app.py

import requests
import gradio as gr
from bs4 import BeautifulSoup
import logging
from urllib.parse import urlparse
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
from trafilatura import fetch_url, extract
from trafilatura import extract
from requests.exceptions import Timeout
from trafilatura.settings import use_config
from urllib.request import urlopen, Request
import json
from huggingface_hub import InferenceClient
import random
import time
from sentence_transformers import SentenceTransformer, util
import torch
from datetime import datetime
import os
from dotenv import load_dotenv
import certifi
import requests
import scrapy
from scrapy.crawler import CrawlerProcess
from scrapy import signals
from scrapy.signalmanager import dispatcher
from scrapy.utils.log import configure_logging
from newspaper import Article
import html2text
from PyPDF2 import PdfReader
from io import BytesIO



# Load environment variables from a .env file
load_dotenv()

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# SearXNG instance details
SEARXNG_URL = 'https://shreyas094-searxng-local.hf.space/search'
SEARXNG_KEY = 'f9f07f93b37b8483aadb5ba717f556f3a4ac507b281b4ca01e6c6288aa3e3ae5'

# Use the environment variable
HF_TOKEN = os.getenv("HF_TOKEN")
client = InferenceClient(
    "mistralai/Mistral-Nemo-Instruct-2407",
    token=HF_TOKEN,
)

# Initialize the similarity model
similarity_model = SentenceTransformer('all-MiniLM-L6-v2')


# Set up a session with retry mechanism
def requests_retry_session(
    retries=0,
    backoff_factor=0.1,
    status_forcelist=(500, 502, 504),
    session=None,
):
    session = session or requests.Session()
    retry = Retry(
        total=retries,
        read=retries,
        connect=retries,
        backoff_factor=backoff_factor,
        status_forcelist=status_forcelist,
    )
    adapter = HTTPAdapter(max_retries=retry)
    session.mount('http://', adapter)
    session.mount('https://', adapter)
    return session

def is_valid_url(url):
    try:
        result = urlparse(url)
        return all([result.scheme, result.netloc])
    except ValueError:
        return False

def is_pdf(url):
    try:
        response = requests.head(url, allow_redirects=True)
        content_type = response.headers.get('Content-Type', '').lower()
        return 'application/pdf' in content_type
    except Exception as e:
        logger.error(f"Error checking content type for {url}: {e}")
        return False

def scrape_pdf(url):
    logger.info(f"Scraping PDF: {url}")
    try:
        response = requests.get(url)
        pdf_file = BytesIO(response.content)
        pdf_reader = PdfReader(pdf_file)
        text = ""
        for page in pdf_reader.pages:
            text += page.extract_text() + "\n"
        return text.strip()
    except Exception as e:
        logger.error(f"Error scraping PDF {url}: {e}")
        return ""

def scrape_with_newspaper(url):
    logger.info(f"Starting to scrape with Newspaper3k: {url}")
    try:
        if is_pdf(url):
            return scrape_pdf(url)
        else:
            article = Article(url)
            article.download()
            article.parse()
            return article.text
    except Exception as e:
        logger.error(f"Error scraping {url} with Newspaper3k: {e}")
        return ""

def scrape_with_html2text(url):
    logger.info(f"Starting to scrape with html2text: {url}")
    try:
        if is_pdf(url):
            return scrape_pdf(url)
        else:
            response = requests.get(url)
            h = html2text.HTML2Text()
            h.ignore_links = True
            return h.handle(response.text)
    except Exception as e:
        logger.error(f"Error scraping {url} with html2text: {e}")
        return ""

def rephrase_query(chat_history, query, temperature=0.2):
    system_prompt = """You are a highly intelligent and context-aware conversational assistant. Your task is to accurately rephrase the given query based on the provided conversation context. Follow these steps:
1. Assess whether the new query logically follows from the conversation context or introduces a new, unrelated topic.
2. If it is a continuation, incorporate the most relevant details from the context to make the rephrased query more specific and aligned with the ongoing conversation.
3. If it introduces a new topic, rewrite the query to ensure clarity, precision, and suitability for a standalone search, avoiding any irrelevant context from the conversation.
4. Return ONLY the rephrased query, ensuring it is concise, clear, and contextually accurate, without any additional commentary or explanation."""

    user_prompt = f"""
Conversation context:
{chat_history}

New query: {query}

Rephrased query:
"""

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt}
    ]

    try:
        logger.info(f"Sending rephrasing request to LLM with temperature {temperature}")
        response = client.chat_completion(
            messages=messages,
            max_tokens=150,
            temperature=temperature
        )
        logger.info("Received rephrased query from LLM")
        rephrased_question = response.choices[0].message.content.strip()

