Update main.py

main.py

@@ -1,1028 +1,1028 @@
-import os
-import time
-import requests
-import random
-import re
-from difflib import SequenceMatcher
-from typing import List, Optional, Dict, Any
-from urllib.parse import quote_plus
-
-from fastapi import FastAPI, UploadFile, File, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-import uvicorn
-from pydantic import BaseModel
-from PyPDF2 import PdfReader
-
-from langchain_groq import ChatGroq
-from langchain_core.prompts import ChatPromptTemplate
-
-# ==========================================
-# 1. Environment & API Setup
-# ==========================================
-GROQ_API_KEY = os.getenv("GROQ_API_KEY","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")
-SERPER_API_KEY = os.getenv("SERPER_API_KEY","e8f2e0a4b337e7e8b63d5bf6057f01441cfa6ca5")
-SEMANTIC_SCHOLAR_API_KEY = os.getenv("SEMANTIC_SCHOLAR_API_KEY","0a00aUnvlY9iwf7sDOQEoaVRubNMtFTi3KOa6bB0")
-SEMANTIC_SCHOLAR_BASE_URL = "https://api.semanticscholar.org/graph/v1"
-SEMANTIC_SCHOLAR_MIN_INTERVAL_SECONDS = 1.2
-SEMANTIC_SCHOLAR_MAX_RETRIES = 4
-
-if not GROQ_API_KEY or not SERPER_API_KEY:
-    print("WARNING: GROQ_API_KEY or SERPER_API_KEY is missing!")
-
-llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0.1)
-
-# Basic Memory Cache to maintain API efficiency (as promised in the application)
-query_cache = {}
-semantic_query_cache: Dict[str, List[Dict[str, str]]] = {}
-_last_semantic_scholar_call_ts = 0.0
-
-# ==========================================
-# 2. Pydantic Models
-# ==========================================
-class MatchReport(BaseModel):
-    chunk_text: str
-    is_plagiarized: bool
-    plagiarism_type: Optional[str] = None 
-    source_url: Optional[str] = None
-    source_type: Optional[str] = None # "Academic" or "Web"
-    similarity_score: float
-
-class PlagiarismReport(BaseModel):
-    filename: str
-    total_words: int
-    plagiarized_words: int
-    overall_plagiarism_score: float
-    severity_level: str  # Low, Medium, High, Very High
-    details: List[MatchReport]
-
-class DetailedPlagiarismReport(BaseModel):
-    """Comprehensive report generated by LLM"""
-    filename: str
-    scan_timestamp: str
-    executive_summary: str
-    overall_score: float
-    severity_level: str
-    matched_sources: List[Dict[str, Any]]
-    key_findings: List[str]
-    plagiarism_breakdown: Dict[str, Any]  # Types and percentages
-    detailed_analysis: str  # LLM-generated detailed analysis
-    affected_sections: List[Dict[str, Any]]  # Which parts are problematic
-    recommendations: List[str]
-    academic_integrity_risk: str  # Assessment level
-
-app = FastAPI(title="Pro Plagiarism Detector (Turnitin Clone)")
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# ==========================================
-# 3. Agent Tools: Serper & Semantic Scholar
-# ==========================================
-
-def _semantic_scholar_headers() -> Dict[str, str]:
-    headers: Dict[str, str] = {}
-    if SEMANTIC_SCHOLAR_API_KEY:
-        # API key must be sent in x-api-key header.
-        headers["x-api-key"] = SEMANTIC_SCHOLAR_API_KEY
-    return headers
-
-
-def _semantic_scholar_get(path: str, params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
-    global _last_semantic_scholar_call_ts
-    filtered_params = {k: v for k, v in (params or {}).items() if v is not None}
-
-    for attempt in range(SEMANTIC_SCHOLAR_MAX_RETRIES):
-        elapsed = time.time() - _last_semantic_scholar_call_ts
-        if elapsed < SEMANTIC_SCHOLAR_MIN_INTERVAL_SECONDS:
-            time.sleep(SEMANTIC_SCHOLAR_MIN_INTERVAL_SECONDS - elapsed)
-
-        response = requests.get(
-            f"{SEMANTIC_SCHOLAR_BASE_URL}{path}",
-            headers=_semantic_scholar_headers(),
-            params=filtered_params,
-            timeout=20,
-        )
-        _last_semantic_scholar_call_ts = time.time()
-
-        if response.status_code == 429 and attempt < SEMANTIC_SCHOLAR_MAX_RETRIES - 1:
-            retry_after = response.headers.get("Retry-After")
-            if retry_after and retry_after.isdigit():
-                wait_seconds = float(retry_after)
-            else:
-                wait_seconds = (2 ** attempt) + random.uniform(0.2, 0.7)
-            time.sleep(wait_seconds)
-            continue
-
-        response.raise_for_status()
-        return response.json()
-
-    raise requests.HTTPError("Semantic Scholar request failed after retries")
-
-
-def _semantic_scholar_post(path: str, body: Dict[str, Any], params: Optional[Dict[str, Any]] = None) -> Any:
-    global _last_semantic_scholar_call_ts
-    filtered_params = {k: v for k, v in (params or {}).items() if v is not None}
-
-    for attempt in range(SEMANTIC_SCHOLAR_MAX_RETRIES):
-        elapsed = time.time() - _last_semantic_scholar_call_ts
-        if elapsed < SEMANTIC_SCHOLAR_MIN_INTERVAL_SECONDS:
-            time.sleep(SEMANTIC_SCHOLAR_MIN_INTERVAL_SECONDS - elapsed)
-
-        response = requests.post(
-            f"{SEMANTIC_SCHOLAR_BASE_URL}{path}",
-            headers=_semantic_scholar_headers(),
-            params=filtered_params,
-            json=body,
-            timeout=25,
-        )
-        _last_semantic_scholar_call_ts = time.time()
-
-        if response.status_code == 429 and attempt < SEMANTIC_SCHOLAR_MAX_RETRIES - 1:
