Broadcast lesson of Serpapi's improvements with Google Gemini-1.5-Flash for developed analytics
In this lesson, we show how you can integrate your Serpapi Google-1.5-flash model to create an advanced, ending and maintenance of the Google Colab booklet. By describing the Advancerpapi Python class, users receive access to enhanced search methods covering the standard web results, news headlines, and photos, while they added deeper results. Code provides specialization services in MarktechPost Tutorials, combined content in all categories such as Chatgpt, ChatGpt, and MLOs, and include active insights using carefully.
!pip install google-search-results langchain-community langchain-core google-generativeai -q
import os
import json
from serpapi import GoogleSearch
import google.generativeai as genai
from datetime import datetimeWe include Python packages needed for Serpapi search, Langchain services, and Gemini SDK. The following imports brings usual modules (OS, JNS, Datetime) to receive environmental configuration, JSON Management, and Timestamp, and Serpapi's Google Class performing API Calls.
SERPAPI_API_KEY = "Use Your API Key Here"
GEMINI_API_KEY = "Use Your API Key Here"
os.environ["SERPAPI_API_KEY"] = SERPAPI_API_KEY
genai.configure(api_key=GEMINI_API_KEY)
We offer the Serpapi cords and gemini apple keys, and set the Serpapi key as a flexibility (so the sender calls verifies automatically) and preparing for Gemini's client for the Gemini model.
class AdvancedSerpAPI:
def __init__(self, serpapi_key, gemini_key):
self.serpapi_key = serpapi_key
self.gemini_model = genai.GenerativeModel('gemini-1.5-flash')
def search_google(self, query, num_results=5, location="United States"):
"""Enhanced Google search with multiple parameters"""
params = {
"engine": "google",
"q": query,
"api_key": self.serpapi_key,
"num": num_results,
"location": location,
"hl": "en",
"gl": "us"
}
search = GoogleSearch(params)
results = search.get_dict()
return self.extract_search_results(results)
def search_news(self, query, days_back=7):
"""Search for recent news articles"""
params = {
"engine": "google_news",
"q": query,
"api_key": self.serpapi_key,
"gl": "us",
"hl": "en"
}
search = GoogleSearch(params)
results = search.get_dict()
return self.extract_news_results(results)
def search_images(self, query, num_images=10):
"""Search for images with metadata"""
params = {
"engine": "google_images",
"q": query,
"api_key": self.serpapi_key,
"num": num_images
}
search = GoogleSearch(params)
results = search.get_dict()
return self.extract_image_results(results)
def extract_search_results(self, results):
"""Extract and clean search results"""
cleaned_results = []
if 'organic_results' in results:
for result in results['organic_results']:
cleaned_results.append({
'title': result.get('title', ''),
'link': result.get('link', ''),
'snippet': result.get('snippet', ''),
'position': result.get('position', 0)
})
return cleaned_results
def extract_news_results(self, results):
"""Extract news articles with timestamps"""
news_results = []
if 'news_results' in results:
for article in results['news_results']:
news_results.append({
'title': article.get('title', ''),
'link': article.get('link', ''),
'snippet': article.get('snippet', ''),
'date': article.get('date', ''),
'source': article.get('source', '')
})
return news_results
def extract_image_results(self, results):
"""Extract image results with metadata"""
image_results = []
if 'images_results' in results:
for img in results['images_results']:
image_results.append({
'title': img.get('title', ''),
'original': img.get('original', ''),
'thumbnail': img.get('thumbnail', ''),
'source': img.get('source', '')
})
return image_results
def analyze_with_gemini(self, search_results, analysis_prompt):
"""Use Gemini Flash to analyze search results"""
results_text = json.dumps(search_results, indent=2)
full_prompt = f"""
{analysis_prompt}
Search Results Data:
{results_text}
Please provide a comprehensive analysis based on the search results.
"""
try:
response = self.gemini_model.generate_content(full_prompt)
return response.text
except Exception as e:
return f"Gemini analysis failed: {str(e)}"
def search_marktechpost_tutorials(self, topic="", num_results=10):
"""Search specifically for trending tutorials from Marktechpost"""
queries = [
f"site:marktechpost.com {topic} tutorial guide how-to 2024 2025",
f"site:marktechpost.com trending {topic} tutorial",
f"site:marktechpost.com top {topic} books frameworks"
]
all_results = []
for query in queries:
params = {
"engine": "google",
"q": query,
"api_key": self.serpapi_key,
"num": num_results // len(queries),
"hl": "en",
"gl": "us"
}
search = GoogleSearch(params)
results = search.get_dict()
extracted = self.extract_search_results(results)
all_results.extend(extracted)
unique_results = []
seen_links = set()
for result in all_results:
if result['link'] not in seen_links:
unique_results.append(result)
seen_links.add(result['link'])
return unique_results[:num_results]
def get_trending_marktechpost_content(self, categories=None):
"""Get trending content from Marktechpost across different categories"""
if categories is None:
categories = ["AI", "LLM", "Machine Learning", "Python", "Tutorial", "Framework"]
trending_content = {}
for category in categories:
print(f"🔍 Searching for trending {category} content...")
