app.py

import os
import json
import re
import gradio as gr
import pandas as pd
import requests
import random
import feedparser
import urllib.parse
from tempfile import NamedTemporaryFile
from typing import List
from bs4 import BeautifulSoup
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader, PDFMinerLoader
from langchain_core.output_parsers import StrOutputParser
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.llms import HuggingFaceHub
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_core.documents import Document
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from openpyxl import load_workbook
from openpyxl.utils.dataframe import dataframe_to_rows
import camelot

huggingface_token = os.environ.get("HUGGINGFACE_TOKEN")

# Memory database to store question-answer pairs
memory_database = {}
conversation_history = []
news_database = []

def load_and_split_document_basic(file):
    """Loads and splits the document into pages."""
    loader = PyPDFLoader(file.name)
    data = loader.load_and_split()
    return data

def load_and_split_document_recursive(file: NamedTemporaryFile) -> List[Document]:
    """Loads and splits the document into chunks."""
    loader = PyPDFLoader(file.name)
    pages = loader.load()
    
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len,
    )
    
    chunks = text_splitter.split_documents(pages)
    return chunks

def load_and_split_document_basic(file: NamedTemporaryFile, parser: str) -> List[Document]:
    """Loads and splits the document into pages."""
    if parser == "PyPDF":
        loader = PyPDFLoader(file.name)
    elif parser == "PDFMiner":
        loader = PDFMinerLoader(file.name)
    elif parser == "Camelot":
        return load_and_split_document_camelot(file)
    else:
        raise ValueError(f"Unknown parser: {parser}")
    
    return loader.load_and_split()

def load_and_split_document_recursive(file: NamedTemporaryFile, parser: str) -> List[Document]:
    """Loads and splits the document into chunks using recursive character text splitter."""
    if parser == "PyPDF":
        loader = PyPDFLoader(file.name)
    elif parser == "PDFMiner":
        loader = PDFMinerLoader(file.name)
    elif parser == "Camelot":
        return load_and_split_document_camelot(file)
    else:
        raise ValueError(f"Unknown parser: {parser}")
    
    pages = loader.load()
    
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len,
    )
    
    chunks = text_splitter.split_documents(pages)
    return chunks

def load_and_split_document_camelot(file: NamedTemporaryFile) -> List[Document]:
    """Loads and splits the document using Camelot for tables and charts."""
    tables = camelot.read_pdf(file.name, pages='all')
    documents = []
    
    for i, table in enumerate(tables):
        df = table.df
        content = df.to_string(index=False)
        documents.append(Document(page_content=content, metadata={"source": file.name, "table_number": i+1}))
    
    return documents

def load_document(file: NamedTemporaryFile, parser: str, use_recursive_splitter: bool) -> List[Document]:
    """Loads the document using the specified parser and splitting method."""
    if parser == "Camelot":
        return load_and_split_document_camelot(file)
    elif use_recursive_splitter:
        return load_and_split_document_recursive(file, parser)
    else:
        return load_and_split_document_basic(file, parser)

def update_vectors(files, use_recursive_splitter, selected_parser):
    if not files:
        return "Please upload at least one PDF file."
    
    embed = get_embeddings()
    total_chunks = 0
    
    all_data = []
    for file in files:
        data = load_document(file, selected_parser, use_recursive_splitter)
        all_data.extend(data)
        total_chunks += len(data)
    
    if os.path.exists("faiss_database"):
        database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
        database.add_documents(all_data)
    else:
        database = FAISS.from_documents(all_data, embed)
    
    database.save_local("faiss_database")
    
    splitting_method = "recursive splitting" if use_recursive_splitter else "page-by-page splitting"
    return f"Vector store updated successfully. Processed {total_chunks} chunks from {len(files)} files using {selected_parser} parser with {splitting_method}."

