import requests
from bs4 import BeautifulSoup
import gradio as gr
from huggingface_hub import InferenceClient
import random
import urllib.parse
from datetime import datetime, timedelta
import re
import os
# List of user agents to rotate through
_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",
]

API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_TOKEN')}"}

def query_llama(payload):
    """Send a query to the Llama model via Hugging Face API"""
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

def google_search(term, num_results=5, lang="en", timeout=5, safe="active", ssl_verify=None, days_back=90):
    """Perform a Google search and return results"""
    print(f"Searching for term: {term}")
    
    # Calculate the date range
    end_date = datetime.now()
    start_date = end_date - timedelta(days=days_back)
    
    # Format dates as strings
    start_date_str = start_date.strftime("%Y-%m-%d")
    end_date_str = end_date.strftime("%Y-%m-%d")
    
    # Add the date range to the search term
    search_term = f"{term} financial earnings report after:{start_date_str} before:{end_date_str}"
    
    escaped_term = urllib.parse.quote_plus(search_term)
    start = 0
    all_results = []

    with requests.Session() as session:
        while len(all_results) < num_results:
            try:
                # Choose a random user agent
                user_agent = random.choice(_useragent_list)
                headers = {'User-Agent': user_agent}

                resp = session.get(
                    url="https://www.google.com/search",
                    headers=headers,
                    params={
                        "q": search_term,
                        "num": num_results - start,
                        "hl": lang,
                        "start": start,
                        "safe": safe,
                    },
                    timeout=timeout,
                    verify=ssl_verify,
                )
                resp.raise_for_status()
            except requests.exceptions.RequestException as e:
                print(f"Error fetching 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 more results found.")
                break

            for result in result_block:
                if len(all_results) >= num_results:
                    break
                link = result.find("a", href=True)
                if link:
                    link = link["href"]
                    print(f"Found link: {link}")
                    try:
                        webpage = session.get(link, headers=headers, timeout=timeout)
                        webpage.raise_for_status()
                        visible_text = extract_text_from_webpage(webpage.text)
                        
                        all_results.append({"link": link, "text": visible_text})
                    except requests.exceptions.RequestException as e:
                        print(f"Error fetching or processing {link}: {e}")
                        all_results.append({"link": link, "text": None})
                else:
                    print("No link found in result.")
                    all_results.append({"link": None, "text": None})

            start += len(result_block)

    print(f"Total results fetched: {len(all_results)}")
    return all_results

def extract_text_from_webpage(html_content):
    """Extract visible text from HTML content"""
    soup = BeautifulSoup(html_content, 'html.parser')
    
    # Remove script and style elements
    for script in soup(["script", "style"]):
        script.decompose()
    
    # Get text
    text = soup.get_text()
    
    # Break into lines and remove leading and trailing space on each
    lines = (line.strip() for line in text.splitlines())
    
    # Break multi-headlines into a line each
    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
    
    # Drop blank lines
    text = '\n'.join(chunk for chunk in chunks if chunk)
    
    return text

def filter_relevant_content(text):
    """Filter out irrelevant content"""
    # List of keywords related to financial reports
    keywords = ['revenue', 'profit', 'earnings', 'financial', 'quarter', 'fiscal', 'growth', 'income', 'loss', 'dividend']
    
    # Split the text into sentences
    sentences = re.split(r'(?<=[.!?])\s+', text)
    
    # Filter sentences containing at least one keyword
    relevant_sentences = [sentence for sentence in sentences if any(keyword in sentence.lower() for keyword in keywords)]
    
    # Join the relevant sentences back into a single string
    filtered_text = ' '.join(relevant_sentences)
    
    return filtered_text

def summarize_financial_news(query):
    """Search for financial news, extract relevant content, and summarize"""
    search_results = google_search(query, num_results=3)
    
    all_filtered_text = ""
    for result in search_results:
        if result['text']:
            filtered_text = filter_relevant_content(result['text'])
            all_filtered_text += filtered_text + "\n\n"
    
    if not all_filtered_text:
        return "No relevant financial information found."
    
    prompt = f"""You are a financial analyst. Summarize the following text from a financial perspective:

{all_filtered_text}

Provide a detailed, coherent summary focusing on financial implications and analysis."""

    summary = query_llama({"inputs": prompt, "parameters": {"max_length": 500}})
    
    return summary[0]['generated_text']

# Gradio Interface
iface = gr.Interface(
    fn=summarize_financial_news,
    inputs=gr.Textbox(lines=2, placeholder="Enter a company name or financial topic..."),
    outputs="text",
    title="Financial News Summarizer",
    description="Enter a company name or financial topic to get a summary of recent financial news."
)

iface.launch()