-            retry_after = response.headers.get("Retry-After")
-            if retry_after and retry_after.isdigit():
-                wait_seconds = float(retry_after)
-            else:
-                wait_seconds = (2 ** attempt) + random.uniform(0.2, 0.7)
-            time.sleep(wait_seconds)
-            continue
-
-        response.raise_for_status()
-        return response.json()
-
-    raise requests.HTTPError("Semantic Scholar request failed after retries")
-
-
-def s2_paper_autocomplete(query: str) -> Dict[str, Any]:
-    return _semantic_scholar_get("/paper/autocomplete", {"query": query[:100]})
-
-
-def s2_paper_batch(ids: List[str], fields: Optional[str] = None) -> Any:
-    return _semantic_scholar_post("/paper/batch", {"ids": ids[:500]}, {"fields": fields})
-
-
-def s2_paper_search(
-    query: str,
-    fields: Optional[str] = None,
-    limit: int = 100,
-    offset: int = 0,
-    year: Optional[str] = None,
-    fields_of_study: Optional[str] = None,
-    open_access_pdf: bool = False,
-) -> Dict[str, Any]:
-    params: Dict[str, Any] = {
-        "query": query,
-        "fields": fields,
-        "limit": min(max(limit, 1), 100),
-        "offset": max(offset, 0),
-        "year": year,
-        "fieldsOfStudy": fields_of_study,
-    }
-    if open_access_pdf:
-        params["openAccessPdf"] = ""
-    return _semantic_scholar_get("/paper/search", params)
-
-
-def s2_paper_search_bulk(
-    query: str,
-    fields: Optional[str] = None,
-    token: Optional[str] = None,
-    sort: Optional[str] = None,
-) -> Dict[str, Any]:
-    return _semantic_scholar_get(
-        "/paper/search/bulk",
-        {
-            "query": query,
-            "fields": fields,
-            "token": token,
-            "sort": sort,
-        },
-    )
-
-
-def s2_paper_search_match(query: str, fields: Optional[str] = None) -> Dict[str, Any]:
-    return _semantic_scholar_get("/paper/search/match", {"query": query, "fields": fields})
-
-
-def s2_paper_details(paper_id: str, fields: Optional[str] = None) -> Dict[str, Any]:
-    safe_id = quote_plus(paper_id)
-    return _semantic_scholar_get(f"/paper/{safe_id}", {"fields": fields})
-
-
-def s2_paper_authors(
-    paper_id: str,
-    fields: Optional[str] = None,
-    limit: int = 100,
-    offset: int = 0,
-) -> Dict[str, Any]:
-    safe_id = quote_plus(paper_id)
-    return _semantic_scholar_get(
-        f"/paper/{safe_id}/authors",
-        {"fields": fields, "limit": min(max(limit, 1), 1000), "offset": max(offset, 0)},
-    )
-
-
-def s2_paper_citations(
-    paper_id: str,
-    fields: Optional[str] = None,
-    limit: int = 100,
-    offset: int = 0,
-    publication_date_or_year: Optional[str] = None,
-) -> Dict[str, Any]:
-    safe_id = quote_plus(paper_id)
-    return _semantic_scholar_get(
-        f"/paper/{safe_id}/citations",
-        {
-            "fields": fields,
-            "limit": min(max(limit, 1), 1000),
-            "offset": max(offset, 0),
-            "publicationDateOrYear": publication_date_or_year,
-        },
-    )
-
-
-def s2_paper_references(
-    paper_id: str,
-    fields: Optional[str] = None,
-    limit: int = 100,
-    offset: int = 0,
-) -> Dict[str, Any]:
-    safe_id = quote_plus(paper_id)
-    return _semantic_scholar_get(
-        f"/paper/{safe_id}/references",
-        {"fields": fields, "limit": min(max(limit, 1), 1000), "offset": max(offset, 0)},
-    )
-
-
-def s2_author_batch(ids: List[str], fields: Optional[str] = None) -> Any:
-    return _semantic_scholar_post("/author/batch", {"ids": ids[:1000]}, {"fields": fields})
-
-
-def s2_author_search(
-    query: str,
-    fields: Optional[str] = None,
-    limit: int = 100,
-    offset: int = 0,
-) -> Dict[str, Any]:
-    return _semantic_scholar_get(
-        "/author/search",
-        {
-            "query": query,
-            "fields": fields,
-            "limit": min(max(limit, 1), 1000),
-            "offset": max(offset, 0),
-        },
-    )
-
-
-def s2_author_details(author_id: str, fields: Optional[str] = None) -> Dict[str, Any]:
-    safe_id = quote_plus(author_id)
-    return _semantic_scholar_get(f"/author/{safe_id}", {"fields": fields})
-
-
-def s2_author_papers(
-    author_id: str,
-    fields: Optional[str] = None,
-    limit: int = 100,
-    offset: int = 0,
-    publication_date_or_year: Optional[str] = None,
-) -> Dict[str, Any]:
-    safe_id = quote_plus(author_id)
-    return _semantic_scholar_get(
-        f"/author/{safe_id}/papers",
-        {
-            "fields": fields,
-            "limit": min(max(limit, 1), 1000),
-            "offset": max(offset, 0),
-            "publicationDateOrYear": publication_date_or_year,
-        },
-    )
-
-
-def s2_snippet_search(
-    query: str,
-    fields: Optional[str] = None,
-    limit: int = 10,
-    year: Optional[str] = None,
-    fields_of_study: Optional[str] = None,
-) -> Dict[str, Any]:
-    return _semantic_scholar_get(
-        "/snippet/search",
-        {
-            "query": query,
-            "fields": fields,
-            "limit": min(max(limit, 1), 1000),
-            "year": year,
-            "fieldsOfStudy": fields_of_study,
-        },
-    )
-
-
-def build_search_query(text: str, max_terms: int = 10) -> str:
-    """Builds a compact keyword query to improve search recall and reduce noisy long queries."""