def get_embeddings():
    return HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

def create_or_update_database(data, embeddings):
    if os.path.exists("faiss_database"):
        db = FAISS.load_local("faiss_database", embeddings, allow_dangerous_deserialization=True)
        db.add_documents(data)
    else:
        db = FAISS.from_documents(data, embeddings)
    db.save_local("faiss_database")

def clear_cache():
    if os.path.exists("faiss_database"):
        os.remove("faiss_database")
        return "Cache cleared successfully."
    else:
        return "No cache to clear."

def get_similarity(text1, text2):
    vectorizer = TfidfVectorizer().fit_transform([text1, text2])
    return cosine_similarity(vectorizer[0:1], vectorizer[1:2])[0][0]

prompt = """
Answer the question based on the following information:

Conversation History:
{history}

Context from documents:
{context}

Current Question: {question}

If the question is referring to the conversation history, use that information to answer.
If the question is not related to the conversation history, use the context from documents to answer.
If you don't have enough information to answer, say so.

Provide a concise and direct answer to the question:
"""

def get_model(temperature, top_p, repetition_penalty):
    return HuggingFaceHub(
        repo_id="mistralai/Mistral-7B-Instruct-v0.3",
        model_kwargs={
            "temperature": temperature,
            "top_p": top_p,
            "repetition_penalty": repetition_penalty,
            "max_length": 1000
        },
        huggingfacehub_api_token=huggingface_token
    )

def generate_chunked_response(model, prompt, max_tokens=1000, max_chunks=5):
    full_response = ""
    for i in range(max_chunks):
        chunk = model(prompt + full_response, max_new_tokens=max_tokens)
        chunk = chunk.strip()
        if chunk.endswith((".", "!", "?")):
            full_response += chunk
            break
        full_response += chunk
    return full_response.strip()

def manage_conversation_history(question, answer, history, max_history=5):
    history.append({"question": question, "answer": answer})
    if len(history) > max_history:
        history.pop(0)
    return history

def is_related_to_history(question, history, threshold=0.3):
    if not history:
        return False
    history_text = " ".join([f"{h['question']} {h['answer']}" for h in history])
    similarity = get_similarity(question, history_text)
    return similarity > threshold

def extract_text_from_webpage(html):
    soup = BeautifulSoup(html, 'html.parser')
    for script in soup(["script", "style"]):
        script.extract()  # Remove scripts and styles
    text = soup.get_text()
    lines = (line.strip() for line in text.splitlines())
    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
    text = '\n'.join(chunk for chunk in chunks if chunk)
    return text

_useragent_list = [
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Edge/91.0.864.59 Safari/537.36",
    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Edge/91.0.864.59 Safari/537.36",
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Safari/537.36",
    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Safari/537.36",
]

def google_search(term, num_results=5, lang="en", timeout=5, safe="active", ssl_verify=None):
    escaped_term = urllib.parse.quote_plus(term)
    start = 0
    all_results = []
    max_chars_per_page = 8000  # Limit the number of characters from each webpage to stay under the token limit

    print(f"Starting Google search for term: '{term}'")

    with requests.Session() as session:
        while start < num_results:
            try:
                user_agent = random.choice(_useragent_list)
                headers = {
                    'User-Agent': user_agent
                }
                resp = session.get(
                    url="https://www.google.com/search",
                    headers=headers,
                    params={
                        "q": term,
                        "num": num_results - start,
                        "hl": lang,
                        "start": start,
                        "safe": safe,
                    },
                    timeout=timeout,
                    verify=ssl_verify,
                )
                resp.raise_for_status()
                print(f"Successfully retrieved search results page (start={start})")
            except requests.exceptions.RequestException as e:
                print(f"Error retrieving search results: {e}")
                break

            soup = BeautifulSoup(resp.text, "html.parser")
            result_block = soup.find_all("div", attrs={"class": "g"})
            if not result_block:
                print("No results found on this page")
                break
            
