groq_key,
            # model="llama3-70b-8192",
            model = 'llama-3.3-70b-versatile',
            temperature=0.4
        )
        
        # Set up the prompt template
        self.prompt = ChatPromptTemplate.from_template("""
        You are an expert research assistant. Use the following context to answer the question. 
        If you don't know the answer, say so, but try your best to find relevant information 
        from the provided context and additional context.
        
        Context from user documents:
        {context}
        
        Additional context from research sources:
        {additional_context}
        
        Question: {input}
        
        Answer:
        """)
        
        # Set up the question-answer chain
        self.question_answer_chain = create_stuff_documents_chain(
            self.llm, self.prompt
        )

    def retrieve_documents(self, query):
        user_context = []
        
        # Get documents from arXiv and Wikipedia
        arxiv_docs = search_arxiv(query)
        wiki_docs = search_wikipedia(query)
        
        summarized_context = []
        for doc in arxiv_docs:
            summarized_context.append(f"**ArXiv - {doc.metadata.get('title', 'Unknown Title')}**:\n{doc.page_content}...")
            
        for doc in wiki_docs:
            summarized_context.append(f"**Wikipedia - {doc.metadata.get('title', 'Unknown Title')}**:\n{doc.page_content}...")
            
        return user_context, summarized_context
    
    def chat(self, question):
        user_context, summarized_context = self.retrieve_documents(question)
        
        input_data = {
            "input": question,
            "context": "\n\n".join(user_context),
            "additional_context": "\n\n".join(summarized_context)
        }
        
        with st.spinner("Generating answer..."):
            # Use the LLM directly
            prompt_text = f"""
            Question: {question}
            
            Additional context:
            {input_data['additional_context']}
            
            Please provide a comprehensive answer based on the above information.
            """
            response = self.llm.invoke(prompt_text)
            return response.content, summarized_context

#-------------------------------------------------------------
# DOCUMENT QA MODULE
#-------------------------------------------------------------

# Initialize retrieval chain
@st.cache_resource(show_spinner=False)
def get_retrieval_chain(uploaded_file, model):
    with st.spinner("Processing document... This may take a minute."):
        # Configure embeddings
        genai.configure(api_key=gemini_key)
        embeddings = GeminiEmbeddings(api_key=gemini_key)
        
        # Read and process document
        docs = read_data_from_doc(uploaded_file)
        splits = make_chunks(docs)
        
        # Set up vector store
        pc = Pinecone(api_key=pinecone_key)
        
        # Check if index exists, create it if not
        indexes = pc.list_indexes()
        index_name = "research-rag"
        if index_name not in [idx.name for idx in indexes]:
            pc.create_index(
                name=index_name,
                dimension=768,  # Dimension for embeddings
                metric="cosine"
            )
            
        vectorstore = PineconeVectorStore.from_documents(
            splits,
            embeddings,
            index_name=index_name,
        )
        retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 4})
        
        # Set up LLM and chain
        llm = ChatGroq(model_name=model, temperature=0.75, api_key=groq_key)
        
        system_prompt = """
        You are an AI assistant answering questions based on retrieved documents and additional context. 
        Use the provided context from both database retrieval and additional sources to answer the question. 

        - **Discard irrelevant context:** If one of the contexts (retrieved or additional) does not match the question, ignore it.
        - **Highlight conflicting information:** If multiple sources provide conflicting information, explicitly mention it by saying:
          - "According to the retrieved context, ... but as per internet sources, ..."
          - "According to the retrieved context, ... but as per internet sources, ..."
        - **Prioritize accuracy:** If neither context provides a relevant answer, say "I don't know" instead of guessing.

        Provide concise yet informative answers, ensuring clarity and completeness.

        Retrieved Context: {context}
        Additional Context: {additional_context}
        """
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", system_prompt),
            ("human", "{input}\n\nRetrieved Context: {context}\n\nAdditional Context: {additional_context}"),
        ])
        
        question_answer_chain = create_stuff_documents_chain(llm, prompt)
        chain = create_retrieval_chain(retriever, question_answer_chain)
        
        return chain

#-------------------------------------------------------------
# WEB SEARCH MODULE
#-------------------------------------------------------------

# Prompt creation functions
def create_search_prompt(query, context=""):
    system_prompt = """You are a smart assistant designed to determine whether a query needs data from a web search or can be answered using a document database. 
    Consider the provided context if available. 
    If the query requires external information, No context is provided, Irrelevent context is present or latest information is required, then output the special token <SEARCH> 
    followed by relevant keywords extracted from the query to optimize for search engine results. 
    Ensure the keywords are concise and relevant. If document data is sufficient, simply return blank."""
    
