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GOOGLE WEBSCRAPPING TRIAL.py

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+from selenium import webdriver
+from selenium.webdriver.common.by import By
+from selenium.webdriver.chrome.options import Options
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+from selenium.common.exceptions import TimeoutException
+from bs4 import BeautifulSoup
+from openpyxl import load_workbook
+import spacy
+from collections import Counter
+import re
+from datetime import datetime
+
+# Caminho do Excel
+excel_path = r"C:\Users\Licitações\Downloads\WEBSCRAPPING.xlsx"
+
+# Abre o Excel
+wb = load_workbook(excel_path)
+ws = wb["Planilha1"]
+
+# Configurações do Chrome
+chrome_options = Options()
+# chrome_options.add_argument("--headless")  # descomente se quiser rodar sem abrir janela
+driver = webdriver.Chrome(options=chrome_options)
+wait = WebDriverWait(driver, 10)
+
+# Carrega modelo spaCy
+nlp = spacy.load("en_core_web_sm")
+
+# Contadores de progresso
+total_links = 0
+links_modificados = 0
+links_sem_dados = 0
+
+
+def extract_from_soup(soup, driver):
+    """Extrai todas as informações da patente e retorna em um dicionário."""
+    result = {}
+
+    # Inventores
+    inventors = []
+    important_people = soup.select("dl.important-people.style-scope.patent-result")
+    if important_people:
+        dl = important_people[0]
+        dt_inventor = dl.find("dt", string=lambda x: x and "Inventor" in x)
+        if dt_inventor:
+            for dd in dt_inventor.find_all_next("dd"):
+                state = dd.find("state-modifier")
+                if state and state.has_attr("data-inventor"):
+                    inventors.append(state["data-inventor"])
+                else:
+                    break
+    result["inventors_count"] = len(inventors)
+
+    # Imagens
+    image_tags = soup.select("section#thumbnails img")
+    result["images_count"] = len(image_tags)
+
+    # Backward citations
+    backward = []
+    citations_section = soup.find("h3", id="patentCitations")
+    if citations_section:
+        table = citations_section.find_next("div", class_="responsive-table")
+        if table:
+            for m in table.find_all("state-modifier"):
+                data = m.get("data-result", "")
+                if "patent/" in data:
+                    backward.append(data.split("patent/")[-1].split("/")[0])
+    result["backward_count"] = len(backward)
+
+    # Forward citations
+    cited_by = []
+    cited_by_section = soup.find("h3", id="citedBy")
+    if cited_by_section:
+        table = cited_by_section.find_next("div", class_="responsive-table")
+        if table:
+            for m in table.find_all("state-modifier"):
+                data = m.get("data-result", "")
+                if "patent/" in data:
+                    cited_by.append(data.split("patent/")[-1].split("/")[0])
+    result["forward_count"] = len(cited_by)
+
+    # Título e substantivos
+    title_tag = soup.find("h1", class_="scroll-target style-scope patent-result")
+    title_text = title_tag.get_text(strip=True) if title_tag else ""
+    result["title_text"] = title_text
+    doc = nlp(title_text)
+    result["num_nouns"] = sum(1 for token in doc if token.pos_ == "NOUN")
+
+    # Priority apps
+    priority_apps = []
+    priority_section = soup.find("h3", id="appsClaimingPriority")
+    if priority_section:
+        table = priority_section.find_next("div", class_="responsive-table style-scope patent-result")
+        if table:
+            for row_div in table.find_all("div", class_="tr style-scope patent-result"):
+                if row_div.find("span", class_="th style-scope patent-result"):
+                    continue
+                state = row_div.find("state-modifier")
+                if state and state.has_attr("data-result"):
+                    priority_apps.append(state["data-result"])
+    result["priority_count"] = len(priority_apps)
+
+    # Similaridade título/descrição
+    try:
+        desc_element = wait.until(
+            EC.presence_of_element_located((By.XPATH, "//patent-text/div/section"))
+        )
+        description_text = desc_element.text.strip()
+    except TimeoutException:
+        description_text = ""
+
+    doc_desc = nlp(description_text)
+    desc_words = [t.lemma_.lower() for t in doc_desc if t.is_alpha and not t.is_stop]
+    word_freq = Counter(desc_words)
+    top_10 = [w for w, _ in word_freq.most_common(10)]
+
+    doc_title = nlp(title_text)
+    title_words = [t.lemma_.lower() for t in doc_title if t.is_alpha and not t.is_stop]
+    result["similarity_score"] = sum(title_words.count(w) for w in top_10)
