!pip install -q langchain-community xmltodict pandas matplotlib seaborn wordcloud google-generativeai langchain-google-genaiimport osimport reimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsfrom datetime import datetime, timedeltafrom collections import Counterfrom wordcloud import WordCloudimport warningswarnings.filterwarnings('ignore')from langchain_community.tools.pubmed.tool import PubmedQueryRunfrom langchain_google_genai import ChatGoogleGenerativeAIfrom langchain.agents import initialize_agent, Toolfrom langchain.agents import AgentType

class AdvancedPubMedResearcher:    """Advanced PubMed research assistant with analysis capabilities"""       def __init__(self, gemini_api_key=None):        """Initialize the researcher with optional Gemini integration"""        self.pubmed_tool = PubmedQueryRun()        self.research_cache = {}               if gemini_api_key:            os.environ["GOOGLE_API_KEY"] = gemini_api_key            self.llm = ChatGoogleGenerativeAI(                model="gemini-1.5-flash",                temperature=0,                convert_system_message_to_human=True            )            self.agent = self._create_agent()        else:            self.llm = None            self.agent = None       def _create_agent(self):        """Create LangChain agent with PubMed tool"""        tools = [            Tool(                name="PubMed Search",                func=self.pubmed_tool.invoke,                description="Search PubMed for biomedical literature. Use specific terms."            )        ]               return initialize_agent(            tools,            self.llm,            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,            verbose=True        )       def search_papers(self, query, max_results=5):        """Search PubMed and parse results"""        print(f" Searching PubMed for: '{query}'")               try:            results = self.pubmed_tool.invoke(query)            papers = self._parse_pubmed_results(results)                       self.research_cache[query] = {                'papers': papers,                'timestamp': datetime.now(),                'query': query            }                       print(f" Found {len(papers)} papers")            return papers                   except Exception as e:            print(f" Error searching PubMed: {str(e)}")            return []       def _parse_pubmed_results(self, results):        """Parse PubMed search results into structured data"""        papers = []               publications = results.split('\n\nPublished: ')[1:]               for pub in publications:            try:                lines = pub.strip().split('\n')                               pub_date = lines[0] if lines else "Unknown"                               title_line = next((line for line in lines if line.startswith('Title: ')), '')                title = title_line.replace('Title: ', '') if title_line else "Unknown Title"                               summary_start = None                for i, line in enumerate(lines):                    if 'Summary::' in line:                        summary_start = i + 1                        break                               summary = ""                if summary_start:                    summary = ' '.join(lines[summary_start:])                               papers.append({                    'date': pub_date,                    'title': title,                    'summary': summary,                    'word_count': len(summary.split()) if summary else 0                })                           except Exception as e:                print(f" Error parsing paper: {str(e)}")                continue               return papers       def analyze_research_trends(self, queries):        """Analyze trends across multiple research topics"""        print(" Analyzing research trends...")               all_papers = []        topic_counts = {}               for query in queries:            papers = self.search_papers(query, max_results=3)            topic_counts[query] = len(papers)                       for paper in papers:                paper['topic'] = query                all_papers.append(paper)               df = pd.DataFrame(all_papers)               if df.empty:            print(" No papers found for analysis")            return None               self._create_visualizations(df, topic_counts)               return df       def _create_visualizations(self, df, topic_counts):        """Create research trend visualizations"""        plt.style.use('seaborn-v0_8')        fig, axes = plt.subplots(2, 2, figsize=(15, 12))        fig.suptitle('PubMed Research Analysis Dashboard', fontsize=16, fontweight='bold')               topics = list(topic_counts.keys())        counts = list(topic_counts.values())               axes[0,0].bar(range(len(topics)), counts, color='skyblue', alpha=0.7)        axes[0,0].set_xlabel('Research Topics')        axes[0,0].set_ylabel('Number of Papers')        axes[0,0].set_title('Papers Found by Topic')        axes[0,0].set_xticks(range(len(topics)))        axes[0,0].set_xticklabels([t[:20]+'...' if len(t)>20 else t for t in topics], rotation=45, ha='right')               if 'word_count' in df.columns and not df['word_count'].empty:            axes[0,1].hist(df['word_count'], bins=10, color='lightcoral', alpha=0.7)            