    if context:
        return f"{system_prompt}\n\nContext: {context}\n\nQuery: {query}"
    
    return f"{system_prompt}\n\nQuery: {query}"

def create_summary_prompt(content):
    return f"""Please provide a comprehensive yet concise summary of the following content, highlighting the most important points and maintaining factual accuracy. Organize the information in a clear and coherent manner:

Content to summarize:
{content}

Summary:"""

# Web scraping functions
def init_selenium_driver():
    chrome_options = Options()
    chrome_options.add_argument("--headless")
    chrome_options.add_argument("--disable-gpu")
    chrome_options.add_argument("--no-sandbox")
    chrome_options.add_argument("--disable-dev-shm-usage")
    
    driver = webdriver.Chrome(options=chrome_options)
    return driver

def extract_static_page(url):
    try:
        response = requests.get(url, timeout=5)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'lxml')
        
        text = soup.get_text(separator=" ", strip=True)
        return text[:5000]

    except requests.exceptions.RequestException as e:
        st.error(f"Error fetching page: {e}")
        return None
        
def extract_dynamic_page(url, driver):
    try:
        driver.get(url)
        time.sleep(random.uniform(2, 5))
        
        body = driver.find_element(By.TAG_NAME, "body")
        ActionChains(driver).move_to_element(body).perform()
        time.sleep(random.uniform(2, 5))
        
        page_source = driver.page_source
        tree = html.fromstring(page_source)
        
        text = tree.xpath('//body//text()')
        text_content = ' '.join(text).strip()
        return text_content[:1000]

    except Exception as e:
        st.error(f"Error fetching dynamic page: {e}")
        return None

def scrape_page(url):
    if "javascript" in url or "dynamic" in url:
        driver = init_selenium_driver()
        text = extract_dynamic_page(url, driver)
        driver.quit()
    else:
        text = extract_static_page(url)
    
    return text

def scrape_web(urls, max_urls=5):
    texts = []
    
    for url in urls[:max_urls]:
        text = scrape_page(url)
        
        if text:
            texts.append(text)
        else:
            st.warning(f"Failed to retrieve content from {url}")
            
    return texts

# Main web search functions
def check_search_needed(model, query, context):
    prompt = create_search_prompt(query, context)
    response = generate_gemini_response(model, prompt)
    
    if "<SEARCH>" in response:
        search_terms = response.split("<SEARCH>")[1].strip()
        return True, search_terms
    return False, None

def summarize_content(model, content):
    prompt = create_summary_prompt(content)
    return generate_gemini_response(model, prompt)

def process_query(query, context=''):
    with st.spinner("Processing query..."):
        model = get_gemini_model()
        search_tool = DuckDuckGoSearchRun()
        
        needs_search, search_terms = check_search_needed(model, query, context)
        
        result = {
            "original_query": query,
            "needs_search": needs_search,
            "search_terms": search_terms,
            "web_content": None,
            "summary": None
        }
        
        if needs_search:
            with st.spinner(f"Searching the web for: {search_terms}"):
                search_results = search_tool.run(search_terms)
                result["web_content"] = search_results
            
            with st.spinner("Summarizing search results..."):
                summary = summarize_content(model, search_results)
                result["summary"] = summary
        
    return result

#-------------------------------------------------------------
# MAIN APP
#-------------------------------------------------------------

# Helper function for creating animated loading indicators
def create_progress_bar(message="Processing..."):
    progress_container = st.empty()
    progress_bar = progress_container.progress(0)
    
    for i in range(100):
        time.sleep(0.01)
        progress_bar.progress(i + 1)
    
    progress_container.empty()

# Function to display search history
def display_search_history(history_key, input_key):
    if history_key in st.session_state and st.session_state[history_key]:
        with st.expander("📜 Search History", expanded=False):
            for i, query in enumerate(st.session_state[history_key]):
                col1, col2 = st.columns([4, 1])
                with col1:
                    st.write(f"**{i+1}.** {query}")
                with col2:
                    if st.button("Use", key=f"history_{i}", help="Use this query again"):
                        st.session_state[input_key] = query
                        st.experimental_rerun()
                st.divider()