-    stopwords = {
-        "the", "and", "for", "that", "with", "this", "from", "into", "our", "their",
-        "were", "have", "has", "had", "been", "are", "was", "will", "would", "can",
-        "could", "should", "about", "through", "using", "based", "than", "then", "also",
-        "such", "these", "those", "while", "where", "when", "what", "which", "who",
-    }
-    words = re.findall(r"[A-Za-z0-9]+", text.lower())
-    keywords = [w for w in words if len(w) > 2 and w not in stopwords]
-    return " ".join(keywords[:max_terms]) if keywords else " ".join(words[:max_terms])
-
-def search_google_serper(query: str) -> List[Dict]:
-    """Searches the open web using Google Serper API."""
-    url = "https://google.serper.dev/search"
-    payload = {"q": query}
-    headers = {
-        'X-API-KEY': SERPER_API_KEY,
-        'Content-Type': 'application/json'
-    }
-    
-    try:
-        response = requests.post(url, headers=headers, json=payload)
-        response.raise_for_status()
-        data = response.json()
-        
-        results = []
-        for item in data.get("organic", [])[:3]: # Top 3 web results
-            results.append({
-                "text": item.get("snippet", ""),
-                "url": item.get("link", ""),
-                "source_type": "Web (Google)"
-            })
-        return results
-    except Exception as e:
-        print(f"Serper Error: {e}")
-        return []
-
-def search_semantic_scholar(query: str) -> List[Dict]:
-    """Searches academic papers using Semantic Scholar API."""
-    prepared_query = build_search_query(query, max_terms=10)
-    normalized_query = " ".join(prepared_query.split()).lower()
-    if normalized_query in semantic_query_cache:
-        return semantic_query_cache[normalized_query]
-
-    try:
-        results = []
-
-        # Try snippet search first because it returns passage-level text better suited for chunk comparison.
-        snippet_data = s2_snippet_search(
-            query=prepared_query,
-            fields="snippet.text,snippet.snippetKind",
-            limit=3,
-        )
-        for item in snippet_data.get("data", []):
-            snippet = item.get("snippet", {})
-            paper = item.get("paper", {})
-            snippet_text = snippet.get("text", "")
-            if snippet_text:
-                corpus_id = paper.get("corpusId")
-                paper_url = f"https://www.semanticscholar.org/paper/{corpus_id}" if corpus_id else None
-                results.append({
-                    "text": snippet_text,
-                    "url": paper_url,
-                    "source_type": "Academic (Semantic Scholar Snippet)",
-                })
-
-        # Keep paper abstract search as fallback/secondary source.
-        data = s2_paper_search(
-            query=prepared_query,
-            limit=2,
-            fields="title,abstract,url",
-        )
-
-        for item in data.get("data", []):
-            if item.get("abstract"): # Only keep if abstract exists to compare text
-                results.append({
-                    "text": item["abstract"],
-                    "url": item.get("url", f"https://www.semanticscholar.org/paper/{item['paperId']}"),
-                    "source_type": "Academic (Semantic Scholar)"
-                })
-        semantic_query_cache[normalized_query] = results
-        return results
-    except Exception as e:
-        print(f"Semantic Scholar Error: {e}")
-        return []
-
-def aggregate_search(query: str) -> List[Dict]:
-    """Combines Academic and Web sources and implements caching."""
