app.py

import os
import json
import re
import gradio as gr
import pandas as pd
import requests
import random
import urllib.parse
import spacy
import nltk
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
from typing import List, Dict
from tempfile import NamedTemporaryFile
from typing import List, Dict
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
from langchain_core.output_parsers import StrOutputParser
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceHub
from langchain_core.documents import Document

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

# Download necessary NLTK data
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')

class Agent1:
    def __init__(self):
        self.question_words = set(["what", "when", "where", "who", "whom", "which", "whose", "why", "how"])
        self.conjunctions = set(["and", "or"])
        self.pronouns = set(["it", "its", "they", "their", "them", "he", "his", "him", "she", "her", "hers"])
        self.context = {}

    def is_question(self, text: str) -> bool:
        words = word_tokenize(text.lower())
        return (words[0] in self.question_words or 
                text.strip().endswith('?') or 
                any(word in self.question_words for word in words))

    def find_subject(self, sentence):
        tokens = nltk.pos_tag(word_tokenize(sentence))
        subject = None
        for word, tag in tokens:
            if tag.startswith('NN'):
                subject = word
                break
            if tag == 'IN':  # Stop at preposition
                break
        return subject

    def replace_pronoun(self, questions: List[str]) -> List[str]:
        if len(questions) < 2:
            return questions

        subject = self.find_subject(questions[0])
        
        if not subject:
            return questions

        for i in range(1, len(questions)):
            words = word_tokenize(questions[i])
            for j, word in enumerate(words):
                if word.lower() in self.pronouns:
                    words[j] = subject
            questions[i] = ' '.join(words)

        return questions

    def rephrase_and_split(self, user_input: str) -> List[str]:
        words = word_tokenize(user_input)
        questions = []
        current_question = []

        for word in words:
            if word.lower() in self.conjunctions and current_question:
                if self.is_question(' '.join(current_question)):
                    questions.append(' '.join(current_question))
                current_question = []
            else:
                current_question.append(word)

        if current_question:
            if self.is_question(' '.join(current_question)):
                questions.append(' '.join(current_question))

        if not questions:
            return [user_input]

        questions = self.replace_pronoun(questions)

        return questions

    def update_context(self, query: str):
       tokens = nltk.pos_tag(word_tokenize(query))
       noun_phrases = []
       current_phrase = []

       for word, tag in tokens:
           if tag.startswith('NN') or tag.startswith('JJ'):
               current_phrase.append(word)
           else:
               if current_phrase:
                   noun_phrases.append(' '.join(current_phrase))
                   current_phrase = []

       if current_phrase:
           noun_phrases.append(' '.join(current_phrase))

       if noun_phrases:
           self.context['main_topic'] = noun_phrases[0]
           self.context['related_topics'] = noun_phrases[1:]
           self.context['last_query'] = query

    def apply_context(self, query: str) -> str:
       words = word_tokenize(query.lower())
       
       if (len(words) <= 5 or 
           any(word in self.pronouns for word in words) or
           (self.context.get('main_topic') and self.context['main_topic'].lower() not in query.lower())):
           
           new_query_parts = []
           main_topic_added = False

           for word in words:
               if word in self.pronouns and self.context.get('main_topic'):
                   new_query_parts.append(self.context['main_topic'])
                   main_topic_added = True
               else:
                   new_query_parts.append(word)

           if not main_topic_added and self.context.get('main_topic'):
               new_query_parts.append(f"in the context of {self.context['main_topic']}")

           query = ' '.join(new_query_parts)

       if self.context.get('last_query'):
           query = f"{self.context['last_query']} and now {query}"

       return query

    def process(self, user_input: str) -> tuple[List[str], Dict[str, List[Dict[str, str]]]]:
        self.update_context(user_input)
        contextualized_input = self.apply_context(user_input)
        queries = self.rephrase_and_split(contextualized_input)
        print("Identified queries:", queries)
        
        results = {}
        for query in queries:
            results[query] = google_search(query)
        
        return queries, results
        
def load_document(file: NamedTemporaryFile) -> List[Document]:
    """Loads and splits the document into pages."""
    loader = PyPDFLoader(file.name)
    return loader.load_and_split()

def update_vectors(files):
    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)
        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")
    
    return f"Vector store updated successfully. Processed {total_chunks} chunks from {len(files)} files."