# Main app display
def main():
    # Initialize session state for search history
    if "research_history_queries" not in st.session_state:
        st.session_state.research_history_queries = []
    
    if "web_search_history" not in st.session_state:
        st.session_state.web_search_history = []
    
    # Sidebar with modern styling
    with st.sidebar:
        # Logo and title
        st.image("https://img.icons8.com/fluency/96/000000/artificial-intelligence.png", width=80)
        st.title("AI Research Hub")
        st.markdown("---")
        
        # Navigation with icons
        st.subheader("📋 Navigation")
        app_mode = st.radio(
            "",
            [
                "🔬 Research Assistant",
                "📄 Document Q&A",
                "🌐 Web Search"
            ]
        )
        
        # Theme selector with working toggle
        st.markdown("---")
        st.subheader("🎨 Appearance")
        
        # Theme toggle button that actually works
        current_theme = st.session_state.theme
        theme_icon = "🌙" if current_theme == "light" else "☀️"
        theme_label = f"{theme_icon} Toggle {current_theme.capitalize()} Mode"
        
        if st.button(theme_label):
            # Toggle theme
            if st.session_state.theme == 'light':
                st.session_state.theme = 'dark'
                # Set dark theme
                st._config.set_option('theme.base', 'dark')
                st._config.set_option('theme.backgroundColor', '#0E1117')
                st._config.set_option('theme.secondaryBackgroundColor', '#262730')
                st._config.set_option('theme.textColor', '#FAFAFA')
            else:
                st.session_state.theme = 'light'
                # Set light theme
                st._config.set_option('theme.base', 'light')
                st._config.set_option('theme.backgroundColor', '#FFFFFF')
                st._config.set_option('theme.secondaryBackgroundColor', '#F0F2F6')
                st._config.set_option('theme.textColor', '#31333F')
            
            # Critical: Rerun the app to apply theme changes
            st.rerun()
        
        # API status with modern indicators
        st.markdown("---")
        st.subheader("🔌 API Status")
        
        api_col1, api_col2 = st.columns(2)
        
        with api_col1:
            st.markdown("**Groq API**")
            st.markdown("**Gemini API**")
            st.markdown("**Pinecone API**")
        
        with api_col2:
            if groq_key:
                st.markdown("✅ Connected")
            else:
                st.markdown("❌ Missing")
                
            if gemini_key:
                st.markdown("✅ Connected")
            else:
                st.markdown("❌ Missing")
                
            if pinecone_key:
                st.markdown("✅ Connected")
            else:
                st.markdown("❌ Missing")
        
        # About section
        st.markdown("---")
        st.subheader("ℹ️ About")
        st.markdown("""
        This AI Research Assistant helps you find and analyze information from various sources including arXiv papers, Wikipedia articles, your documents, and web search results.
        """)
        
        # Version info
        st.markdown("---")
        st.caption("Version 2.0 | Updated April 2025")
    
    # Main content area based on selected mode
    if "Research Assistant" in app_mode:
        display_research_assistant()
    elif "Document Q&A" in app_mode:
        display_document_qa()
    else:
        display_web_search()

def display_research_assistant():
    # Modern header with icon and description
    st.markdown(f"""
    <div style="
        display: flex; 
        align-items: center; 
        margin-bottom: 1rem;
        background-color: {theme_bg_color};
        padding: 1rem;
        border-radius: 10px;
    ">
        <img src="https://img.icons8.com/fluency/48/000000/microscope.png" 
             style="margin-right: 1rem;"/>
        <div>
            <h1 style="margin: 0; color: {theme_text_color};">Research Assistant</h1>
            <p style="margin: 0; color: {theme_text_color}; opacity: 0.7;">
                Get insights from arXiv papers and Wikipedia articles
            </p>
        </div>
    </div>
    """, unsafe_allow_html=True)
    
    # Initialize session state for chat history
    if "research_history" not in st.session_state:
        st.session_state.research_history = []
        
    # Initialize Research Assistant
    if "research_assistant" not in st.session_state:
        with st.spinner("Initializing Research Assistant..."):
            st.session_state.research_assistant = ResearchAssistant()
    
    # Search history display
    display_search_history("research_history_queries", "research_question")
    
    # Modern input area with shadow and rounded corners
    st.markdown("""
    <div style="background-color: white; padding: 1.5rem; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); margin-bottom: 2rem;">
        <h3 style="margin-top: 0; margin-bottom: 1rem;">Ask a Research Question</h3>
    </div>
    """, unsafe_allow_html=True)
    