            print(f"Found {len(result_block)} results on this page")
            for result in result_block:
                link = result.find("a", href=True)
                if link:
                    link = link["href"]
                    print(f"Processing link: {link}")
                    try:
                        webpage = session.get(link, headers=headers, timeout=timeout)
                        webpage.raise_for_status()
                        visible_text = extract_text_from_webpage(webpage.text)
                        if len(visible_text) > max_chars_per_page:
                            visible_text = visible_text[:max_chars_per_page] + "..."
                        all_results.append({"link": link, "text": visible_text})
                        print(f"Successfully extracted text from {link}")
                    except requests.exceptions.RequestException as e:
                        print(f"Error retrieving webpage content: {e}")
                        all_results.append({"link": link, "text": None})
                else:
                    print("No link found for this result")
                    all_results.append({"link": None, "text": None})
            start += len(result_block)

    print(f"Search completed. Total results: {len(all_results)}")
    print("Search results:")
    for i, result in enumerate(all_results, 1):
        print(f"Result {i}:")
        print(f"  Link: {result['link']}")
        if result['text']:
            print(f"  Text: {result['text'][:100]}...")  # Print first 100 characters
        else:
            print("  Text: None")
    print("End of search results")

    if not all_results:
        print("No search results found. Returning a default message.")
        return [{"link": None, "text": "No information found in the web search results."}]

    return all_results

def fetch_google_news_rss(query, num_results=10):
    base_url = "https://news.google.com/rss/search"
    params = {
        "q": query,
        "hl": "en-US",
        "gl": "US",
        "ceid": "US:en"
    }
    url = f"{base_url}?{urllib.parse.urlencode(params)}"
    
    try:
        feed = feedparser.parse(url)
        articles = []
        
        for entry in feed.entries[:num_results]:
            article = {
                "published_date": entry.get("published", "N/A"),
                "title": entry.get("title", "N/A"),
                "url": entry.get("link", "N/A"),
                "content": entry.get("summary", "N/A")
            }
            articles.append(article)
        
        return articles
    except Exception as e:
        print(f"Error fetching news: {str(e)}")
        return []

def summarize_news_content(content, model):
    prompt_template = """
    Summarize the following news article in a concise manner:
    {content}
    
    Summary:
    """
    prompt = ChatPromptTemplate.from_template(prompt_template)
    formatted_prompt = prompt.format(content=content)
    full_response = generate_chunked_response(model, formatted_prompt, max_tokens=200)
    
    # Extract only the summary part
    summary_parts = full_response.split("Summary:")
    if len(summary_parts) > 1:
        summary = summary_parts[-1].strip()
    else:
        summary = full_response.strip()
    
    # Create a cleaned version of the summary
    lines = summary.split('\n')
    cleaned_lines = [line for line in lines if not line.strip().startswith(("Human:", "Assistant:", "Summary:"))]
    cleaned_summary = ' '.join(cleaned_lines).strip()
    
    return summary, cleaned_summary

def process_news(query, temperature, top_p, repetition_penalty, news_source):
    model = get_model(temperature, top_p, repetition_penalty)
    embed = get_embeddings()
    
    if news_source == "Google News RSS":
        articles = fetch_google_news_rss(query)
    elif news_source == "Golomt Bank":
        articles = fetch_golomt_bank_news()
    else:
        return "Invalid news source selected."
    
    if not articles:
        return f"No news articles found for the given {news_source}."
    
    processed_articles = []
    
    for article in articles:
        try:
            # Remove HTML tags from content
            clean_content = BeautifulSoup(article["content"], "html.parser").get_text()
            
            # If content is very short, use the title as content
            if len(clean_content) < 50:
                clean_content = article["title"]
            
            full_summary, cleaned_summary = summarize_news_content(clean_content, model)
            relevance_score = calculate_relevance_score(cleaned_summary, model)
            print(f"Relevance score for article '{article['title']}': {relevance_score}")  # Debug print
            
            processed_article = {
                "published_date": article["published_date"],
                "title": article["title"],
                "url": article["url"],
                "content": clean_content,
                "summary": full_summary,
                "cleaned_summary": cleaned_summary,
                "relevance_score": relevance_score
            }
            processed_articles.append(processed_article)
        except Exception as e:
            print(f"Error processing article: {str(e)}")
    