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

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_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):
        try:
            chunk = model(prompt + full_response, max_new_tokens=max_tokens)
            chunk = chunk.strip()
            if chunk.endswith((".", "!", "?")):
                full_response += chunk
                break
            full_response += chunk
        except Exception as e:
            print(f"Error in generate_chunked_response: {e}")
            break
    return full_response.strip()

def extract_text_from_webpage(html):
    soup = BeautifulSoup(html, 'html.parser')
    for script in soup(["script", "style"]):
        script.extract()
    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

    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)}")
    
    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 ask_question(question, temperature, top_p, repetition_penalty, web_search, agent1=None):
    if not question:
        return "Please enter a question."

    if agent1 is None:
        agent1 = Agent1()

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

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

    max_attempts = 3
    context_reduction_factor = 0.7

    agent1.update_context(question)
    contextualized_question = agent1.apply_context(question)

    if web_search:
        queries, search_results = agent1.process(contextualized_question)
        all_answers = []

        for query in queries:
            for attempt in range(max_attempts):
                try:
                    web_docs = [Document(page_content=result["text"], metadata={"source": result["link"], "query": query}) for result in search_results[query] 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([f"Query: {doc.metadata['query']}\nSource: {doc.metadata['source']}\nContent: {doc.page_content}" for doc in web_docs])

                    prompt_template = """
                    Answer the question based on the following web search results:
                    Web Search Results:
                    {context}
                    Original 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 summarized and direct answer to the original question without mentioning the web search or these instructions.
                    Do not include any source information in your answer.                    
                    """

                    prompt_val = ChatPromptTemplate.from_template(prompt_template)
                    formatted_prompt = prompt_val.format(context=context_str, question=query)

                    full_response = generate_chunked_response(model, formatted_prompt)

                    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:",
                        r"Answer:",
                        r"Provide a summarized and direct answer to the original question without mentioning the web search or these instructions:",
                        r"Do not include any source information in your 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:
                        answer = full_response.strip()

                    all_answers.append(answer)
                    break

                except Exception as e:
                    print(f"Error in ask_question for query '{query}' (attempt {attempt + 1}): {e}")
                    if "Input validation error" in str(e) and attempt < max_attempts - 1:
                        print(f"Reducing context length for next attempt")
                    elif attempt == max_attempts - 1:
                        all_answers.append(f"I apologize, but I'm having trouble processing the query '{query}' due to its length or complexity.")

        answer = "\n\n".join(all_answers)
        sources = set(doc.metadata['source'] for docs in search_results.values() for doc in [Document(page_content=result["text"], metadata={"source": result["link"]}) for result in docs if result["text"]])
        sources_section = "\n\nSources:\n" + "\n".join(f"- {source}" for source in sources)
        answer += sources_section

        return answer

    else:
        for attempt in range(max_attempts):
            try:
                if database is None:
                    return "No documents available. Please upload documents or enable web search to answer questions."

                retriever = database.as_retriever()
                relevant_docs = retriever.get_relevant_documents(contextualized_question)
                context_str = "\n".join([doc.page_content for doc in relevant_docs])

                if attempt > 0:
                    words = context_str.split()
                    context_str = " ".join(words[:int(len(words) * context_reduction_factor)])

                prompt_template = """
                Answer the question based on the following context:
                Context:
                {context}
                Current Question: {question}
                If the context doesn't contain relevant information, state that the information is not available.
                Provide a summarized and direct answer to the question.
                Do not include any source information in your answer.
                """

                prompt_val = ChatPromptTemplate.from_template(prompt_template)
                formatted_prompt = prompt_val.format(context=context_str, question=contextualized_question)

                full_response = generate_chunked_response(model, formatted_prompt)

                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:",
                    r"Answer:",
                    r"Provide a summarized and direct answer to the original question without mentioning the web search or these instructions:",
                    r"Do not include any source information in your 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:
                    answer = full_response.strip()

                return answer

            except Exception as e:
                print(f"Error in ask_question (attempt {attempt + 1}): {e}")
                if "Input validation error" in str(e) and attempt < max_attempts - 1:
                    print(f"Reducing context length for next attempt")
                elif attempt == max_attempts - 1:
                    return f"I apologize, but I'm having trouble processing your question due to its length or complexity. Could you please try rephrasing it more concisely?"

    return "An unexpected error occurred. Please try again later."

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Chat with your PDF documents and Web Search")
    
    with gr.Row():
        file_input = gr.Files(label="Upload your PDF documents", file_types=[".pdf"])
        update_button = gr.Button("Upload PDF")
    
    update_output = gr.Textbox(label="Update Status")
    update_button.click(update_vectors, inputs=[file_input], outputs=update_output)
    
    with gr.Row():
        with gr.Column(scale=2):
            chatbot = gr.Chatbot(label="Conversation")
            question_input = gr.Textbox(label="Perplexity AI lite, enable web search to retrieve any web search results. Feel free to provide any feedbacks.")
            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)

    agent1 = Agent1()

    def chat(question, history, temperature, top_p, repetition_penalty, web_search):
        answer = ask_question(question, temperature, top_p, repetition_penalty, web_search, agent1)
        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], outputs=[question_input, chatbot])
    
    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()