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 requests.exceptions import Timeout 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 from newspaper import Article import PyPDF2 import io import requests import random import datetime from groq import Groq import os from mistralai import Mistral from dotenv import load_dotenv # Automatically get the current year current_year = datetime.datetime.now().year # 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, ) # Default API key for examples (replace with a dummy value or leave empty) GROQ_API_KEY = os.getenv("GROQ_API_KEY") # Initialize Groq client groq_client = Groq(api_key=GROQ_API_KEY) # Initialize Mistral client MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY") mistral_client = Mistral(api_key=MISTRAL_API_KEY) # 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 scrape_pdf_content(url, max_chars=3000, timeout=5): try: logger.info(f"Scraping PDF content from: {url}") # Download the PDF file response = requests.get(url, timeout=timeout) response.raise_for_status() # Create a PDF reader object pdf_reader = PyPDF2.PdfReader(io.BytesIO(response.content)) # Extract text from all pages content = "" for page in pdf_reader.pages: content += page.extract_text() + "\n" # Limit the content to max_chars return content[:max_chars] if content else "" except requests.Timeout: logger.error(f"Timeout error while scraping PDF content from {url}") return "" except Exception as e: logger.error(f"Error scraping PDF content from {url}: {e}") return "" def scrape_with_newspaper(url): if url.lower().endswith('.pdf'): return scrape_pdf_content(url) logger.info(f"Starting to scrape with Newspaper3k: {url}") try: article = Article(url) article.download() article.parse() # Combine title and text content = f"Title: {article.title}\n\n" content += article.text # Add publish date if available if article.publish_date: content += f"\n\nPublish Date: {article.publish_date}" # Add authors if available if article.authors: content += f"\n\nAuthors: {', '.join(article.authors)}" # Add top image URL if available if article.top_image: content += f"\n\nTop Image URL: {article.top_image}" return content except Exception as e: logger.error(f"Error scraping {url} with Newspaper3k: {e}") return "" def rephrase_query(chat_history, query, temperature=0.2): system_prompt = f""" You are a highly intelligent and context-aware conversational assistant. Your tasks are as follows: 1. Determine if the new query is a continuation of the previous conversation or an entirely new topic. 2. For both continuations and new topics: a. **Entity Identification and Quotation**: - Analyze the user's query to identify the main entities (e.g., organizations, brands, products, locations). - For each identified entity, enclose ONLY the entity itself in double quotes within the query. - If no identifiable entities are found, proceed without adding quotes. b. **Query Preservation**: - Maintain the entire original query, including any parts after commas or other punctuation. - Do not remove or truncate any part of the original query. 3. If it's a continuation: - Incorporate relevant information from the context to make the query more specific and contextual. - Ensure that entities from the previous context are properly quoted if they appear in the rephrased query. 4. For both continuations and new topics: - Append "after: {current_year}" to the end of the rephrased query. - Ensure there is a space before "after:" for proper formatting. - Do not use quotes or the "+" operator when adding the year. 5. **Output**: - Return ONLY the rephrased query, ensuring it is concise, clear, and contextually accurate. - Do not include any additional commentary or explanation. ### Example Scenarios **Scenario 1: New Topic** - **User Query**: "What is the latest news on Golomt Bank?" - **Rephrased Query**: "What is the latest news on \"Golomt Bank\" after: {current_year}" **Scenario 2: Continuation** - **Previous Query**: "What is the latest news on Golomt Bank?" - **User Query**: "How did the Bank perform in Q2 2024?" - **Rephrased Query**: "How did \"Golomt Bank\" perform in Q2 2024 after: {current_year}" **Scenario 3: Query with Multiple Entities and Comma** - **User Query**: "What is the latest news about Prospect Capital, did the rating change?" - **Rephrased Query**: "What is the latest news about \"Prospect Capital\", did the rating change after: {current_year}" **Scenario 4: Query Without Recognizable Entities** - **User Query**: "How does photosynthesis work?" - **Rephrased Query**: "How does photosynthesis work? after: {current_year}" """ 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, similarity_threshold=0.95, max_results=5): 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 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] # Combine documents and cosine scores scored_documents = list(zip(documents, cosine_scores)) # Step 3: Sort documents by cosine similarity score scored_documents.sort(key=lambda x: x[1], reverse=True) # Step 4: Filter out similar documents filtered_docs = [] for doc, score in scored_documents: if score < 0.5: # If similarity to query is too low, skip continue # Check similarity with already selected documents is_similar = False for selected_doc in filtered_docs: similarity = util.pytorch_cos_sim( similarity_model.encode(doc['summary'], convert_to_tensor=True), similarity_model.encode(selected_doc['summary'], convert_to_tensor=True) ) if similarity > similarity_threshold: is_similar = True break if not is_similar: filtered_docs.append(doc) if len(filtered_docs) >= max_results: break logger.info(f"Reranked and filtered to {len(filtered_docs)} unique documents.") return filtered_docs except Exception as e: logger.error(f"Error during reranking documents: {e}") return documents[:max_results] # Fallback to first max_results 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 