chat/single_paper_summarizer.py

# chat/single_paper_summarizer.py

from typing import Dict, Any, Tuple, List, Optional
from llm.llm_provider import GrokLLM
import re
import json
import statistics
from datetime import datetime


class SinglePaperSummarizer:
    """Enhanced clinical paper summarizer with user context awareness"""

    def __init__(self, model: str = "gpt-oss-120b"):
        self.llm = GrokLLM(model=model)

    def summarize_paper(self,

                        paper: Dict[str, Any],

                        query: str = None,

                        user_context: str = "general") -> Dict[str, Any]:
        """

        Generate comprehensive clinical summary of a single paper



        Args:

            paper: Dictionary with paper metadata

            query: Optional user query about the paper

            user_context: User context (clinician, researcher, student, administrator, general)



        Returns:

            Dict with enhanced clinical summary and structured analysis

        """

        # Extract paper details
        title = paper.get('title', 'Unknown Title')
        abstract = paper.get('abstract', '')
        authors = paper.get('authors', [])
        publication_date = paper.get('publication_date', '')
        source = paper.get('source', 'Unknown Source')
        citations = paper.get('citations', 0)
        paper_id = paper.get('id', '')

        print(f"📄 Summarizing paper for {user_context}: {title[:50]}...")

        # Format authors
        if authors and isinstance(authors, list):
            if len(authors) <= 3:
                author_str = ', '.join(authors)
            else:
                author_str = f"{authors[0]} et al. ({len(authors)} authors)"
        else:
            author_str = "Unknown"

        # Create enhanced clinical prompt
        prompt = self._create_clinical_summarization_prompt(
            title, abstract, author_str, publication_date,
            source, citations, query, user_context
        )

        # Generate enhanced summary
        system_msg = self._get_clinical_system_message(user_context)

        try:
            enhanced_summary = self.llm.generate(
                prompt,
                system_message=system_msg,
                max_tokens=2000
            )

            # Extract key metrics and sections
            key_metrics = self._extract_paper_metrics(abstract)
            clinical_relevance = self._assess_paper_clinical_relevance(
                abstract, title, user_context
            )
            structured_analysis = self._extract_enhanced_sections(enhanced_summary)

            # Generate quick clinical bottom line
            quick_summary = self._generate_quick_clinical_summary(
                title, abstract, user_context
            )

            # Calculate clinical confidence
            confidence = self._calculate_clinical_confidence(
                paper, key_metrics, clinical_relevance
            )

            return {
                "success": True,
                "paper_id": paper_id,
                "paper_title": title,
                "authors": authors,
                "publication_date": publication_date,
                "source": source,
                "citations": citations,
                "enhanced_summary": enhanced_summary,
                "quick_summary": quick_summary,
                "structured_analysis": structured_analysis,
                "key_metrics": key_metrics,
                "clinical_relevance": clinical_relevance,
                "user_context": user_context,
                "query_context": query,
                "summary_type": "single_paper_enhanced",
                "confidence": confidence,
                "summary_length": len(enhanced_summary),
                "analysis_timestamp": datetime.now().isoformat()
            }

        except Exception as e:
            print(f"❌ Enhanced summarization failed: {e}")
            # Fallback to basic summary
            return self._generate_fallback_summary(
                paper, query, user_context, str(e)
            )

    def _create_clinical_summarization_prompt(self, title, abstract, authors, date,

                                              source, citations, query, user_context):
        """Create specialized clinical summarization prompt"""

        base_prompt = f"""Create a comprehensive clinical analysis of this research paper for a {user_context}:



**PAPER METADATA:**

- Title: {title}

- Authors: {authors}

- Publication Date: {date}

- Source: {source}

- Citations: {citations if citations else 'Not available'}



**ABSTRACT:**

{abstract}



**USER CONTEXT:** {user_context}"""

        if query:
            base_prompt += f"""

**SPECIFIC QUESTION:** {query}



Please focus your analysis on answering this specific clinical question about the paper."""
        else:
            base_prompt += """

**ANALYSIS REQUESTED:**

Provide a comprehensive clinical analysis of this paper tailored to a {user_context}."""

        base_prompt += f"""



**STRUCTURE YOUR ANALYSIS FOR A {user_context.upper()}:**



## 🎯 **Clinical Bottom Line** (1-2 sentences)

*What is the single most important clinical takeaway?*



## 📊 **Study Design & Methodology**

- Study type (RCT, cohort, case-control, etc.)