        # Remove surrounding quotes if present
        if (rephrased_question.startswith('"') and rephrased_question.endswith('"')) or \
           (rephrased_question.startswith("'") and rephrased_question.endswith("'")):
            rephrased_question = rephrased_question[1:-1].strip()

        logger.info(f"Rephrased Query (cleaned): {rephrased_question}")
        return rephrased_question
    except Exception as e:
        logger.error(f"Error rephrasing query with LLM: {e}")
        return query  # Fallback to original query if rephrasing fails

def rerank_documents(query, documents):
    try:
        # Step 1: Encode the query and document summaries
        query_embedding = similarity_model.encode(query, convert_to_tensor=True)
        doc_summaries = [doc['summary'] for doc in documents]
        
        if not doc_summaries:
            logger.warning("No document summaries to rerank.")
            return documents  # Return original documents if there's nothing to rerank
        
        doc_embeddings = similarity_model.encode(doc_summaries, convert_to_tensor=True)
        
        # Step 2: Compute Cosine Similarity
        cosine_scores = util.cos_sim(query_embedding, doc_embeddings)[0]
        
        # Step 3: Compute Dot Product Similarity
        dot_product_scores = torch.matmul(query_embedding, doc_embeddings.T)
        
        # Ensure dot_product_scores is a 1-D tensor
        if dot_product_scores.dim() == 0:
            dot_product_scores = dot_product_scores.unsqueeze(0)
        
        # Combine documents, cosine scores, and dot product scores
        scored_documents = list(zip(documents, cosine_scores, dot_product_scores))

        # Step 4: Sort documents by cosine similarity score
        scored_documents.sort(key=lambda x: x[1], reverse=True)
        
        # Step 5: Return only the top 5 documents
        reranked_docs = [doc[0] for doc in scored_documents[:5]]
        logger.info(f"Reranked to top {len(reranked_docs)} documents.")
        return reranked_docs
    except Exception as e:
        logger.error(f"Error during reranking documents: {e}")
        return documents[:5]  # Fallback to first 5 documents if reranking fails

def compute_similarity(text1, text2):
    # Encode the texts
    embedding1 = similarity_model.encode(text1, convert_to_tensor=True)
    embedding2 = similarity_model.encode(text2, convert_to_tensor=True)
    
    # Compute cosine similarity
    cosine_similarity = util.pytorch_cos_sim(embedding1, embedding2)
    
    return cosine_similarity.item()

def is_content_unique(new_content, existing_contents, similarity_threshold=0.8):
    for existing_content in existing_contents:
        similarity = compute_similarity(new_content, existing_content)
        if similarity > similarity_threshold:
            return False
    return True

def assess_relevance_and_summarize(llm_client, query, document, temperature=0.2):
    system_prompt = """You are a world class AI assistant. Your task is to assess whether the given text is relevant to the user's query and provide a brief summary if it is relevant."""

    user_prompt = f"""
Query: {query}

Document Content:
{document['content']}

Instructions:
1. Assess if the document is relevant to the QUERY  made by the user.
2. If relevant, summarize the main points in 1-2 sentences.
3. If not relevant, simply state "Not relevant".

Your response should be in the following format:
Relevant: [Yes/No]
Summary: [Your 1-2 sentence summary if relevant, or "Not relevant" if not]

Remember to focus on financial aspects and implications in your assessment and summary.
"""

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt}
    ]

    try:
        response = llm_client.chat_completion(
            messages=messages,
            max_tokens=150,
            temperature=temperature,
            top_p=0.9
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        logger.error(f"Error assessing relevance and summarizing with LLM: {e}")
        return "Error: Unable to assess relevance and summarize"

def scrape_full_content(url, max_chars=3000):
    try:
        logger.info(f"Scraping full content from: {url}")
        
        # Try newspaper first
        content = scrape_with_newspaper(url)
        
        # If newspaper fails, try html2text
        if not content:
            content = scrape_with_html2text(url)
        
        # Limit the content to max_chars
        return content[:max_chars] if content else ""
    except Exception as e:
        logger.error(f"Error scraping full content from {url}: {e}")
        return ""

def llm_summarize(json_input, llm_client, temperature=0.2):
    system_prompt = """You are Sentinel, a world-class Financial analysis AI model who is expert at searching the web and answering user's queries. You are also an expert at summarizing web pages or documents and searching for content in them."""

    user_prompt = f"""
Please provide a comprehensive summary based on the following JSON input:
{json_input}

Instructions:
1. Analyze the query and the provided documents.
2. Write a detailed, long, and complete research document that is informative and relevant to the user's query.
3. Use an unbiased and professional tone in your response.
4. Do not repeat text verbatim from the input.
5. Provide the answer in the response itself.
6. You can use markdown to format your response.
7. Use bullet points to list information where appropriate.
8. Cite the answer using [number] notation along with the appropriate source URL embedded in the notation.
9. Place these citations at the end of the relevant sentences.
10. You can cite the same sentence multiple times if it's relevant to different parts of your answer.

