Comprehensive coding tutorial with Google Gemini-1.5-Flash for advanced analytics for advanced analytics
In this tutorial, we demonstrate how to combine Serpapi’s Google search capabilities with Google’s Gemini-1.5-Flash model to create advanced, end-to-end research and analytics workflows in Google COLAB notebooks. By defining the AdvancedSerpapi Python class, users can access enhanced search methods covering general network results, news articles, and images, while also using Gemini to conduct in-depth analysis of these results. The code provides specialized utilities for MarkTechPost tutorials, spanning categories like Langchain, Chatgpt, and MLOPS, and then uses well-constructed tips to synthesize actionable insights.
!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 install the required Python packages for SERPAPI search, Langchain utility, and Google’s Gemini SDK. The subsequent import brought standard modules (OS, JSON, DATETIME) for environment configuration, JSON processing and timestamps, as well as Serpapi’s GoogleSearch class for making API calls and Genai to interact with the Gemini model.
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 assign a placeholder string to your SERPAPI and GEMINI API keys, then set the SERPAPI key to an environment variable (so Serpapi will automatically verify), and configure the Gemini client with its API keys so that you can call 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 AdvancedSerpapi encapsulates SERPAPI-based search methods (Web, News, and Images) and accessibility functions to clean the resulting JSON data. It also integrates the Gemini-1.5-Flash model through Analyze_with_gemini to generate an AI-driven summary of any collected search data. Other utilities include dedicated Marktechpost tutorial lookups, “get trends” routines across categories, and a combined “smart research” workflow that stitches together tutorials, web results, news, and Gemini analytics.
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']}")
Demo_markTechpost_tutorials() initializes the advanced Sepipipin Island class and prints the category list of trend tutorials (Langchain, Chatgpt, Python, AI, MLOPS) from Marktechpost. It then conducted an intelligent study of “Langchain” on “Deep Diving”, showing the count of tutorials and a preview of Gemini AI analysis. Finally, it retrieves and lists five recent “AI Trends 2024-2025” articles from Marktechpost.
Additionally, demo_advanced_serpapi() creates an advanced Seppipibinpi instance, but focuses on a wider workflow: it conducts intelligent research on “Artificial Intelligence 2024 Trends” and prints a topic summary and AI analysis summary. It then conducts a news search for “Technical Breakthroughs”, listing the first three articles with sources and dates, and ending by obtaining and displaying a few “Space Exploration 2024” image results.
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, this section includes a python “main” defender, first verifying your serpapi and gemini keys and prompting you to get them when they are still placeholders, otherwise run in order with the highlights and general demos of MarktechPost. It also defines two utility functions: Compare_search_engines, which query multiple search engines (Google, Bing, DuckDuckgo) via SERPAPI and returns their RAW JSON results or errors, and Trendend_Searches, which uses Google Trend endpoints to get the trend topics today. After these definitions, the script prints a short status message confirming that the tutorial is loading successfully, reminds you to set the API keys, and highlights the newly added method for getting the MarktechPost tutorial and trend content.
In short, by following this tutorial, users will have reusable modular Python classes, from performing keyword-driven searches to automatically summarize discoveries using Gemini-Power, simplifying web research and analysis. The combination of Serpapi’s reliable search endpoints and Gemini’s natural language understanding makes a seamless “Study to Inslights” workflow ideal for content creators, developers and technical teams who need to stay up to date with the latest tutorials and industry trends.
Check out the notebook here. All credits for this study are to the researchers on the project. Also, please stay tuned for us twitter And don’t forget to join us 95k+ ml reddit And subscribe Our newsletter.
Asif Razzaq is CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, ASIF is committed to harnessing the potential of artificial intelligence to achieve social benefits. His recent effort is to launch Marktechpost, an artificial intelligence media platform that has an in-depth coverage of machine learning and deep learning news that can sound both technically, both through technical voices and be understood by a wide audience. The platform has over 2 million views per month, demonstrating its popularity among its audience.
