import time
import random
import re
from datetime import datetime
import pandas as pd
import gradio as gr
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.chrome.service import Service

def scrape_amazon(search_term, pincode, num_pages=5):
    options = Options()
    options.add_argument('--headless')
    options.add_argument('--disable-blink-features=AutomationControlled')
    options.add_argument('--disable-gpu')
    options.add_argument('--no-sandbox')

    driver = webdriver.Chrome(service=Service(), options=options)

    all_products = []
    seen_titles = set()

    for page in range(1, num_pages + 1):
        url = f"https://www.amazon.in/s?k={search_term}&page={page}&crid=2M096C61O4MLT&sprefix={search_term},aps,283"
        driver.get(url)

        time.sleep(random.uniform(3, 5))  # Let page load

        # Scroll down to load dynamic content
        driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
        time.sleep(random.uniform(2, 4))

        products = driver.find_elements(By.XPATH, "//div[@data-component-type='s-search-result']")
        print(f"Scraping page {page}, found {len(products)} products...")

        for product in products:
            try:
                title_elem = product.find_element(By.XPATH, ".//h2//span")
                title = title_elem.text.strip()
            except:
                title = "No Title"

            if title in seen_titles:
                continue
            seen_titles.add(title)

            try:
                link_elem = product.find_element(By.XPATH, ".//a[@class='a-link-normal s-no-outline']")
                link = link_elem.get_attribute('href')
            except:
                link = "No Link"

            try:
                price_elem = product.find_element(By.XPATH, ".//span[@class='a-price-whole']")
                selling_price = price_elem.text.replace(',', '').strip()
            except:
                try:
                    price_elem = product.find_element(By.XPATH, ".//span[@class='a-offscreen']")
                    selling_price = price_elem.text.replace('₹', '').replace(',', '').strip()
                except:
                    selling_price = "No Price"

            try:
                mrp_elem = product.find_element(By.XPATH, ".//span[@class='a-price a-text-price' and @data-a-strike='true']//span[@class='a-offscreen']")
                raw_price = mrp_elem.get_attribute("textContent")
                mrp = raw_price.replace('₹', '').replace(',', '').strip()
            except:
                mrp = "No Price"

            try:
                if selling_price != "No Price" and mrp != "No Price":
                    discount_percent = round(100 * (float(mrp) - float(selling_price)) / float(mrp), 2)
                else:
                    discount_percent = 0.0
            except:
                discount_percent = 0.0

            try:
                grammage_match = re.search(r'(\d+\.?\d*\s?(ml|g|kg|l))', title.lower())
                grammage = grammage_match.group(0) if grammage_match else "No Grammage"
            except:
                grammage = "No Grammage"

            try:
                badge = product.find_element(By.XPATH, ".//div[contains(@class, 'a-color-secondary')]//span[contains(translate(text(), 'ABCDEFGHIJKLMNOPQRSTUVWXYZ', 'abcdefghijklmnopqrstuvwxyz'), 'deal') or contains(translate(text(), 'ABCDEFGHIJKLMNOPQRSTUVWXYZ', 'abcdefghijklmnopqrstuvwxyz'), 'coupon') or contains(translate(text(), 'ABCDEFGHIJKLMNOPQRSTUVWXYZ', 'abcdefghijklmnopqrstuvwxyz'), 'save') or contains(translate(text(), 'ABCDEFGHIJKLMNOPQRSTUVWXYZ', 'abcdefghijklmnopqrstuvwxyz'), 'limited')]")
                deal_tag = badge.text.strip()
            except:
                deal_tag = "No Deal"

            try:
                qty = product.find_element(By.XPATH, ".//span[contains(text(),'bought in past month')]").text.strip()
            except:
                qty = "No data"

            try:
                rating_elem = product.find_element(By.XPATH, ".//span[@class='a-icon-alt']")
                rating = rating_elem.get_attribute("textContent").split()[0]
            except:
                rating = "No Rating"

            try:
                reviews = product.find_element(By.XPATH, ".//a[contains(@aria-label,'ratings')]/span").text.strip()
            except:
                reviews = "No Reviews"

            try:
                ad_elem = product.find_element(By.XPATH, ".//span[contains(@class, 'a-color-secondary') and contains(text(), 'Sponsored')]")
                ad_status = "Ad"
            except:
                ad_status = "Not Ad"

            product_data = {
                'Title': title,
                'Grammage': grammage,
                'Selling Price': selling_price,
                'MRP': mrp,
                'Discount %': discount_percent,
                'Deal Tags': deal_tag,
                'Quantity Bought': qty,
                'Rating': rating,
                'Reviews': reviews,
                'Link': link,
                'Ad/Not Ad': ad_status,
                'Date': datetime.now().strftime("%d-%m-%Y"),
                'Search Term': search_term,
                'Pincode': pincode,
                'Category': search_term,
            }

            all_products.append(product_data)

        time.sleep(random.uniform(2, 4))  # Pause between pages

    driver.quit()

    df = pd.DataFrame(all_products)

    today_date = datetime.now().strftime("%Y-%m-%d")
    filename_base = f"{search_term}_scrape_{today_date}"

    excel_path = f"{filename_base}.xlsx"
    csv_path = f"{filename_base}.csv"
    json_path = f"{filename_base}.json"

    df.to_excel(excel_path, index=False)
    df.to_csv(csv_path, index=False)
    df.to_json(json_path, orient="records", lines=True)

    return excel_path, csv_path, json_path, df

### Now the Gradio interface

def gradio_interface(search_term, pincode, num_pages):
    excel_path, csv_path, json_path, df = scrape_amazon(search_term, pincode, int(num_pages))
    return df, excel_path, csv_path, json_path

# Gradio App
app = gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Textbox(label="Search Term"),
        gr.Textbox(label="Pincode"),
        gr.Slider(minimum=1, maximum=10, step=1, value=2, label="Number of Pages to Scrape")
    ],
    outputs=[
        gr.Dataframe(label="Scraped Data"),
        gr.File(label="Excel File"),
        gr.File(label="CSV File"),
        gr.File(label="JSON File"),
    ],
    title="🛒 Amazon.in Product Scraper",
    description="Enter a search term, pincode, and number of pages. Download the results as Excel/CSV/JSON.",
)

if __name__ == "__main__":
    app.launch(share=True)