Your response should be detailed, informative, accurate, and directly relevant to the user's query."""

    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": user_prompt}
    ]

    try:
        response = llm_client.chat_completion(
            messages=messages,
            max_tokens=10000,
            temperature=temperature,
            frequency_penalty=1.2,
            top_p=0.9
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        logger.error(f"Error in LLM summarization: {e}")
        return "Error: Unable to generate a summary. Please try again."

def search_and_scrape(query, chat_history, num_results=5, max_chars=3000, time_range="", language="all", category="", 
                      engines=[], safesearch=2, method="GET", llm_temperature=0.2, timeout=5):
    try:
        # Step 1: Rephrase the Query
        rephrased_query = rephrase_query(chat_history, query, temperature=llm_temperature)
        logger.info(f"Rephrased Query: {rephrased_query}")

        if not rephrased_query or rephrased_query.lower() == "not_needed":
            logger.info("No need to perform search based on the rephrased query.")
            return "No search needed for the provided input."

        # Search query parameters
        params = {
            'q': rephrased_query,
            'format': 'json',
            'time_range': time_range,
            'language': language,
            'category': category,
            'engines': ','.join(engines),
            'safesearch': safesearch
        }

        # Remove empty parameters
        params = {k: v for k, v in params.items() if v != ""}

        # If no engines are specified, set default engines
        if 'engines' not in params:
            params['engines'] = 'google'  # Default to 'google' or any preferred engine
            logger.info("No engines specified. Defaulting to 'google'.")

        # Headers for SearXNG request
        headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
            'Accept': 'application/json, text/javascript, */*; q=0.01',
            'Accept-Language': 'en-US,en;q=0.5',
            'Origin': 'https://shreyas094-searxng-local.hf.space',
            'Referer': 'https://shreyas094-searxng-local.hf.space/',
            'DNT': '1',
            'Connection': 'keep-alive',
            'Sec-Fetch-Dest': 'empty',
            'Sec-Fetch-Mode': 'cors',
            'Sec-Fetch-Site': 'same-origin',
        }

        scraped_content = []
        page = 1
        while len(scraped_content) < num_results:
            # Update params with current page
            params['pageno'] = page

            # Send request to SearXNG
            logger.info(f"Sending request to SearXNG for query: {rephrased_query} (Page {page})")
            session = requests_retry_session()

            try:
                if method.upper() == "GET":
                    response = session.get(SEARXNG_URL, params=params, headers=headers, timeout=10, verify=certifi.where())
                else:  # POST
                    response = session.post(SEARXNG_URL, data=params, headers=headers, timeout=10, verify=certifi.where())
                
                response.raise_for_status()
            except requests.exceptions.RequestException as e:
                logger.error(f"Error during SearXNG request: {e}")
                return f"An error occurred during the search request: {e}"

            search_results = response.json()
            logger.debug(f"SearXNG Response: {search_results}")

            results = search_results.get('results', [])
            if not results:
                logger.warning(f"No more results returned from SearXNG on page {page}.")
                break

            for result in results:
                if len(scraped_content) >= num_results:
                    break
        
                url = result.get('url', '')
                title = result.get('title', 'No title')
        
                if not is_valid_url(url):
                    logger.warning(f"Invalid URL: {url}")
                    continue
        
                try:
                    logger.info(f"Scraping content from: {url}")
                    
                    content = scrape_full_content(url, max_chars, timeout)
                    
                    if not content:
                        logger.warning(f"Failed to scrape content from {url}")
                        continue
                    
                    scraped_content.append({
                        "title": title,
                        "url": url,
                        "content": content
                    })
                    logger.info(f"Successfully scraped content from {url}. Total scraped: {len(scraped_content)}")
                except requests.exceptions.RequestException as e:
                    logger.error(f"Error scraping {url}: {e}")
                except Exception as e:
                    logger.error(f"Unexpected error while scraping {url}: {e}")

            page += 1

        if not scraped_content:
            logger.warning("No content scraped from search results.")
            return "No content could be scraped from the search results."

        logger.info(f"Successfully scraped {len(scraped_content)} documents.")

