app.py

import os
import json
import re
import gradio as gr
import pandas as pd
import requests
import random
import urllib.parse
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")

class Agent1:
    def __init__(self, model):
        self.model = model

    def rephrase_and_split(self, user_input: str) -> List[str]:
        rephrase_prompt = PromptTemplate(
            input_variables=["query"],
            template="""
            Your task is to rephrase the given query into one or more concise, search-engine-friendly formats.
            If the query contains multiple distinct questions, split them.
            Provide ONLY the rephrased queries without any additional text or explanations, one per line.
            
            Query: {query}
            
            Rephrased queries:"""
        )
        
        chain = LLMChain(llm=self.model, prompt=rephrase_prompt)
        response = chain.run(query=user_input).strip()
        
        return [q.strip() for q in response.split('\n') if q.strip()]

    def process(self, user_input: str) -> Dict[str, List[Dict[str, str]]]:
        queries = self.rephrase_and_split(user_input)
        results = {}
        for query in queries:
            results[query] = google_search(query)
        return results

class Agent2:
    def __init__(self, model):
        self.model = model

    def validate_response(self, user_query: str, response: str) -> bool:
        validation_prompt = PromptTemplate(
            input_variables=["query", "response"],
            template="""
            Evaluate if the following response fully answers the user's query.
            User query: {query}
            Response: {response}
            
            Does the response fully answer the query? Answer with Yes or No:"""
        )
        
        chain = LLMChain(llm=self.model, prompt=validation_prompt)
        result = chain.run(query=user_query, response=response).strip().lower()
        return result == 'yes'

    def generate_follow_up_query(self, user_query: str, response: str) -> str:
        follow_up_prompt = PromptTemplate(
            input_variables=["query", "response"],
            template="""
            The following response did not fully answer the user's query.
            User query: {query}
            Response: {response}
            
            Generate a follow-up query to get more relevant information:"""
        )
        
        chain = LLMChain(llm=self.model, prompt=follow_up_prompt)
        return chain.run(query=user_query, response=response).strip()

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 rephrase_for_search(query, model):
    rephrase_prompt = PromptTemplate(
        input_variables=["query"],
        template="""
        Your task is to rephrase the given conversational query into a concise, search-engine-friendly format.
        Remove any conversational elements and focus on the core information need.
        Provide ONLY the rephrased query without any additional text or explanations.
        
        Conversational query: {query}
        
        Rephrased query:"""
    )
    
    chain = LLMChain(llm=model, prompt=rephrase_prompt)
    response = chain.run(query=query).strip()
    
    rephrased_query = response.replace("Rephrased query:", "").strip()
    
    if rephrased_query.lower() == query.lower() or len(rephrased_query) > len(query) * 1.5:
        common_words = set(['the', 'a', 'an', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', 'from', 'up', 'about', 'into', 'over', 'after'])
        keywords = [word.lower() for word in query.split() if word.lower() not in common_words]
        keywords = [word for word in keywords if word.isalnum()]
        return ' '.join(keywords)
    
    return rephrased_query

def ask_question(question, temperature, top_p, repetition_penalty, web_search):
    if not question:
        return "Please enter a question."

    model = get_model(temperature, top_p, repetition_penalty)
    embed = get_embeddings()
    agent1 = Agent1(model)
    agent2 = Agent2(model)

    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

    for attempt in range(max_attempts):
        try:
            if web_search:
                search_results = agent1.process(question)
                web_docs = []
                for query, results in search_results.items():
                    web_docs.extend([Document(page_content=result["text"], metadata={"source": result["link"], "query": query}) for result in 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([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 concise and direct answer to the original question without mentioning the web search or these instructions.
                Do not include any source information in your answer.
                """
            else:
                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(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 concise 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=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 concise 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()

            if not agent2.validate_response(question, answer):
                follow_up_query = agent2.generate_follow_up_query(question, answer)
                follow_up_results = agent1.process(follow_up_query)
                follow_up_docs = [Document(page_content=result["text"], metadata={"source": result["link"], "query": follow_up_query}) for results in follow_up_results.values() for result in results if result["text"]]
                database.add_documents(follow_up_docs)
                context_str += "\n" + "\n".join([f"Follow-up Query: {doc.metadata['query']}\nSource: {doc.metadata['source']}\nContent: {doc.page_content}" for doc in follow_up_docs])
                formatted_prompt = prompt_val.format(context=context_str, question=question)
                full_response = generate_chunked_response(model, formatted_prompt)
                answer = full_response.strip()

            if web_search:
                sources = set(doc.metadata['source'] for doc in web_docs)
                sources_section = "\n\nSources:\n" + "\n".join(f"- {source}" for source in sources)
                answer += sources_section

            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)

    def chat(question, history, temperature, top_p, repetition_penalty, web_search):
        answer = ask_question(question, temperature, top_p, repetition_penalty, web_search)
        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()