-    # Use the first 15 words to make the search query efficient
-    search_query = " ".join(query.split()[:15])
-    
-    if search_query in query_cache:
-        return query_cache[search_query]
-
-    # Run both searches
-    web_results = search_google_serper(search_query)
-    academic_results = search_semantic_scholar(search_query)
-    
-    combined = web_results + academic_results
-    query_cache[search_query] = combined # Save to cache
-    
-    # Sleep to respect rate limits
-    time.sleep(1)
-    
-    return combined
-
-# ==========================================
-# 4. Core Comparison Logic
-# ==========================================
-
-def calculate_exact_similarity(text1: str, text2: str) -> float:
-    return SequenceMatcher(None, text1.lower(), text2.lower()).ratio()
-
-def check_paraphrasing_with_llm(chunk: str, source_text: str) -> bool:
-    prompt = ChatPromptTemplate.from_messages([
-        ("system", "You are an expert academic plagiarism detector. Determine if TEXT A is a direct paraphrase, stolen idea, or highly similar structure to TEXT B. Ignore generic academic phrases like 'In this paper we demonstrate'. Respond ONLY with 'YES' or 'NO'."),
-        ("user", "TEXT A: {chunk}\n\nTEXT B: {source_text}")
-    ])
-    chain = prompt | llm
-    response = chain.invoke({"chunk": chunk, "source_text": source_text})
-    return "YES" in response.content.upper()
-
-def generate_detailed_report_with_llm(
-    filename: str, 
-    match_reports: List[MatchReport], 
-    total_words: int, 
-    overall_score: float
-) -> DetailedPlagiarismReport:
-    """Generate a comprehensive report using LLM analysis"""
-    from datetime import datetime
-    
-    # 1. Aggregate data for analysis
-    plagiarized_reports = [r for r in match_reports if r.is_plagiarized]
-    plagiarism_types = {}
-    sources_by_type = {"Academic": [], "Web": []}
-    
-    for report in plagiarized_reports:
-        ptype = report.plagiarism_type or "Unknown"
-        plagiarism_types[ptype] = plagiarism_types.get(ptype, 0) + 1
-        
-        if report.source_type:
-            if "Academic" in report.source_type:
-                if report.source_url not in sources_by_type["Academic"]:
-                    sources_by_type["Academic"].append({
-                        "url": report.source_url,
-                        "type": report.source_type,
-                        "max_similarity": report.similarity_score
-                    })
-            else:
-                if report.source_url not in sources_by_type["Web"]:
-                    sources_by_type["Web"].append({
-                        "url": report.source_url,
-                        "type": report.source_type,
-                        "max_similarity": report.similarity_score
-                    })
-    
-    # 2. Determine severity level
-    if overall_score < 15:
-        severity = "Low"
-        risk_level = "Minimal - Normal citation variations detected"
-    elif overall_score < 30:
-        severity = "Medium"
-        risk_level = "Moderate - Multiple sources match detected"
-    elif overall_score < 50:
-        severity = "High"
-        risk_level = "Significant - Substantial plagiarism detected"
-    else:
-        severity = "Very High"
-        risk_level = "Critical - Extensive plagiarism detected"
-    
-    # 3. Use LLM to generate detailed analysis
-    plagiarism_context = f"""
-Document: {filename}
-Total Words: {total_words}
-Plagiarism Score: {overall_score}%
-Plagiarism Types Found: {plagiarism_types}
-Academic Matches: {len(sources_by_type['Academic'])}
-Web Matches: {len(sources_by_type['Web'])}
-
-Suspicious Sections (samples):
-{chr(10).join([f"- {r.chunk_text[:100]}..." for r in plagiarized_reports[:5]])}
-"""
-    
-    analysis_prompt = ChatPromptTemplate.from_messages([
-        ("system", """You are an expert academic integrity analyzer and plagiarism report generator.
-        Generate a professional, detailed plagiarism analysis report.
-        Focus on: severity assessment, academic integrity concerns, specific problem areas, and recommendations.
-        Be thorough but concise."""),
-        ("user", """Create a detailed plagiarism analysis for this document:
-
-{plagiarism_context}
-
-Provide:
-1. Executive Summary (2-3 sentences)
-2. Key Findings (3-4 bullet points)
-3. Detailed Analysis (2-3 paragraphs explaining the plagiarism pattern)
-4. Recommendations (3-4 specific actions to remediate)
-
-Format clearly with section headers.""")
-    ])
-
-    chain = analysis_prompt | llm
-    llm_response = chain.invoke({"plagiarism_context": plagiarism_context})
-    llm_analysis = llm_response.content
-    
-    # 4. Extract findings from LLM response
-    lines = llm_analysis.split('\n')
-    key_findings = []
-    recommendations = []
-    detailed_analysis = ""
-    
-    in_findings = False
-    in_recommendations = False
-    
-    for line in lines:
-        if 'Key Findings' in line:
-            in_findings = True
-            in_recommendations = False
-        elif 'Recommendations' in line:
-            in_findings = False
-            in_recommendations = True
-        elif 'Detailed Analysis' in line or 'Analysis' in line:
-            in_findings = False
-            in_recommendations = False
-        elif in_findings and line.strip().startswith(('-', '*', '•')):
-            key_findings.append(line.strip().lstrip('-*•').strip())
-        elif in_recommendations and line.strip().startswith(('-', '*', '•')):
-            recommendations.append(line.strip().lstrip('-*•').strip())
-        elif not in_findings and not in_recommendations and line.strip():
-            detailed_analysis += line + "\n"
-    
-    if not key_findings:
-        key_findings = [
-            f"Overall plagiarism score: {overall_score}%",
-            f"Primary plagiarism type: {max(plagiarism_types.keys(), key=plagiarism_types.get) if plagiarism_types else 'Not detected'}",
-            f"Multiple sources detected: {len(sources_by_type['Academic']) + len(sources_by_type['Web'])} sources"
-        ]
-    
-    if not recommendations:
-        recommendations = [
-            "Properly cite all sources according to your institution's guidelines",
-            "Use quotation marks for direct quotes and provide page numbers",
-            "Paraphrase content properly and cite original sources",
-            "Use plagiarism detection tools during the writing process"
-        ]
-    
-    # 5. Affected sections
-    affected_sections = []
-    for i, report in enumerate(plagiarized_reports[:10]):
