A Comprehensive Coding Tutorial for Advanced SerpAPI Integration with Google Gemini-1.5-Flash for Advanced Analytics

!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 datetime

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)

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)
}

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’]}”)

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: https://serpapi.com”)
print(“2. Get Gemini API key from: https://makersuite.google.com”)
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)”)