axes[0,1].set_xlabel('Abstract Word Count')            axes[0,1].set_ylabel('Frequency')            axes[0,1].set_title('Distribution of Abstract Lengths')               try:            dates = pd.to_datetime(df['date'], errors='coerce')            valid_dates = dates.dropna()            if not valid_dates.empty:                axes[1,0].hist(valid_dates, bins=10, color='lightgreen', alpha=0.7)                axes[1,0].set_xlabel('Publication Date')                axes[1,0].set_ylabel('Number of Papers')                axes[1,0].set_title('Publication Timeline')                plt.setp(axes[1,0].xaxis.get_majorticklabels(), rotation=45)        except:            axes[1,0].text(0.5, 0.5, 'Date parsing unavailable', ha='center', va='center', transform=axes[1,0].transAxes)               all_titles = ' '.join(df['title'].fillna('').astype(str))        if all_titles.strip():            clean_titles = re.sub(r'[^a-zA-Z\s]', '', all_titles.lower())                       try:                wordcloud = WordCloud(width=400, height=300, background_color='white',                                    max_words=50, colormap='viridis').generate(clean_titles)                axes[1,1].imshow(wordcloud, interpolation='bilinear')                axes[1,1].axis('off')                axes[1,1].set_title('Common Words in Titles')            except:                axes[1,1].text(0.5, 0.5, 'Word cloud unavailable', ha='center', va='center', transform=axes[1,1].transAxes)               plt.tight_layout()        plt.show()       def comparative_analysis(self, topic1, topic2):        """Compare two research topics"""        print(f" Comparing '{topic1}' vs '{topic2}'")               papers1 = self.search_papers(topic1)        papers2 = self.search_papers(topic2)               avg_length1 = sum(p['word_count'] for p in papers1) / len(papers1) if papers1 else 0        avg_length2 = sum(p['word_count'] for p in papers2) / len(papers2) if papers2 else 0               print("\n Comparison Results:")        print(f"Topic 1 ({topic1}):")        print(f"  - Papers found: {len(papers1)}")        print(f"  - Avg abstract length: {avg_length1:.1f} words")               print(f"\nTopic 2 ({topic2}):")        print(f"  - Papers found: {len(papers2)}")        print(f"  - Avg abstract length: {avg_length2:.1f} words")               return papers1, papers2       def intelligent_query(self, question):        """Use AI agent to answer research questions (requires Gemini API)"""        if not self.agent:            print(" AI agent not available. Please provide Gemini API key.")            print(" Get free API key at: https://makersuite.google.com/app/apikey")            return None               print(f" Processing intelligent query with Gemini: '{question}'")        try:            response = self.agent.run(question)            return response        except Exception as e:            print(f" Error with AI query: {str(e)}")            return None

def main():    """Main tutorial demonstration"""    print(" Advanced PubMed Research Assistant Tutorial")    print("=" * 50)       # Initialize researcher    # Uncomment next line and add your free Gemini API key for AI features    # Get your free API key at: https://makersuite.google.com/app/apikey    # researcher = AdvancedPubMedResearcher(gemini_api_key="your-gemini-api-key")    researcher = AdvancedPubMedResearcher()       print("\n1⃣ Basic PubMed Search")    papers = researcher.search_papers("CRISPR gene editing", max_results=3)       if papers:        print(f"\nFirst paper preview:")        print(f"Title: {papers[0]['title']}")        print(f"Date: {papers[0]['date']}")        print(f"Summary preview: {papers[0]['summary'][:200]}...")    print("\n\n2⃣ Research Trends Analysis")    research_topics = [        "machine learning healthcare",        "CRISPR gene editing",        "COVID-19 vaccine"    ]       df = researcher.analyze_research_trends(research_topics)       if df is not None:        print(f"\nDataFrame shape: {df.shape}")        print("\nSample data:")        print(df[['topic', 'title', 'word_count']].head())    print("\n\n3⃣ Comparative Analysis")    papers1, papers2 = researcher.comparative_analysis(        "artificial intelligence diagnosis",        "traditional diagnostic methods"    )       print("\n\n4⃣ Advanced Features")    print("Cache contents:", list(researcher.research_cache.keys()))       if researcher.research_cache:        latest_query = list(researcher.research_cache.keys())[-1]        cached_data = researcher.research_cache[latest_query]        print(f"Latest cached query: '{latest_query}'")        print(f"Cached papers count: {len(cached_data['papers'])}")       print("\n Tutorial complete!")    print("\nNext steps:")    print("- Add your FREE Gemini API key for AI-powered analysis")    print("  Get it at: https://makersuite.google.com/app/apikey")    print("- Customize queries for your research domain")    print("- Export results to CSV with: df.to_csv('research_results.csv')")       print("\n Bonus: To test AI features, run:")    print("researcher = AdvancedPubMedResearcher(gemini_api_key='your-key')")    print("response = researcher.intelligent_query('What are the latest breakthrough in cancer treatment?')")    print("print(response)")if __name__ == "__main__":    main()