-        affected_sections.append({
-            "section_number": i + 1,
-            "text_snippet": report.chunk_text[:150],
-            "similarity_score": report.similarity_score,
-            "plagiarism_type": report.plagiarism_type,
-            "source": report.source_url,
-            "source_type": report.source_type
-        })
-    
-    return DetailedPlagiarismReport(
-        filename=filename,
-        scan_timestamp=datetime.now().isoformat(),
-        executive_summary=llm_analysis.split('\n')[0] if llm_analysis else f"Document contains {overall_score}% plagiarized content",
-        overall_score=round(overall_score, 2),
-        severity_level=severity,
-        matched_sources=sources_by_type["Academic"] + sources_by_type["Web"],
-        key_findings=key_findings,
-        plagiarism_breakdown={
-            "total_plagiarism_percentage": round(overall_score, 2),
-            "types": plagiarism_types,
-            "academic_sources": len(sources_by_type["Academic"]),
-            "web_sources": len(sources_by_type["Web"])
-        },
-        detailed_analysis=detailed_analysis or llm_analysis,
-        affected_sections=affected_sections,
-        recommendations=recommendations,
-        academic_integrity_risk=risk_level
-    )
-
-def analyze_chunk(chunk: str) -> MatchReport:
-    search_results = aggregate_search(chunk)
-    
-    best_score = 0.0
-    best_url = None
-    best_source_type = None
-    plagiarism_type = None
-    is_plagiarized = False
-    
-    for result in search_results:
-        source_text = result['text']
-        
-        # 1. Math/Deterministic Check
-        exact_sim = calculate_exact_similarity(chunk, source_text)
-        
-        if exact_sim > best_score:
-            best_score = exact_sim
-            best_url = result['url']
-            best_source_type = result['source_type']
-            
-        if exact_sim > 0.50: # Lowered to 50% because we are comparing against abstracts/snippets
-            is_plagiarized = True
-            plagiarism_type = "Exact/Heavy Match"
-            break 
-            
-        # 2. Agentic Check for Mosaic Plagiarism
-        elif exact_sim > 0.25: 
-            if check_paraphrasing_with_llm(chunk, source_text):
-                is_plagiarized = True
-                plagiarism_type = "Paraphrased Match (Mosaic)"
-                best_url = result['url']
-                best_source_type = result['source_type']
-                best_score = max(best_score, 0.85) 
-                break
-                
-    return MatchReport(
-        chunk_text=chunk,
-        is_plagiarized=is_plagiarized,
-        plagiarism_type=plagiarism_type,
-        source_url=best_url,
-        source_type=best_source_type,
-        similarity_score=round(best_score, 2)
-    )
-
-# ==========================================
-# 6. Report Formatting Functions
-# ==========================================
-
-def format_report_json(detailed_report: DetailedPlagiarismReport) -> Dict[str, Any]:
-    """Format report as JSON"""
-    return {
-        "filename": detailed_report.filename,
-        "scan_timestamp": detailed_report.scan_timestamp,
-        # Backward-compatible top-level fields expected by existing clients.
-        "overall_score": detailed_report.overall_score,
-        "severity_level": detailed_report.severity_level,
-        "academic_integrity_risk": detailed_report.academic_integrity_risk,
-        "summary": {
-            "overall_plagiarism_score": detailed_report.overall_score,
-            "severity_level": detailed_report.severity_level,
-            "academic_integrity_risk": detailed_report.academic_integrity_risk
-        },
-        "executive_summary": detailed_report.executive_summary,
-        "key_findings": detailed_report.key_findings,
-        "plagiarism_breakdown": detailed_report.plagiarism_breakdown,
-        "matched_sources": detailed_report.matched_sources,
-        "affected_sections": detailed_report.affected_sections,
-        "detailed_analysis": detailed_report.detailed_analysis,
-        "recommendations": detailed_report.recommendations
-    }
-
-def format_report_text(detailed_report: DetailedPlagiarismReport) -> str:
-    """Format report as plain text"""
-    report = "=" * 80 + "\n"
-    report += "DETAILED PLAGIARISM DETECTION REPORT\n"
-    report += "=" * 80 + "\n\n"
-    
-    report += f"FILE: {detailed_report.filename}\n"
-    report += f"SCAN DATE: {detailed_report.scan_timestamp}\n"
-    report += "-" * 80 + "\n\n"
-    
-    report += "SUMMARY\n"
-    report += "-" * 80 + "\n"
-    report += f"Overall Plagiarism Score: {detailed_report.overall_score}%\n"
-    report += f"Severity Level: {detailed_report.severity_level}\n"
-    report += f"Academic Integrity Risk: {detailed_report.academic_integrity_risk}\n\n"
-    
-    report += "EXECUTIVE SUMMARY\n"
-    report += "-" * 80 + "\n"
-    report += f"{detailed_report.executive_summary}\n\n"
-    
-    report += "KEY FINDINGS\n"
-    report += "-" * 80 + "\n"
-    for i, finding in enumerate(detailed_report.key_findings, 1):
-        report += f"{i}. {finding}\n"
-    report += "\n"
-    
-    report += "PLAGIARISM BREAKDOWN\n"
-    report += "-" * 80 + "\n"
-    report += f"Total Plagiarism %: {detailed_report.plagiarism_breakdown['total_plagiarism_percentage']}%\n"
-    report += f"Academic Sources: {detailed_report.plagiarism_breakdown['academic_sources']}\n"
-    report += f"Web Sources: {detailed_report.plagiarism_breakdown['web_sources']}\n"
-    if detailed_report.plagiarism_breakdown.get('types'):
-        report += "Types Detected:\n"
-        for ptype, count in detailed_report.plagiarism_breakdown['types'].items():
-            report += f"  - {ptype}: {count} instances\n"
-    report += "\n"
-    
-    report += "MATCHED SOURCES\n"
-    report += "-" * 80 + "\n"
-    if detailed_report.matched_sources:
-        for i, source in enumerate(detailed_report.matched_sources[:10], 1):
-            report += f"{i}. URL: {source.get('url', 'N/A')}\n"
-            report += f"   Type: {source.get('type', 'N/A')}\n"
-            report += f"   Similarity: {source.get('max_similarity', 'N/A')}\n\n"
-    else:
-        report += "No sources matched.\n\n"
-    
-    report += "DETAILED ANALYSIS\n"
-    report += "-" * 80 + "\n"
-    report += f"{detailed_report.detailed_analysis}\n\n"
-    
-    if detailed_report.affected_sections:
-        report += "AFFECTED SECTIONS (Top Issues)\n"
-        report += "-" * 80 + "\n"
-        for section in detailed_report.affected_sections[:5]:
-            report += f"\nSection {section['section_number']}:\n"
-            report += f"Text Snippet: {section['text_snippet']}\n"
-            report += f"Similarity Score: {section['similarity_score']}\n"
-            report += f"Plagiarism Type: {section['plagiarism_type']}\n"
-            report += f"Source: {section['source']}\n"
-    report += "\n"
-    
-    report += "RECOMMENDATIONS\n"
-    report += "-" * 80 + "\n"
-    for i, rec in enumerate(detailed_report.recommendations, 1):
-        report += f"{i}. {rec}\n"
-    report += "\n"
-    
-    report += "=" * 80 + "\n"
-    report += "End of Report\n"
-    report += "=" * 80 + "\n"
-    
-    return report
-
-def format_report_html(detailed_report: DetailedPlagiarismReport) -> str:
-    """Format report as HTML"""
-    html = f"""
-    <!DOCTYPE html>
-    <html lang="en">
-    <head>
-        <meta charset="UTF-8">
-        <meta name="viewport" content="width=device-width, initial-scale=1.0">
-        <title>Plagiarism Detection Report - {detailed_report.filename}</title>
-        <style>
-            body {{ font-family: Arial, sans-serif; margin: 40px; background-color: #f5f5f5; }}
-            .container {{ background-color: white; padding: 30px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); }}
-            h1 {{ color: #333; border-bottom: 3px solid #2196F3; padding-bottom: 10px; }}
-            h2 {{ color: #2196F3; margin-top: 30px; }}
-            .summary {{ background-color: #f0f7ff; padding: 15px; border-left: 4px solid #2196F3; margin: 20px 0; }}
-            .score {{ font-size: 24px; font-weight: bold; color: #d32f2f; }}
-            .severity-low {{ color: #4caf50; }}
-            .severity-medium {{ color: #ff9800; }}
-            .severity-high {{ color: #f44336; }}
-            .severity-very-high {{ color: #c41c3b; }}
-            .findings {{ background-color: #fff3e0; padding: 15px; border-left: 4px solid #ff9800; }}
-            .source-item {{ background-color: #f5f5f5; padding: 10px; margin: 10px 0; border-radius: 4px; }}
-            .recommendation {{ background-color: #e8f5e9; padding: 10px; margin: 10px 0; border-left: 3px solid #4caf50; }}
-            table {{ width: 100%; border-collapse: collapse; margin: 15px 0; }}
-            th, td {{ padding: 10px; text-align: left; border-bottom: 1px solid #ddd; }}
-            th {{ background-color: #2196F3; color: white; }}
-            .affected-section {{ background-color: #fce4ec; padding: 15px; margin: 10px 0; border-radius: 4px; }}
-        </style>
-    </head>
-    <body>
-        <div class="container">
-            <h1>🔍 Plagiarism Detection Report</h1>
-            
-            <div class="summary">
-                <p><strong>File:</strong> {detailed_report.filename}</p>
-                <p><strong>Scan Date:</strong> {detailed_report.scan_timestamp}</p>
-                <p><strong>Overall Plagiarism Score:</strong> <span class="score">{detailed_report.overall_score}%</span></p>
-                <p><strong>Severity Level:</strong> <span class="severity-{detailed_report.severity_level.lower().replace(' ', '-')}">{detailed_report.severity_level}</span></p>
-                <p><strong>Academic Integrity Risk:</strong> {detailed_report.academic_integrity_risk}</p>
-            </div>
-            
-            <h2>Executive Summary</h2>
-            <p>{detailed_report.executive_summary}</p>
-            
-            <h2>Key Findings</h2>
-            <div class="findings">
-                <ul>
-                    {"".join([f"<li>{finding}</li>" for finding in detailed_report.key_findings])}
-                </ul>
-            </div>
-            
-            <h2>Plagiarism Breakdown</h2>
-            <table>
-                <tr>
-                    <th>Category</th>
-                    <th>Value</th>
-                </tr>
-                <tr>
-                    <td>Total Plagiarism %</td>
-                    <td>{detailed_report.plagiarism_breakdown['total_plagiarism_percentage']}%</td>
-                </tr>
-                <tr>
-                    <td>Academic Sources</td>
-                    <td>{detailed_report.plagiarism_breakdown['academic_sources']}</td>
-                </tr>
-                <tr>
-                    <td>Web Sources</td>
-                    <td>{detailed_report.plagiarism_breakdown['web_sources']}</td>
-                </tr>
-            </table>
-            
-            <h2>Matched Sources</h2>
-            {"".join([f'<div class="source-item"><strong>{source.get("type", "Unknown")}</strong><br/><a href="{source.get("url", "#")}" target="_blank">{source.get("url", "N/A")}</a><br/>Similarity: {source.get("max_similarity", "N/A")}</div>' for source in detailed_report.matched_sources[:10]])}
-            
-            <h2>Detailed Analysis</h2>
-            <p>{detailed_report.detailed_analysis.replace(chr(10), "<br/>")}</p>
-            
-            {"<h2>Affected Sections (Top Issues)</h2>" + "".join([f'<div class="affected-section"><strong>Section {section["section_number"]}</strong><br/><em>Text:</em> {section["text_snippet"]}...<br/><em>Similarity:</em> {section["similarity_score"]}<br/><em>Type:</em> {section["plagiarism_type"]}</div>' for section in detailed_report.affected_sections[:5]]) if detailed_report.affected_sections else ""}
-            
-            <h2>Recommendations</h2>
-            <div>
-                {"".join([f'<div class="recommendation"><strong>✓</strong> {rec}</div>' for rec in detailed_report.recommendations])}
-            </div>
-        </div>
-    </body>
-    </html>
-    """
-    return html
-
-# ==========================================
-# 5. API Endpoints & Utility
-# ==========================================
-
-def extract_text_from_pdf(file_bytes) -> str:
-    reader = PdfReader(file_bytes)
-    return "".join([page.extract_text() + "\n" for page in reader.pages if page.extract_text()])
-
-def chunk_text(text: str, words_per_chunk: int = 40) -> List[str]:
-    words = text.split()
-    chunks = []
-    for i in range(0, len(words), words_per_chunk - 10): 
-        chunk = " ".join(words[i:i + words_per_chunk])
-        if len(chunk.split()) > 15: 
-            chunks.append(chunk)
-    return chunks
-
-@app.post("/scan-paper", response_model=PlagiarismReport)
-async def scan_paper(file: UploadFile = File(...)):
-    text = extract_text_from_pdf(file.file)
-    total_words = len(text.split())
-    
-    if total_words == 0:
-        raise HTTPException(status_code=400, detail="Could not extract text. Is this a scanned PDF?")