        # Step 3: Assess relevance, summarize, and check for uniqueness
        relevant_documents = []
        unique_summaries = []
        for doc in scraped_content:
            assessment = assess_relevance_and_summarize(client, rephrased_query, doc, temperature=llm_temperature)
            relevance, summary = assessment.split('\n', 1)
            
            if relevance.strip().lower() == "relevant: yes":
                summary_text = summary.replace("Summary: ", "").strip()
                
                if is_content_unique(summary_text, unique_summaries):
                    relevant_documents.append({
                        "title": doc['title'],
                        "url": doc['url'],
                        "summary": summary_text,
                        "scraper": doc['scraper']
                    })
                    unique_summaries.append(summary_text)
                else:
                    logger.info(f"Skipping similar content: {doc['title']}")

        if not relevant_documents:
            logger.warning("No relevant and unique documents found.")
            return "No relevant and unique financial news found for the given query."
            logger.debug(f"Assessment result: {assessment}")

        # Step 4: Rerank documents based on similarity to query
        reranked_docs = rerank_documents(rephrased_query, relevant_documents)
        
        if not reranked_docs:
            logger.warning("No documents remained after reranking.")
            return "No relevant financial news found after filtering and ranking."
        
        logger.info(f"Reranked and filtered to top {len(reranked_docs)} unique, finance-related documents.")

        # Step 5: Scrape full content for top documents (up to num_results)
        for doc in reranked_docs[:num_results]:
            full_content = scrape_full_content(doc['url'], scraper, max_chars)
            doc['full_content'] = full_content
    
        # Prepare JSON for LLM
        llm_input = {
            "query": query,
            "documents": [
                {
                    "title": doc['title'],
                    "url": doc['url'],
                    "summary": doc['summary'],
                    "full_content": doc['full_content']
                } for doc in reranked_docs[:num_results]
            ]
        }
    
        # Step 6: LLM Summarization
        llm_summary = llm_summarize(json.dumps(llm_input), client, temperature=llm_temperature)
    
        return llm_summary

    except Exception as e:
        logger.error(f"Unexpected error in search_and_scrape: {e}")
        return f"An unexpected error occurred during the search and scrape process: {e}"


def chat_function(message, history, num_results, scraper, max_chars, time_range, language, category, engines, safesearch, method, llm_temperature):
    chat_history = "\n".join([f"{role}: {msg}" for role, msg in history])
    
    response = search_and_scrape(
        query=message,
        chat_history=chat_history,
        num_results=num_results,
        scraper=scraper,
        max_chars=max_chars,
        time_range=time_range,
        language=language,
        category=category,
        engines=engines,
        safesearch=safesearch,
        method=method,
        llm_temperature=llm_temperature
    )
    
    yield response

iface = gr.ChatInterface(
    chat_function,
    title="SearXNG Scraper for Financial News",
    description="Enter your query, and I'll search the web for the most recent and relevant financial news, scrape content, and provide summarized results.",
    theme=gr.Theme.from_hub("allenai/gradio-theme"),
    additional_inputs=[
        gr.Slider(5, 20, value=10, step=1, label="Number of initial results"),
        gr.Dropdown(["bs4", "trafilatura", "scrapy", "newspaper"], value="newspaper", label="Scraping Method"),
        gr.Slider(500, 10000, value=1500, step=100, label="Max characters to retrieve"),
        gr.Dropdown(["", "day", "week", "month", "year"], value="year", label="Time Range"),
        gr.Dropdown(["all", "en", "fr", "de", "es", "it", "nl", "pt", "pl", "ru", "zh"], value="en", label="Language"),
        gr.Dropdown(["", "general", "news", "images", "videos", "music", "files", "it", "science", "social media"], value="", label="Category"),
        gr.Dropdown(
            ["google", "bing", "duckduckgo", "baidu", "yahoo", "qwant", "startpage"],
            multiselect=True,
            value=["google", "duckduckgo"],
            label="Engines"
        ),
        gr.Slider(0, 2, value=2, step=1, label="Safe Search Level"),
        gr.Radio(["GET", "POST"], value="POST", label="HTTP Method"),
        gr.Slider(0, 1, value=0.2, step=0.1, label="LLM Temperature"),
    ],
    additional_inputs_accordion=gr.Accordion("⚙️ Advanced Parameters", open=True),
    retry_btn="Retry",
    undo_btn="Undo",
    clear_btn="Clear",
    chatbot=gr.Chatbot(
        show_copy_button=True,
        likeable=True,
        layout="bubble",
        height=500,
    )
)

if __name__ == "__main__":
    logger.info("Starting the SearXNG Scraper for Financial News using ChatInterface with Advanced Parameters")
    iface.launch(share=True)