+
+    # NPL citations
+    num_npl = 0
+    npl_section = soup.find("h3", id="nplCitations")
+    if npl_section:
+        match = re.search(r"\((\d+)\)", npl_section.get_text(strip=True))
+        if match:
+            num_npl = int(match.group(1))
+        else:
+            table = npl_section.find_next("div", class_="responsive-table")
+            if table:
+                num_npl = len(table.find_all("div", class_="tr style-scope patent-result"))
+    result["npl_count"] = num_npl
+
+    # Número de claims
+    num_claims = 0
+    for c in soup.find_all("div", class_="flex style-scope patent-result"):
+        text = c.get_text(strip=True)
+        if "Claims" in text or "Reivindicações" in text:
+            span = c.find("span", class_="style-scope patent-result")
+            if span:
+                m = re.search(r"\((\d+)\)", span.get_text(strip=True))
+                if m:
+                    num_claims = int(m.group(1))
+                    break
+    result["num_claims"] = num_claims
+
+    # Data de aplicação
+    app_date_tag = soup.find("div", class_="filed style-scope application-timeline")
+    if app_date_tag:
+        app_date = app_date_tag.get_text(strip=True)
+        try:
+            app_date = datetime.strptime(app_date, "%Y-%m-%d").strftime("%d-%m-%Y")
+        except ValueError:
+            pass
+    else:
+        app_date = ""
+    result["app_date"] = app_date
+
+    return result
+
+
+# Loop principal
+for row in range(2, 102):
+    url_original = ws[f"A{row}"].value
+    if not url_original:
+        continue
+
+    total_links += 1
+    print(f"\n[Linha {row}] Acessando: {url_original}")
+    urls_to_try = [url_original]
+
+    # cria variação /BRPI se aplicável
+    if "/patent/BR" in url_original and "BRPI" not in url_original:
+        urls_to_try.append(url_original.replace("/patent/BR", "/patent/BRPI", 1))
+
+    final_results = None
+    link_used = "Link original"
+
+    for idx, url in enumerate(urls_to_try, start=1):
+        try:
+            driver.get(url)
+            wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, "body")))
+            soup = BeautifulSoup(driver.page_source, "html.parser")
+            results = extract_from_soup(soup, driver)
+
+            found_any = any([
+                results["inventors_count"],
+                results["images_count"],
+                results["backward_count"],
+                results["forward_count"],
+                results["num_nouns"],
+                results["priority_count"],
+                results["similarity_score"],
+                results["npl_count"],
+                results["num_claims"],
+                bool(results["app_date"]),
+                bool(results["title_text"]),
+            ])
+
+            if found_any:
+                final_results = results
+                if url != url_original:
+                    ws[f"A{row}"] = url
+                    link_used = "Link modificado"
+                    links_modificados += 1
+                print(f"  ✅ Dados encontrados (tentativa {idx})")
+                break
+            else:
+                print(f"  ⚠️ Nenhum dado na tentativa {idx}")
+        except TimeoutException:
+            print(f"  ⏳ Timeout ao acessar {url}")
+        except Exception as e:
+            print(f"  ❌ Erro inesperado: {e}")
+
+    # Grava resultados
+    if final_results:
+        ws[f"B{row}"] = final_results["inventors_count"]
+        ws[f"C{row}"] = final_results["images_count"]
+        ws[f"D{row}"] = final_results["backward_count"]
+        ws[f"E{row}"] = final_results["forward_count"]
+        ws[f"F{row}"] = final_results["num_nouns"]
+        ws[f"G{row}"] = final_results["priority_count"]
+        ws[f"H{row}"] = final_results["similarity_score"]
+        ws[f"I{row}"] = final_results["npl_count"]
+        ws[f"J{row}"] = final_results["num_claims"]
+        ws[f"K{row}"] = final_results["app_date"]
+        ws[f"L{row}"] = link_used
+    else:
+        ws[f"L{row}"] = "Sem dados"
+        links_sem_dados += 1
+
+# Finaliza
+driver.quit()
+wb.save(excel_path)
+
+# 📊 Resumo final
+print("\n====================== RESUMO ======================")
+print(f"🔍 Total de patentes analisadas: {total_links}")
+print(f"🧩 Links modificados:            {links_modificados}")
+print(f"⚠️  Patentes sem dados:           {links_sem_dados}")
+print("✅ Processo concluído. Dados salvos no Excel.")
+print("====================================================")

INVENTORES.txt

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+PINTO JOSE RICARDO MEDEIROS
+GALVAO CELIA MARIA ARAUJO
+AUDI RICARDO