-
-    chunks = chunk_text(text)
-    
-    # Cap chunks for safety during testing (remove in production)
-    if len(chunks) > 20:
-        chunks = chunks[:20]
-
-    detailed_reports = []
-    plagiarized_word_count = 0
-    
-    for chunk in chunks:
-        report = analyze_chunk(chunk)
-        detailed_reports.append(report)
-        
-        if report.is_plagiarized:
-            plagiarized_word_count += len(chunk.split())
-
-    plagiarized_word_count = min(plagiarized_word_count, total_words) 
-    overall_score = (plagiarized_word_count / total_words) * 100
-    
-    # Determine severity level
-    if overall_score < 15:
-        severity = "Low"
-    elif overall_score < 30:
-        severity = "Medium"
-    elif overall_score < 50:
-        severity = "High"
-    else:
-        severity = "Very High"
-
-    return PlagiarismReport(
-        filename=file.filename,
-        total_words=total_words,
-        plagiarized_words=plagiarized_word_count,
-        overall_plagiarism_score=round(overall_score, 2),
-        severity_level=severity,
-        details=detailed_reports
-    )
-
-@app.post("/generate-detailed-report")
-async def generate_detailed_report(file: UploadFile = File(...)):
-    """Generate comprehensive plagiarism report with LLM analysis"""
-    text = extract_text_from_pdf(file.file)
-    total_words = len(text.split())
-    
-    if total_words == 0:
-        raise HTTPException(status_code=400, detail="Could not extract text. Is this a scanned PDF?")
-
-    chunks = chunk_text(text)
-    
-    # Cap chunks
-    if len(chunks) > 20:
-        chunks = chunks[:20]
-
-    detailed_reports = []
-    plagiarized_word_count = 0
-    
-    for chunk in chunks:
-        report = analyze_chunk(chunk)
-        detailed_reports.append(report)
-        
-        if report.is_plagiarized:
-            plagiarized_word_count += len(chunk.split())
-
-    plagiarized_word_count = min(plagiarized_word_count, total_words)
-    overall_score = (plagiarized_word_count / total_words) * 100
-
-    # Generate detailed report with LLM analysis
-    detailed_report = generate_detailed_report_with_llm(
-        filename=file.filename,
-        match_reports=detailed_reports,
-        total_words=total_words,
-        overall_score=overall_score
-    )
-    
-    return format_report_json(detailed_report)
-
-@app.post("/report/text")
-async def report_text(file: UploadFile = File(...)):
-    """Generate detailed plagiarism report as plain text"""
-    text = extract_text_from_pdf(file.file)
-    total_words = len(text.split())
-    
-    if total_words == 0:
-        raise HTTPException(status_code=400, detail="Could not extract text. Is this a scanned PDF?")
-
-    chunks = chunk_text(text)
-    
-    if len(chunks) > 20:
-        chunks = chunks[:20]
-
-    detailed_reports = []
-    plagiarized_word_count = 0
-    
-    for chunk in chunks:
-        report = analyze_chunk(chunk)
-        detailed_reports.append(report)
-        
-        if report.is_plagiarized:
-            plagiarized_word_count += len(chunk.split())
-
-    plagiarized_word_count = min(plagiarized_word_count, total_words)
-    overall_score = (plagiarized_word_count / total_words) * 100
-
-    # Generate detailed report
-    detailed_report = generate_detailed_report_with_llm(
-        filename=file.filename,
-        match_reports=detailed_reports,
-        total_words=total_words,
-        overall_score=overall_score
-    )
-    
-    from fastapi.responses import PlainTextResponse
-    return PlainTextResponse(format_report_text(detailed_report))
-
-@app.post("/report/html")
-async def report_html(file: UploadFile = File(...)):
-    """Generate detailed plagiarism report as HTML"""
-    text = extract_text_from_pdf(file.file)
-    total_words = len(text.split())
-    
-    if total_words == 0:
-        raise HTTPException(status_code=400, detail="Could not extract text. Is this a scanned PDF?")