    # Input form with modern styling
    with st.form(key="research_form", clear_on_submit=False):
        question = st.text_area(
            "Your research question:",
            key="research_question",
            height=100,
            placeholder="E.g., What are the latest developments in quantum computing?"
        )
        
        col1, col2 = st.columns([1, 4])
        with col1:
            submit_button = st.form_submit_button("🔍 Research")
        with col2:
            if st.form_submit_button("🗑️ Clear Chat"):
                st.session_state.research_history = []
                st.session_state.research_history_queries = []
                st.experimental_rerun()
    
    # Process query when submitted
    if submit_button and question:
        # Add to search history
        if question not in st.session_state.research_history_queries:
            st.session_state.research_history_queries.insert(0, question)
            if len(st.session_state.research_history_queries) > 10:
                st.session_state.research_history_queries.pop()
        
        # Add user query to chat history
        st.session_state.research_history.append({"role": "user", "content": question})
        
        # Get response from assistant
        answer, sources = st.session_state.research_assistant.chat(question)
        
        # Add assistant response to chat history
        st.session_state.research_history.append({
            "role": "assistant", 
            "content": answer,
            "sources": sources
        })
    
    # Display chat history with modern styling
    if st.session_state.research_history:
        st.markdown("### Conversation")
        
        for i, message in enumerate(st.session_state.research_history):
            if message["role"] == "user":
                with st.chat_message("user", avatar="👤"):
                    st.write(message['content'])
            else:
                with st.chat_message("assistant", avatar="🤖"):
                    st.markdown(message["content"])
                    
                    # Display sources in expandable section with modern styling
                    if message.get("sources"):
                        with st.expander("📚 View Sources"):
                            tabs = st.tabs([f"Source {i+1}" for i in range(len(message["sources"]))])
                            for i, (tab, source) in enumerate(zip(tabs, message["sources"])):
                                with tab:
                                    st.markdown(source)

def display_document_qa():
    # Modern header with icon and description
    st.markdown(f"""
    <div style="
        display: flex; 
        align-items: center; 
        margin-bottom: 1rem;
        background-color: {theme_bg_color};
        padding: 1rem;
        border-radius: 10px;
    ">
        <img src="https://img.icons8.com/fluency/48/000000/document.png" 
             style="margin-right: 1rem;"/>
        <div>
            <h1 style="margin: 0; color: {theme_text_color};">Document Q&A</h1>
            <p style="margin: 0; color: {theme_text_color}; opacity: 0.7;">
                Upload a PDF and ask questions about its content
            </p>
        </div>
    </div>
    """, unsafe_allow_html=True)
    
    # Initialize session state for conversation history
    if 'document_conversation' not in st.session_state:
        st.session_state.document_conversation = []
    
    # Document upload section with modern styling
    st.markdown("""
    <div style="background-color: white; padding: 1.5rem; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); margin-bottom: 2rem;">
        <h3 style="margin-top: 0; margin-bottom: 1rem;">Upload Document</h3>
    </div>
    """, unsafe_allow_html=True)
    
    # Two-column layout for model selection and file upload
    col1, col2 = st.columns([1, 2])
    
    with col1:
        # Model selection with modern dropdown
        st.markdown("#### Model Selection")
        model_name = st.selectbox(
            "Select AI Model",
            [
                "llama3-70b-8192",
                "gemma2-9b-it",
                "llama-3.3-70b-versatile",
                "llama-3.1-8b-instant",
                "llama-guard-3-8b",
                "mixtral-8x7b-32768",
                "deepseek-r1-distill-llama-70b",
                "llama-3.2-1b-preview"
            ],
            index=0
        )
    
    with col2:
        # File upload with drag-and-drop area
        st.markdown("#### Document Upload")
        uploaded_file = st.file_uploader(
            "Drag and drop your PDF here",
            type="pdf",
            help="Upload a PDF document to analyze"
        )
    
    # Document processing and Q&A
    if uploaded_file:
        try:
            # Process document
            with st.spinner("Processing your document..."):
                chain = get_retrieval_chain(
                    uploaded_file, 
                    model_name
                )
            
            # Show success message with document info
            st.success(f"✅ Document '{uploaded_file.name}' processed successfully!")
            
