Create Amazon_Scrapper.py

Amazon_Scrapper.py

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+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()