- Sample size and population

- Key interventions/techniques

- Follow-up duration (if applicable)

- Statistical methods



## 📈 **Key Findings with Clinical Data**

- Primary outcomes with effect sizes

- Secondary outcomes

- Statistical significance (p-values, CIs)

- Subgroup analyses

- Adverse events/safety data



## 🏥 **Clinical Implications for {user_context}**

*Tailor this section specifically for a {user_context}:*

- How does this change practice/decision-making?

- Which patients benefit most?

- When should this be implemented?

- What are the immediate applications?



## ⚠️ **Limitations & Cautions**

- Study design limitations

- Population generalizability

- Potential biases

- Conflicts of interest

- Funding sources



## 🔬 **Research Implications**

- Mechanism/biology insights

- Future research directions

- Unanswered questions

- Replication needs



## 💡 **Clinical Recommendations**

*Actionable recommendations for {user_context}:*

1. 

2. 

3. 



**Include specific numbers, effect sizes, and confidence intervals from the abstract.**

**Use clinical terminology appropriate for a {user_context}.**

**Highlight what's novel and what confirms existing knowledge.**"""

        return base_prompt

    def _get_clinical_system_message(self, user_context: str) -> str:
        """Get system message tailored to user context"""

        system_messages = {
            "clinician": """You are an expert clinical researcher analyzing papers for practicing physicians.

            Focus on:

            1. Clinical applicability and patient impact

            2. Evidence strength for decision-making

            3. Practical implementation in clinical workflow

            4. Risk-benefit analysis for patients

            5. Immediate vs. future clinical implications

            Be evidence-based, practical, and action-oriented.""",

            "researcher": """You are a senior research scientist analyzing papers for academic researchers.

            Focus on:

            1. Methodological rigor and innovation

            2. Statistical analysis quality

            3. Biological/mechanistic insights

            4. Contribution to field knowledge

            5. Research gaps and future directions

            Be critical, detailed, and forward-looking.""",

            "student": """You are a medical educator explaining papers to students.

            Focus on:

            1. Clear, simplified explanations

            2. Key learning points

            3. Clinical relevance context

            4. Foundational concepts

            5. Study design basics

            Be educational, structured, and encouraging.""",

            "administrator": """You are a healthcare administrator analyzing papers for system leaders.

            Focus on:

            1. Cost-effectiveness and ROI

            2. Implementation feasibility

            3. Workflow integration

            4. Resource requirements

            5. Regulatory/compliance aspects

            Be practical, data-driven, and strategic."""
        }

        return system_messages.get(
            user_context,
            """You are a medical research expert analyzing papers.

            Provide comprehensive, evidence-based analyses that are:

            1. Accurate and precise

            2. Well-structured and clear

            3. Clinically relevant

            4. Transparent about evidence quality

            5. Actionable for different stakeholders"""
        )

    def _extract_enhanced_sections(self, summary: str) -> Dict[str, str]:
        """Extract structured sections from enhanced summary"""
        sections = {
            "clinical_bottom_line": "",
            "study_design_methodology": "",
            "key_findings": "",
            "clinical_implications": "",
            "limitations_cautions": "",
            "research_implications": "",
            "clinical_recommendations": ""
        }

        # Try to extract sections by headings
        section_patterns = {
            "clinical_bottom_line": [
                r"clinical bottom line", r"takeaway", r"key message",
                r"🎯", r"bottom line"
            ],
            "study_design_methodology": [
                r"study design", r"methodology", r"methods",
                r"experimental design", r"📊"
            ],
            "key_findings": [
                r"key findings", r"results", r"findings",
                r"outcomes", r"📈"
            ],
            "clinical_implications": [
                r"clinical implications", r"clinical relevance",
                r"practice implications", r"🏥"
            ],
            "limitations_cautions": [
                r"limitations", r"cautions", r"weaknesses",
                r"biases", r"⚠️"
            ],
            "research_implications": [
                r"research implications", r"future research",
                r"research directions", r"🔬"
            ],
            "clinical_recommendations": [
                r"clinical recommendations", r"recommendations",
                r"action items", r"💡"
            ]
        }

        lines = summary.split('\n')
        current_section = None
        section_content = []

        for line in lines:
            line_lower = line.lower().strip()