    # Debug print
    print("Processed articles:")
    for article in processed_articles:
        print(f"Title: {article['title']}, Score: {article['relevance_score']}")
        
    if not processed_articles:
        return f"Failed to process any news articles from {news_source}. Please try again or check the summarization process."
    
    # Add processed articles to the database
    docs = [Document(page_content=article["cleaned_summary"], metadata={
        "source": article["url"],
        "title": article["title"],
        "published_date": article["published_date"],
        "relevance_score": article["relevance_score"]
    }) for article in processed_articles]
    
    try:
        if os.path.exists("faiss_database"):
            database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
            database.add_documents(docs)
        else:
            database = FAISS.from_documents(docs, embed)
        
        database.save_local("faiss_database")
        
        # Update news_database for excel export
        global news_database
        news_database = processed_articles  # Directly assign the processed articles
        
        print("Updated news_database:")
        for article in news_database:
            print(f"Title: {article['title']}, Score: {article['relevance_score']}")
        
        return f"Processed and added {len(processed_articles)} news articles from {news_source} to the database."
    except Exception as e:
        return f"Error adding articles to the database: {str(e)}"

def fetch_articles_from_page(url):
    response = requests.get(url)
    response.raise_for_status()
    soup = BeautifulSoup(response.content, 'html.parser')
    articles = soup.find_all('div', class_='entry-post gt-box-shadow-2')
    return articles, soup

def fetch_articles_from_page(url):
    response = requests.get(url)
    response.raise_for_status()
    soup = BeautifulSoup(response.content, 'html.parser')
    articles = soup.find_all('div', class_='entry-post gt-box-shadow-2')
    return articles, soup

def extract_articles(articles):
    article_data = []
    for article in articles:
        title_div = article.find('h2', class_='entry-title')
        title = title_div.get_text(strip=True) if title_div else "No Title"
        date_div = article.find('div', class_='entry-date gt-meta')
        date = date_div.get_text(strip=True) if date_div else "No Date"
        link_tag = article.find('a')
        link = link_tag['href'] if link_tag else "No Link"
        if not link.startswith('http'):
            link = "https://golomtbank.com" + link
        article_response = requests.get(link)
        article_response.raise_for_status()
        article_soup = BeautifulSoup(article_response.content, 'html.parser')
        article_content_div = article_soup.find('div', class_='entry-content')
        article_content = article_content_div.get_text(strip=True) if article_content_div else "No content found"
        article_data.append({
            'title': title,
            'date': date,
            'link': link,
            'content': article_content
        })
    return article_data

def fetch_golomt_bank_news(num_results=20):
    base_url = "https://golomtbank.com/en/rnews"
    current_page_url = base_url
    all_articles = []
    
    try:
        while len(all_articles) < num_results:
            print(f"Fetching articles from: {current_page_url}")
            articles, soup = fetch_articles_from_page(current_page_url)
            if not articles:
                print("No articles found on this page.")
                break
            all_articles.extend(extract_articles(articles))
            print(f"Total articles fetched so far: {len(all_articles)}")
            if len(all_articles) >= num_results:
                all_articles = all_articles[:num_results]
                break
            next_page_link = soup.find('a', class_='next')
            if not next_page_link:
                print("No next page link found.")
                break
            current_page_url = next_page_link['href']
            if not current_page_url.startswith('http'):
                current_page_url = "https://golomtbank.com" + current_page_url

        return [
            {
                "published_date": article['date'],
                "title": article['title'],
                "url": article['link'],
                "content": article['content']
            } for article in all_articles
        ]
    except Exception as e:
        print(f"Error fetching Golomt Bank news: {str(e)}")
        return []
        
def export_news_to_excel():
    global news_database
    
    if not news_database:
        return "No articles to export. Please fetch news first."
    