results = self.search_marktechpost_tutorials(category, num_results=5)
trending_contentAgentic AI = results
print(f"✅ Found {len(results)} {category} tutorials")
return trending_content
def smart_research(self, topic, research_depth="medium", focus_marktechpost=True):
"""Intelligent research combining multiple search types with Marktechpost focus"""
print(f"🔍 Starting smart research on: {topic}")
if focus_marktechpost:
marktechpost_results = self.search_marktechpost_tutorials(topic, num_results=8)
print(f"✅ Found {len(marktechpost_results)} Marktechpost tutorials")
web_results = self.search_google(f"{topic} tutorial guide", num_results=3)
print(f"✅ Found {len(web_results)} additional web results")
all_web_results = marktechpost_results + web_results
else:
all_web_results = self.search_google(f"{topic} overview facts", num_results=5)
print(f"✅ Found {len(all_web_results)} web results")
news_results = self.search_news(topic)
print(f"✅ Found {len(news_results)} news articles")
analysis_prompt = f"""
Analyze the search results about '{topic}' with focus on Marktechpost content and provide:
1. Key tutorials and guides available
2. Trending topics and frameworks
3. Learning resources and books mentioned
4. Recent developments and updates
5. Practical implementation guides
6. Recommended learning path
Focus on actionable insights and learning resources.
"""
all_results = {
"marktechpost_results": marktechpost_results if focus_marktechpost else [],
"web_results": all_web_results,
"news_results": news_results,
"search_topic": topic,
"timestamp": datetime.now().isoformat()
}
gemini_analysis = self.analyze_with_gemini(all_results, analysis_prompt)
return {
"topic": topic,
"marktechpost_tutorials": marktechpost_results if focus_marktechpost else [],
"web_results": all_web_results,
"news_results": news_results,
"ai_analysis": gemini_analysis,
"total_sources": len(all_web_results) + len(news_results)
}
This section, Adtureatraper, includes search methods based on Serpapi (Web, News, and photos) and Jonze data cleaning. It includes Gemini-1.5-flash model, with anaogne_gith_gimini, to produce a summary of AI for any search data collected. Additional services include the visualization of special MarktechPost option, “Get Trending” HousKal in all categories, and the combined combined tasks of teaching together, the web consequences, news and gemini analysis.
def demo_marktechpost_tutorials():
"""Demo specifically focused on Marktechpost tutorials"""
searcher = AdvancedSerpAPI(SERPAPI_API_KEY, GEMINI_API_KEY)
print("🚀 Marktechpost Trending Tutorials Finder")
print("=" * 50)
print("n📚 Demo 1: Trending Marktechpost Tutorials by Category")
trending_content = searcher.get_trending_marktechpost_content([
"LangChain", "ChatGPT", "Python", "AI", "MLOps"
])
for category, tutorials in trending_content.items():
print(f"n🔥 Trending {category} Tutorials:")
for i, tutorial in enumerate(tutorials[:3], 1):
print(f" {i}. {tutorial['title']}")
print(f" 📎 {tutorial['link']}")
if tutorial['snippet']:
print(f" 📝 {tutorial['snippet'][:100]}...")
print("n🎯 Demo 2: Deep Dive - LangChain Tutorials")
langchain_research = searcher.smart_research("LangChain", focus_marktechpost=True)
print(f"n📊 Research Summary:")
print(f"Topic: {langchain_research['topic']}")
print(f"Marktechpost Tutorials Found: {len(langchain_research['marktechpost_tutorials'])}")
print(f"Total Sources: {langchain_research['total_sources']}")
print(f"n🤖 AI Analysis Preview:")
print(langchain_research['ai_analysis'][:600] + "..." if len(langchain_research['ai_analysis']) > 600 else langchain_research['ai_analysis'])
print("n📰 Demo 3: Latest AI Trends from Marktechpost")
ai_trends = searcher.search_marktechpost_tutorials("AI trends 2024 2025", num_results=5)
print("Recent AI trend articles:")
for i, article in enumerate(ai_trends, 1):
print(f"{i}. {article['title']}")
print(f" 🔗 {article['link']}")
def demo_advanced_serpapi():
"""Comprehensive demo of SerpAPI capabilities"""
searcher = AdvancedSerpAPI(SERPAPI_API_KEY, GEMINI_API_KEY)
print("🚀 Advanced SerpAPI Tutorial with Gemini Flash")
print("=" * 50)
print("n📊 Demo 1: Smart Research on AI Technology")
research_results = searcher.smart_research("artificial intelligence 2024 trends")
print(f"n🔍 Research Summary:")
print(f"Topic: {research_results['topic']}")
print(f"Total Sources: {research_results['total_sources']}")
print(f"n🤖 AI Analysis Preview:")
print(research_results['ai_analysis'][:500] + "..." if len(research_results['ai_analysis']) > 500 else research_results['ai_analysis'])
print("n📰 Demo 2: Recent News Search")
tech_news = searcher.search_news("technology breakthrough", days_back=7)
print(f"Found {len(tech_news)} recent tech news articles:")
for i, article in enumerate(tech_news[:3], 1):
print(f"{i}. {article['title'][:80]}...")