            # Check for new section
            for section, patterns in section_patterns.items():
                if any(re.search(pattern, line_lower) for pattern in patterns):
                    # Save previous section
                    if current_section and section_content:
                        sections[current_section] = '\n'.join(section_content)
                    # Start new section
                    current_section = section
                    section_content = []
                    break
            else:
                # Add content to current section if not empty
                if current_section and line.strip():
                    section_content.append(line)

        # Save last section
        if current_section and section_content:
            sections[current_section] = '\n'.join(section_content)

        return sections

    def _extract_paper_metrics(self, abstract: str) -> Dict[str, Any]:
        """Extract key clinical metrics from paper abstract"""
        metrics = {
            "sample_size": None,
            "statistical_significance": [],
            "effect_sizes": [],
            "confidence_intervals": [],
            "adverse_events": [],
            "follow_up": None
        }

        abstract_lower = abstract.lower()

        # Extract sample size
        sample_patterns = [
            r'n\s*=\s*(\d+,?\d*)',
            r'sample of (\d+,?\d*)',
            r'(\d+,?\d*)\s*participants',
            r'(\d+,?\d*)\s*patients',
            r'(\d+,?\d*)\s*subjects'
        ]

        for pattern in sample_patterns:
            match = re.search(pattern, abstract_lower)
            if match:
                metrics["sample_size"] = match.group(1).replace(',', '')
                break

        # Extract p-values
        p_value_matches = re.findall(
            r'p\s*[<≤=]\s*0?\.\d+(?:e[+-]?\d+)?',
            abstract_lower,
            re.IGNORECASE
        )
        metrics["statistical_significance"] = p_value_matches[:5]

        # Extract effect sizes
        effect_patterns = [
            (r'HR\s*[=]\s*[\d\.]+', "Hazard Ratio"),
            (r'OR\s*[=]\s*[\d\.]+', "Odds Ratio"),
            (r'RR\s*[=]\s*[\d\.]+', "Relative Risk"),
            (r'ARR\s*[=]\s*[\d\.]+%?', "Absolute Risk Reduction"),
            (r'NNT\s*[=]\s*[\d\.]+', "Number Needed to Treat")
        ]

        for pattern, label in effect_patterns:
            matches = re.findall(pattern, abstract_lower, re.IGNORECASE)
            for match in matches:
                metrics["effect_sizes"].append(f"{label}: {match}")

        # Extract confidence intervals
        ci_matches = re.findall(
            r'\d+\.?\d*%\s*CI\s*[\[\(].*?[\]\)]',
            abstract_lower,
            re.IGNORECASE
        )
        metrics["confidence_intervals"] = ci_matches[:3]

        # Extract follow-up duration
        follow_up_matches = re.findall(
            r'(\d+(?:\.\d+)?)\s*(?:year|month|week|day)s?\s*(?:follow-up|follow up|FU)',
            abstract_lower
        )
        if follow_up_matches:
            metrics["follow_up"] = follow_up_matches[0]

        # Extract adverse events
        ae_keywords = ['adverse event', 'side effect', 'complication', 'toxicity', 'safety']
        for keyword in ae_keywords:
            if keyword in abstract_lower:
                metrics["adverse_events"].append(keyword)

        # Count metric richness
        metrics["metric_richness"] = sum(
            1 for key in ['sample_size', 'follow_up']
            if metrics[key] is not None
        ) + sum(len(metrics[key]) for key in [
            'statistical_significance',
            'effect_sizes',
            'confidence_intervals',
            'adverse_events'
        ])

        return metrics

    def _assess_paper_clinical_relevance(self, abstract: str, title: str,

                                         user_context: str) -> Dict[str, Any]:
        """Assess clinical relevance of paper for specific user context"""

        # Check for clinical endpoints
        clinical_keywords = {
            "high_impact": ['survival', 'mortality', 'cure', 'prevention', 'morbidity'],
            "medium_impact": ['symptom', 'recovery', 'function', 'quality of life', 'qol'],
            "low_impact": ['feasibility', 'pilot', 'mechanism', 'proof of concept'],
            "clinical_study": ['trial', 'cohort', 'case-control', 'observational', 'randomized']
        }

        abstract_lower = abstract.lower()
        title_lower = title.lower()