    print("Exporting the following articles:")
    for article in news_database:
        print(f"Title: {article['title']}, Score: {article.get('relevance_score', 'N/A')}")
    
    df = pd.DataFrame(news_database)
    
    # Ensure relevance_score is present and convert to float
    if 'relevance_score' not in df.columns:
        df['relevance_score'] = 0.0
    else:
        df['relevance_score'] = pd.to_numeric(df['relevance_score'], errors='coerce').fillna(0.0)
    
    # Use the cleaned summary for the Excel export
    if 'cleaned_summary' in df.columns:
        df['summary'] = df['cleaned_summary']
        df = df.drop(columns=['cleaned_summary'])
    
    # Reorder columns to put relevance_score after summary
    columns = ['published_date', 'title', 'url', 'content', 'summary', 'relevance_score']
    df = df[[col for col in columns if col in df.columns]]
    
    print("Final DataFrame before export:")
    print(df[['title', 'relevance_score']])
    
    with NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp:
        excel_path = tmp.name
        df.to_excel(excel_path, index=False, engine='openpyxl')
        print(f"Excel file saved to: {excel_path}")
        print("Final relevance scores before export:")
    for article in news_database:
        print(f"Title: {article['title']}, Score: {article.get('relevance_score', 'N/A')}")
    
    return excel_path

def calculate_relevance_score(summary, model):
    prompt_template = PromptTemplate(
        input_variables=["summary"],
        template="""You are a financial analyst tasked with providing a relevance score to news summaries. 
        The score should be based on the financial significance and impact of the news. 
    
        Consider the following factors when assigning relevance:
        - Earnings reports and financial performance
        - Debt issuance or restructuring
        - Mergers, acquisitions, or divestments
        - Changes in key leadership (e.g., CEO, CFO)
        - Regulatory changes or legal issues affecting the company
        - Major product launches or market expansion
        - Significant shifts in market share or competitive landscape
        - Macroeconomic factors directly impacting the company or industry
        - Stock price movements and trading volume changes
        - Dividend announcements or changes in capital allocation
        - Credit rating changes
        - Material financial events (e.g., bankruptcy, major contracts)
    
        Use the following scoring guide:
        - 0.00-0.20: Not relevant to finance or economics
        - 0.21-0.40: Slightly relevant, but minimal financial impact
        - 0.41-0.60: Moderately relevant, some financial implications
        - 0.61-0.80: Highly relevant, significant financial impact 
        - 0.81-1.00: Extremely relevant, major financial implications
    
        Provide a score between 0.00 and 1.00, where 0.00 is not relevant at all, and 1.00 is extremely relevant from a financial perspective.
    
        Summary: {summary}

        Relevance Score:"""
    )

    chain = LLMChain(llm=model, prompt=prompt_template)
    response = chain.run(summary=summary)
    
    print(f"Raw relevance score response: {response}")  # Debug print
    
    try:
        # Extract the score from the response
        score_match = re.search(r'Relevance Score:\s*(\d+\.\d+)', response)
        if score_match:
            score = float(score_match.group(1))
            final_score = min(max(score, 0.00), 1.00)  # Ensure the score is between 0.00 and 1.00
            print(f"Processed relevance score: {final_score}")  # Debug print
            return final_score
        else:
            raise ValueError("No relevance score found in the response")
    except ValueError as e:
        print(f"Error parsing relevance score: {e}")
        return 0.00
    
def ask_question(question, temperature, top_p, repetition_penalty, web_search, google_news_rss):
    global conversation_history

    if not question:
        return "Please enter a question."

    model = get_model(temperature, top_p, repetition_penalty)
    embed = get_embeddings()