            # Display document info card
            st.markdown("""
            <div style="background-color: #f8f9fa; padding: 1rem; border-radius: 8px; border-left: 4px solid #1E3A8A; margin-bottom: 1rem;">
                <h4 style="margin-top: 0;">Document Ready for Questions</h4>
                <p>You can now ask questions about the content of your document.</p>
            </div>
            """, unsafe_allow_html=True)
            
            # Chat interface
            st.markdown("### Chat with your Document")
            
            # Display conversation history with modern chat bubbles
            for q, a in st.session_state.document_conversation:
                with st.chat_message("user", avatar="👤"):
                    st.write(q)
                with st.chat_message("assistant", avatar="🤖"):
                    st.write(a)
            
            # Question input with modern chat input
            question = st.chat_input("Ask a question about your document...")
            
            if question:
                with st.chat_message("user", avatar="👤"):
                    st.write(question)
                    
                with st.chat_message("assistant", avatar="🤖"):
                    with st.spinner("Analyzing document..."):
                        additional_context = ""  # Can be modified to add external context if needed
                        result = chain.invoke({
                            "input": question,
                            "additional_context": additional_context
                        })
                        answer = result['answer']
                        st.write(answer)
                
                # Store in conversation history
                st.session_state.document_conversation.append((question, answer))
                
        except Exception as e:
            st.error(f"An error occurred: {str(e)}")
            
    elif not (groq_key and gemini_key and pinecone_key):
        # API key warning with modern alert
        st.warning("⚠️ Please make sure all API keys are properly configured in your environment variables.")

def display_web_search():
    # Modern header with icon and description
    st.markdown(f"""
    <div style="
        display: flex; 
        align-items: center; 
        margin-bottom: 1rem;
        background-color: {theme_bg_color};
        padding: 1rem;
        border-radius: 10px;
    ">
        <img src="https://img.icons8.com/fluency/48/000000/internet.png" 
             style="margin-right: 1rem;"/>
        <div>
            <h1 style="margin: 0; color: {theme_text_color};">Web Search</h1>
            <p style="margin: 0; color: {theme_text_color}; opacity: 0.7;">
                Search the web for answers to your questions
            </p>
        </div>
    </div>
    """, unsafe_allow_html=True)
    
    # Search history display
    display_search_history("web_search_history", "web_query")
    
    # Modern input area with shadow and rounded corners
    st.markdown("""
    <div style="background-color: white; padding: 1.5rem; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); margin-bottom: 2rem;">
        <h3 style="margin-top: 0; margin-bottom: 1rem;">Web Research</h3>
    </div>
    """, unsafe_allow_html=True)
    
    # Input form with modern styling
    with st.form("web_query_form"):
        query = st.text_area(
            "Enter your research question",
            key="web_query",
            height=100, 
            placeholder="E.g., What are the latest developments in quantum computing?"
        )
        
        # Expandable advanced options
        with st.expander("Advanced Options", expanded=False):
            context = st.text_area(
                "Additional context (optional)",
                height=100, 
                placeholder="Add any additional context that might help with the research"
            )
        
        # Submit button with icon
        submit_col1, submit_col2 = st.columns([1, 4])
        with submit_col1:
            submit_button = st.form_submit_button("🔍 Research")
        with submit_col2:
            st.write("")  # Empty space for layout
    
    # Process query when submitted
    if submit_button and query:
        # Add to search history
        if query not in st.session_state.web_search_history:
            st.session_state.web_search_history.insert(0, query)
            if len(st.session_state.web_search_history) > 10:
                st.session_state.web_search_history.pop()
        
        # Process the query
        result = process_query(query, context)
        
        if result["needs_search"]:
            # Display results in a modern card layout
            st.markdown("""
            <div style="background-color: #f0f8ff; padding: 1rem; border-radius: 8px; border-left: 4px solid #4CAF50; margin-bottom: 1rem;">
                <h4 style="margin-top: 0; color: #4CAF50;">✅ Research Complete</h4>
                <p>Web search completed successfully. Results are shown below.</p>
            </div>
            """, unsafe_allow_html=True)
            
            # Results in tabs for better organization
            search_tab, summary_tab = st.tabs(["📊 Search Details", "📝 Summary"])
            
            with search_tab:
                st.subheader("Search Terms Used")
                st.info(result["search_terms"])
                
                st.subheader("Raw Web Content")
                st.text_area("Web Content", result["web_content"], height=200)
            
            with summary_tab:
                st.subheader("Summary of Findings")
                st.markdown(result["summary"])
        else:
            # No search needed message
            st.info("Based on the analysis, no web search was needed for this query.")

if __name__ == "__main__":
    main()