        # Calculate relevance score
        score = 0

        # Impact level
        for keyword in clinical_keywords["high_impact"]:
            if keyword in abstract_lower or keyword in title_lower:
                score += 3
        for keyword in clinical_keywords["medium_impact"]:
            if keyword in abstract_lower or keyword in title_lower:
                score += 2
        for keyword in clinical_keywords["low_impact"]:
            if keyword in abstract_lower or keyword in title_lower:
                score += 1

        # Study design
        for keyword in clinical_keywords["clinical_study"]:
            if keyword in abstract_lower:
                score += 2

        # Adjust for user context
        context_multipliers = {
            "clinician": 1.3,
            "researcher": 1.1,
            "student": 1.0,
            "administrator": 1.2,
            "general": 1.0
        }

        score = min(10, score * context_multipliers.get(user_context, 1.0))

        # Determine relevance level
        if score >= 8:
            relevance_level = "High"
            applicability = "Ready for clinical consideration"
        elif score >= 5:
            relevance_level = "Medium"
            applicability = "Promising but requires validation"
        elif score >= 3:
            relevance_level = "Low"
            applicability = "Preliminary evidence"
        else:
            relevance_level = "Very Low"
            applicability = "Primarily theoretical/research"

        # Check study design
        study_design = "Unknown"
        for design in ['randomized controlled trial', 'RCT', 'prospective cohort',
                       'retrospective cohort', 'case-control', 'systematic review']:
            if design in abstract_lower:
                study_design = design
                break

        return {
            "clinical_impact": relevance_level,
            "score": round(score, 1),
            "applicability": applicability,
            "study_design": study_design,
            "key_strengths": self._identify_strengths(abstract_lower),
            "main_limitations": self._identify_limitations(abstract_lower)
        }

    def _identify_strengths(self, abstract: str) -> List[str]:
        """Identify study strengths from abstract"""
        strengths = []

        if 'randomized' in abstract or 'rct' in abstract:
            strengths.append("Randomized controlled trial design")
        if 'prospective' in abstract:
            strengths.append("Prospective design")
        if 'multicenter' in abstract or 'multi-center' in abstract:
            strengths.append("Multi-center study")
        if 'large sample' in abstract or 'n > 1000' in abstract:
            strengths.append("Large sample size")
        if 'long-term' in abstract:
            strengths.append("Long-term follow-up")
        if 'blinded' in abstract:
            strengths.append("Blinded assessment")

        return strengths[:3] if strengths else ["Standard study design"]

    def _identify_limitations(self, abstract: str) -> List[str]:
        """Identify study limitations from abstract"""
        limitations = []

        limitation_phrases = [
            'limitation', 'limited by', 'caution', 'constraint',
            'small sample', 'retrospective', 'single center',
            'short-term', 'observational', 'cannot determine',
            'further research', 'larger studies', 'validate'
        ]

        sentences = re.split(r'[.!?]+', abstract)
        for sentence in sentences:
            if any(phrase in sentence.lower() for phrase in limitation_phrases):
                limitations.append(sentence.strip())

        return limitations[:3] if limitations else ["Standard study limitations apply"]

    def _calculate_clinical_confidence(self, paper: Dict,

                                       metrics: Dict,

                                       relevance: Dict) -> float:
        """Calculate confidence score for clinical paper summary"""
        confidence = 0.5  # Base confidence

        # Abstract quality
        abstract = paper.get('abstract', '')
        if len(abstract) > 800:
            confidence += 0.2
        elif len(abstract) > 400:
            confidence += 0.1

        # Source reliability
        source = paper.get('source', '').lower()
        if any(journal in source for journal in ['nejm', 'lancet', 'jama', 'bmj']):
            confidence += 0.15
        elif 'pubmed' in source:
            confidence += 0.1
        elif 'arxiv' in source:
            confidence += 0.05

        # Recency
        if paper.get('publication_date'):
            try:
                pub_year = int(str(paper['publication_date'])[:4])
                current_year = datetime.now().year
                if current_year - pub_year <= 2:
                    confidence += 0.1
                elif current_year - pub_year <= 5:
                    confidence += 0.05
            except:
                pass

        # Citations
        citations = paper.get('citations', 0)
        if citations > 100:
            confidence += 0.05
        elif citations > 20:
            confidence += 0.03

        # Metric richness
        metric_score = metrics.get("metric_richness", 0)
        confidence += min(0.1, metric_score * 0.02)

        # Clinical relevance
        relevance_score = relevance.get("score", 0)
        confidence += min(0.1, relevance_score * 0.01)

        return min(1.0, max(0.3, confidence))

    def _generate_quick_clinical_summary(self, title: str, abstract: str,

                                         user_context: str) -> str:
        """Generate a quick 2-sentence clinical summary"""

        prompt = f"""Create a 2-sentence clinical summary of this paper for a {user_context}:



Title: {title}



Key content: {abstract[:800]}



First sentence: Main clinical finding.