specializing in financial news analysis. Your task is to assess the relevance of a given document to a user's query and provide a detailed summary if it's relevant.""" user_prompt = f""" Query: {query} Document Title: {document['title']} Document Content: {document['content'][:1000]} # Limit to first 1000 characters for efficiency Instructions: 1. Assess if the document is relevant to the QUERY made by the user. 2. If relevant, provide a detailed summary that captures the unique aspects of this particular news item. Include: - Key facts and figures - Dates of events or announcements - Names of important entities mentioned - Any financial metrics or changes reported - The potential impact or significance of the news 3. If not relevant, simply state "Not relevant". Your response should be in the following format: Relevant: [Yes/No] Summary: [Your detailed summary if relevant, or "Not relevant" if not] Remember to focus on financial aspects and implications in your assessment and summary. Aim to make the summary distinctive, highlighting what makes this particular news item unique compared to similar news. """ messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ] try: response = llm_client.chat_completion( messages=messages, max_tokens=300, # Increased to allow for more detailed summaries temperature=temperature, top_p=0.9, frequency_penalty=1.4 ) 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, timeout=5, use_pydf2=True): try: logger.info(f"Scraping full content from: {url}") # Check if the URL ends with .pdf if url.lower().endswith('.pdf'): if use_pydf2: return scrape_pdf_content(url, max_chars, timeout) else: logger.info(f"Skipping PDF document: {url}") return None # Use Newspaper3k for non-PDF content content = scrape_with_newspaper(url) # Limit the content to max_chars return content[:max_chars] if content else "" except requests.Timeout: logger.error(f"Timeout error while scraping full content from {url}") return "" except Exception as e: logger.error(f"Error scraping full content from {url}: {e}") return "" def llm_summarize(json_input, model, 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 based on provided context (the context consists of search results containing a brief description of the content of that page). 3. You must use this context to answer the user's query in the best way possible. Use an unbiased and journalistic tone in your response. Do not repeat the text. 4. Use an unbiased and professional tone in your response. 5. Do not repeat text verbatim from the input. 6. Provide the answer in the response itself. 7. You can use markdown to format your response. 8. Use bullet points to list information where appropriate. 9. Cite the answer using [number] notation along with the appropriate source URL embedded in the notation. 10. Place these citations at the end of the relevant sentences. 11. You can cite the same sentence multiple times if it's relevant to different parts of your answer. 12. Make sure the answer is not short and is informative. 13. 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: if model == "groq": response = groq_client.chat.completions.create( messages=messages, model="llama-3.1-70b-instant", max_tokens=5500, temperature=temperature, top_p=0.9, presence_penalty=1.2, stream=False ) return response.choices[0].message.content.strip() elif model == "mistral": response = mistral_client.chat.complete( model="Mistral-Nemo-Instruct-2407", messages=messages, max_tokens=5500, temperature=temperature, top_p=0.9, presence_penalty=1.2, stream=False ) return response.choices[0].message.content.strip() else: # huggingface response = client.chat_completion( messages=messages, max_tokens=10000, temperature=temperature, frequency_penalty=1.4, 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, model="huggingface", use_pydf2=True): 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." # Step 2: Perform search # 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"Processing content from: {url}") content = scrape_full_content(url, max_chars, timeout, use_pydf2) if content is None: # This means it's a PDF and use_pydf2 is False continue if not content: logger.warning(f"Failed to scrape content from {url}") continue scraped_content.append({ "title": title, "url": url, "content": content, "scraper": "pdf" if url.lower().endswith('.pdf') else "newspaper" }) 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." # Step 4: Rerank documents based on similarity to query reranked_docs = rerank_documents(rephrased_query, relevant_documents, similarity_threshold=0.95, max_results=num_results) 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'], 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), model, 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, max_chars, time_range, language, category, engines, safesearch, method, llm_temperature, model, use_pydf2): 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, max_chars=max_chars, time_range=time_range, language=language, category=category, engines=engines, safesearch=safesearch, method=method, llm_temperature=llm_temperature, model=model, use_pydf2=use_pydf2 ) yield response iface = gr.ChatInterface( chat_function, title="Web 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.Slider(500, 10000, value=1500, step=100, label="Max characters to retrieve"), gr.Dropdown(["", "day", "week", "month", "year"], value="", label="Time Range"), gr.Dropdown(["", "all", "en", "fr", "de", "es", "it", "nl", "pt", "pl", "ru", "zh"], value="", 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", "bing", "qwant"], 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"), gr.Dropdown(["huggingface", "groq", "mistral"], value="huggingface", label="LLM Model"), gr.Checkbox(label="Use PyPDF2 for PDF scraping", value=True), ], 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)