    # Check if the FAISS database exists
    if os.path.exists("faiss_database"):
        database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
    else:
        database = None

    if web_search:
        search_results = google_search(question)
        web_docs = [Document(page_content=result["text"], metadata={"source": result["link"]}) for result in search_results if result["text"]]
        
        if database is None:
            database = FAISS.from_documents(web_docs, embed)
        else:
            database.add_documents(web_docs)
        
        database.save_local("faiss_database")
        
        context_str = "\n".join([doc.page_content for doc in web_docs])
        
        prompt_template = """
        Answer the question based on the following web search results:
        Web Search Results:
        {context}
        Current Question: {question}
        If the web search results don't contain relevant information, state that the information is not available in the search results.
        Provide a concise and direct answer to the question without mentioning the web search or these instructions:
        """
        prompt_val = ChatPromptTemplate.from_template(prompt_template)
        formatted_prompt = prompt_val.format(context=context_str, question=question)
    
    elif google_news_rss:
        if database is None:
            return "No news articles available. Please fetch news articles first."

        retriever = database.as_retriever()
        relevant_docs = retriever.get_relevant_documents(question)
        context_str = "\n".join([f"Title: {doc.metadata.get('title', 'N/A')}\nURL: {doc.metadata.get('source', 'N/A')}\nSummary: {doc.page_content}" for doc in relevant_docs])

        prompt_template = """
        Answer the question based on the following news summaries:
        News Summaries:
        {context}
        Current Question: {question}
        If the news summaries don't contain relevant information, state that the information is not available in the news articles.
        Provide a concise and direct answer to the question without mentioning the news summaries or these instructions:
        """
        prompt_val = ChatPromptTemplate.from_template(prompt_template)
        formatted_prompt = prompt_val.format(context=context_str, question=question)
    else:
        if database is None:
            return "No documents available. Please upload documents, enable web search, or fetch news articles to answer questions."

        history_str = "\n".join([f"Q: {item['question']}\nA: {item['answer']}" for item in conversation_history])

        if is_related_to_history(question, conversation_history):
            context_str = "No additional context needed. Please refer to the conversation history."
        else:
            retriever = database.as_retriever()
            relevant_docs = retriever.get_relevant_documents(question)
            context_str = "\n".join([doc.page_content for doc in relevant_docs])

        prompt_val = ChatPromptTemplate.from_template(prompt)
        formatted_prompt = prompt_val.format(history=history_str, context=context_str, question=question)

    full_response = generate_chunked_response(model, formatted_prompt)
    
    # Extract only the part after the last occurrence of a prompt-like sentence
    answer_patterns = [
        r"Provide a concise and direct answer to the question without mentioning the web search or these instructions:",
        r"Provide a concise and direct answer to the question without mentioning the news summaries or these instructions:",
        r"Provide a concise and direct answer to the question:",
        r"Answer:"
    ]
    
    for pattern in answer_patterns:
        match = re.split(pattern, full_response, flags=re.IGNORECASE)
        if len(match) > 1:
            answer = match[-1].strip()
            break
    else:
        # If no pattern is found, return the full response
        answer = full_response.strip()

    if not web_search and not google_news_rss:
        memory_database[question] = answer
        conversation_history = manage_conversation_history(question, answer, conversation_history)

    return answer

def extract_db_to_excel():
    embed = get_embeddings()
    database = FAISS.load_local("faiss_database", embed, allow_dangerous_deserialization=True)
    
    documents = database.docstore._dict.values()
    data = [{"page_content": doc.page_content, "metadata": json.dumps(doc.metadata)} for doc in documents]
    df = pd.DataFrame(data)
    
    with NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp:
        excel_path = tmp.name
        df.to_excel(excel_path, index=False)
    
    return excel_path

def export_memory_db_to_excel():
    data = [{"question": question, "answer": answer} for question, answer in memory_database.items()]
    df_memory = pd.DataFrame(data)
    