Second sentence: Clinical implication for {user_context}.



Be extremely concise and action-oriented."""

        try:
            summary = self.llm.generate(prompt, max_tokens=150)
            return summary.strip()
        except:
            # Fallback
            title_snippet = title.split(':')[0] if ':' in title else title[:50]
            return f"""1. Study shows promising results in {title_snippet}.

2. Consider for {self._infer_application(abstract, user_context)}."""

    def _infer_application(self, abstract: str, user_context: str) -> str:
        """Infer clinical application from abstract"""
        abstract_lower = abstract.lower()

        if user_context == "clinician":
            if 'treatment' in abstract_lower:
                return "treatment decisions"
            elif 'diagnosis' in abstract_lower:
                return "diagnostic workup"
            elif 'screening' in abstract_lower:
                return "screening protocols"
            else:
                return "clinical consideration"

        elif user_context == "researcher":
            if 'mechanism' in abstract_lower:
                return "mechanistic studies"
            elif 'novel' in abstract_lower:
                return "innovation validation"
            else:
                return "further research"

        return "appropriate applications"

    def _generate_fallback_summary(self, paper: Dict, query: str,

                                   user_context: str, error: str) -> Dict[str, Any]:
        """Generate fallback summary when enhanced summary fails"""
        title = paper.get('title', '')
        abstract = paper.get('abstract', '')

        return {
            "success": False,
            "paper_title": title,
            "error": f"Enhanced summarization failed: {error}",
            "enhanced_summary": f"""# 📄 Basic Paper Summary\n\n**Title:** {title}\n\n**Abstract:**\n{abstract[:1500]}...\n\n*Note: Enhanced clinical analysis unavailable. Please try again.*""",
            "quick_summary": f"Basic summary of {title[:50]}...",
            "user_context": user_context,
            "query_context": query,
            "confidence": 0.4
        }

    def generate_quick_summary(self, paper: Dict[str, Any],

                               user_context: str = "general") -> str:
        """Generate a quick clinical summary (legacy method for compatibility)"""
        return self._generate_quick_clinical_summary(
            paper.get('title', ''),
            paper.get('abstract', ''),
            user_context
        )

    def summarize_multiple_papers(self, papers: List[Dict],

                                  query: str = None,

                                  user_context: str = "general") -> Dict[str, Any]:
        """Generate comparative summary of multiple papers"""
        if not papers:
            return {"error": "No papers provided"}

        summaries = []
        for paper in papers[:5]:  # Limit to 5 papers
            summary = self.summarize_paper(paper, query, user_context)
            summaries.append(summary)

        # Generate comparative analysis
        comparative = self._generate_comparative_analysis(summaries, user_context)

        return {
            "success": True,
            "paper_count": len(papers),
            "individual_summaries": summaries,
            "comparative_analysis": comparative,
            "user_context": user_context
        }

    def _generate_comparative_analysis(self, summaries: List[Dict],

                                       user_context: str) -> str:
        """Generate comparative analysis of multiple papers"""
        if len(summaries) < 2:
            return "Single paper analysis only"

        prompt = f"""Compare these {len(summaries)} papers for a {user_context}:



"""

        for i, summary in enumerate(summaries, 1):
            prompt += f"""Paper {i}: {summary.get('paper_title', 'Unknown')}

Clinical Impact: {summary.get('clinical_relevance', {}).get('clinical_impact', 'Unknown')}

Key Finding: {summary.get('quick_summary', '')[:100]}



"""

        prompt += f"""Provide a comparative analysis focusing on:

1. Consistency of findings

2. Evidence strength across papers

3. Clinical implications for {user_context}

4. Research gaps identified



Format as a concise clinical comparison."""

        try:
            return self.llm.generate(prompt, max_tokens=800)
        except:
            return "Comparative analysis unavailable"