print(f" Source: {article['source']} | Date: {article['date']}")
print("n🖼️ Demo 3: Image Search")
space_images = searcher.search_images("space exploration 2024", num_images=5)
print(f"Found {len(space_images)} space-related images:")
for i, img in enumerate(space_images[:3], 1):
print(f"{i}. {img['title'][:60]}...")
print(f" Source: {img['source']}")
The demo_markeThost_Tutorials () launches the Advasserperpapi class and prints the printer tutorials from the Langneinpost Marks (Langchain, Chatgpt, Python, Ai, Mrops). Then make a “deep dive” a good study on “Langchain,” indicating the calculation of tutorials and the first view of Gemino's Ai. Finally, returns and stabs five upper articles in the new appearance “AI 20244-2025” topics from MarktechPost.
Also, Demo_advanced_SERPAPE () Creates Advatterpapi Advatterpapi Democracy But Focus on Good Works: Artificial Intelligence 2024 Trends “and the AI Snippet summary. Then make the search news “the success of technology,” lists the first three articles with sources and days, and concludes in downloading the results of “Spying 2024”.
if __name__ == "__main__":
if SERPAPI_API_KEY == "your_serpapi_key_here" or GEMINI_API_KEY == "your_gemini_key_here":
print("⚠️ Please set your API keys before running the demo!")
print("1. Get SerpAPI key from:
print("2. Get Gemini API key from:
else:
print("🎯 Running Marktechpost-focused demo...")
demo_marktechpost_tutorials()
print("n" + "="*50)
print("🌟 Running general demo...")
demo_advanced_serpapi()
def compare_search_engines(query, engines=['google', 'bing', 'duckduckgo']):
"""Compare results across different search engines"""
results = {}
for engine in engines:
params = {
"engine": engine,
"q": query,
"api_key": SERPAPI_API_KEY
}
try:
search = GoogleSearch(params)
results[engine] = search.get_dict()
except Exception as e:
results[engine] = {"error": str(e)}
return results
def trending_searches(location="United States"):
"""Get trending searches"""
params = {
"engine": "google_trends_trending_now",
"api_key": SERPAPI_API_KEY,
"geo": location
}
search = GoogleSearch(params)
return search.get_dict()
print("✅ Advanced SerpAPI Tutorial with Marktechpost Focus loaded successfully!")
print("🔑 Remember to set your API keys before running demos")
print("📚 New Functions: search_marktechpost_tutorials, get_trending_marktechpost_content")
print("🎯 Marktechpost-specific features: LangChain, ChatGPT, Python, AI, MLOps tutorials")
print("n🚀 Quick Start Examples:")
print("searcher = AdvancedSerpAPI(SERPAPI_API_KEY, GEMINI_API_KEY)")
print("langchain_tutorials = searcher.search_marktechpost_tutorials('LangChain')")
print("trending_ai = searcher.get_trending_marktechpost_content(['AI', 'Python'])")
print("research = searcher.smart_research('ChatGPT', focus_marktechpost=True)")Finally, section “includes the main Python security guards confirmed your Vermapi and Gemini buttons. We assure us that a successful study, remembrance of the new API paths, and has shown additional new ways to collect the marked Marktechpost Tutorials and outstanding content.
In conclusion, in terms of this lesson, the more harmonious, relevant Web cows of the Serpapi's relief cows and the understanding of the content of the content, developers, and technical teams need to stay in the latest industrial tutorials.
See a letter of writing here. All credit for this study goes to research for this project. Also, feel free to follow it Sane and don't forget to join ours 95k + ml subreddit Then sign up for Our newspaper.
Asphazzaq is a Markteach Media Inc. According to a View Business and Developer, Asifi is committed to integrating a good social intelligence. His latest attempt is launched by the launch of the chemistrylife plan for an intelligence, MarktechPost, a devastating intimate practice of a machine learning and deep learning issues that are clearly and easily understood. The platform is adhering to more than two million moon visits, indicating its popularity between the audience.