    data_history = [{"question": item["question"], "answer": item["answer"]} for item in conversation_history]
    df_history = pd.DataFrame(data_history)
    
    with NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp:
        excel_path = tmp.name
        with pd.ExcelWriter(excel_path, engine='openpyxl') as writer:
            df_memory.to_excel(writer, sheet_name='Memory Database', index=False)
            df_history.to_excel(writer, sheet_name='Conversation History', index=False)
    
    return excel_path

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Chat with your PDF documents and News")
    
    with gr.Row():
        file_input = gr.Files(label="Upload your PDF documents", file_types=[".pdf"])
        update_button = gr.Button("Update Vector Store")
        use_recursive_splitter = gr.Checkbox(label="Use Recursive Text Splitter", value=False)
        parser_dropdown = gr.Dropdown(
            choices=["PyPDF", "PDFMiner", "Camelot"],
            label="Select Parser",
            value="PyPDF"
        )
    
    update_output = gr.Textbox(label="Update Status")
    update_button.click(update_vectors, inputs=[file_input, use_recursive_splitter, parser_dropdown], outputs=update_output)
    
    with gr.Row():
        with gr.Column(scale=2):
            chatbot = gr.Chatbot(label="Conversation")
            question_input = gr.Textbox(label="Ask a question about your documents or news")
            submit_button = gr.Button("Submit")
        with gr.Column(scale=1):
            temperature_slider = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.5, step=0.1)
            top_p_slider = gr.Slider(label="Top P", minimum=0.0, maximum=1.0, value=0.9, step=0.1)
            repetition_penalty_slider = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.0, step=0.1)
            web_search_checkbox = gr.Checkbox(label="Enable Web Search", value=False)
            google_news_rss_checkbox = gr.Checkbox(label="Google News RSS", value=False)
    
    with gr.Row():
        news_source_dropdown = gr.Dropdown(
            choices=["Google News RSS", "Golomt Bank"],
            label="Select News Source",
            value="Google News RSS"
        )
        news_query_input = gr.Textbox(label="Enter news query (for Google News RSS)")
        fetch_news_button = gr.Button("Fetch News")
    
    news_fetch_output = gr.Textbox(label="News Fetch Status")
    
    def chat(question, history, temperature, top_p, repetition_penalty, web_search, google_news_rss):
        answer = ask_question(question, temperature, top_p, repetition_penalty, web_search, google_news_rss)
        history.append((question, answer))
        return "", history
    
    submit_button.click(chat, inputs=[question_input, chatbot, temperature_slider, top_p_slider, repetition_penalty_slider, web_search_checkbox, google_news_rss_checkbox], outputs=[question_input, chatbot])
    
    def fetch_news(query, temperature, top_p, repetition_penalty, news_source):
        return process_news(query, temperature, top_p, repetition_penalty, news_source)
    
    fetch_news_button.click(
        fetch_news,
        inputs=[news_query_input, temperature_slider, top_p_slider, repetition_penalty_slider, news_source_dropdown],
        outputs=news_fetch_output
    )
    
    extract_button = gr.Button("Extract Database to Excel")
    excel_output = gr.File(label="Download Excel File")
    extract_button.click(extract_db_to_excel, inputs=[], outputs=excel_output)
    
    export_memory_button = gr.Button("Export Memory Database to Excel")
    memory_excel_output = gr.File(label="Download Memory Excel File")
    export_memory_button.click(export_memory_db_to_excel, inputs=[], outputs=memory_excel_output)
    
    export_news_button = gr.Button("Download News Excel File")
    news_excel_output = gr.File(label="Download News Excel File")
    export_news_button.click(export_news_to_excel, inputs=[], outputs=news_excel_output)
    
    clear_button = gr.Button("Clear Cache")
    clear_output = gr.Textbox(label="Cache Status")
    clear_button.click(clear_cache, inputs=[], outputs=clear_output)

if __name__ == "__main__":
    demo.launch()