-
-    chunks = chunk_text(text)
-    
-    if len(chunks) > 20:
-        chunks = chunks[:20]
-
-    detailed_reports = []
-    plagiarized_word_count = 0
-    
-    for chunk in chunks:
-        report = analyze_chunk(chunk)
-        detailed_reports.append(report)
-        
-        if report.is_plagiarized:
-            plagiarized_word_count += len(chunk.split())
-
-    plagiarized_word_count = min(plagiarized_word_count, total_words)
-    overall_score = (plagiarized_word_count / total_words) * 100
-
-    # Generate detailed report
-    detailed_report = generate_detailed_report_with_llm(
-        filename=file.filename,
-        match_reports=detailed_reports,
-        total_words=total_words,
-        overall_score=overall_score
-    )
-    
-    from fastapi.responses import HTMLResponse
-    return HTMLResponse(format_report_html(detailed_report))
-
-@app.get("/")
-async def root():
-    return {
-        "message": "Pro Plagiarism Detector API",
-        "endpoints": {
-            "scan": "/scan-paper (POST - basic scan)",
-            "detailed_report": "/generate-detailed-report (POST - JSON report with LLM analysis)",
-            "text_report": "/report/text (POST - plain text report)",
-            "html_report": "/report/html (POST - HTML report)"
-        }
-    }
-
-if __name__ == "__main__":
+import os
+import time
+import requests
+import random
+import re
+from difflib import SequenceMatcher
+from typing import List, Optional, Dict, Any
+from urllib.parse import quote_plus
+
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+from pydantic import BaseModel
+from PyPDF2 import PdfReader
+
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate
+
+# ==========================================
+# 1. Environment & API Setup
+# ==========================================
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+SERPER_API_KEY = os.getenv("SERPER_API_KEY")
+SEMANTIC_SCHOLAR_API_KEY = os.getenv("SEMANTIC_SCHOLAR_API_KEY")
+SEMANTIC_SCHOLAR_BASE_URL = "https://api.semanticscholar.org/graph/v1"
+SEMANTIC_SCHOLAR_MIN_INTERVAL_SECONDS = 1.2
+SEMANTIC_SCHOLAR_MAX_RETRIES = 4
+
+if not GROQ_API_KEY or not SERPER_API_KEY:
+    print("WARNING: GROQ_API_KEY or SERPER_API_KEY is missing!")
+
+llm = ChatGroq(model="openai/gpt-oss-120b", temperature=0.1)
+
+# Basic Memory Cache to maintain API efficiency (as promised in the application)
+query_cache = {}
+semantic_query_cache: Dict[str, List[Dict[str, str]]] = {}
+_last_semantic_scholar_call_ts = 0.0
+
+# ==========================================
+# 2. Pydantic Models
+# ==========================================
+class MatchReport(BaseModel):
+    chunk_text: str
+    is_plagiarized: bool
+    plagiarism_type: Optional[str] = None 
+    source_url: Optional[str] = None
+    source_type: Optional[str] = None # "Academic" or "Web"
+    similarity_score: float
+
+class PlagiarismReport(BaseModel):
+    filename: str
+    total_words: int
+    plagiarized_words: int
+    overall_plagiarism_score: float
+    severity_level: str  # Low, Medium, High, Very High
+    details: List[MatchReport]
+
+class DetailedPlagiarismReport(BaseModel):
+    """Comprehensive report generated by LLM"""
+    filename: str
+    scan_timestamp: str
+    executive_summary: str
+    overall_score: float
+    severity_level: str
+    matched_sources: List[Dict[str, Any]]
+    key_findings: List[str]
+    plagiarism_breakdown: Dict[str, Any]  # Types and percentages
+    detailed_analysis: str  # LLM-generated detailed analysis
+    affected_sections: List[Dict[str, Any]]  # Which parts are problematic
+    recommendations: List[str]
+    academic_integrity_risk: str  # Assessment level
+
+app = FastAPI(title="Pro Plagiarism Detector (Turnitin Clone)")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ==========================================
+# 3. Agent Tools: Serper & Semantic Scholar
+# ==========================================
+
+def _semantic_scholar_headers() -> Dict[str, str]:
+    headers: Dict[str, str] = {}
+    if SEMANTIC_SCHOLAR_API_KEY:
+        # API key must be sent in x-api-key header.
+        headers["x-api-key"] = SEMANTIC_SCHOLAR_API_KEY
+    return headers
+
+
+def _semantic_scholar_get(path: str, params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+    global _last_semantic_scholar_call_ts
+    filtered_params = {k: v for k, v in (params or {}).items() if v is not None}
+
+    for attempt in range(SEMANTIC_SCHOLAR_MAX_RETRIES):
+        elapsed = time.time() - _last_semantic_scholar_call_ts
+        if elapsed < SEMANTIC_SCHOLAR_MIN_INTERVAL_SECONDS:
+            time.sleep(SEMANTIC_SCHOLAR_MIN_INTERVAL_SECONDS - elapsed)
+
+        response = requests.get(
+            f"{SEMANTIC_SCHOLAR_BASE_URL}{path}",
+            headers=_semantic_scholar_headers(),
+            params=filtered_params,
+            timeout=20,
+        )
+        _last_semantic_scholar_call_ts = time.time()
+
+        if response.status_code == 429 and attempt < SEMANTIC_SCHOLAR_MAX_RETRIES - 1:
+            retry_after = response.headers.get("Retry-After")
+            if retry_after and retry_after.isdigit():
+                wait_seconds = float(retry_after)
+            else:
+                wait_seconds = (2 ** attempt) + random.uniform(0.2, 0.7)
+            time.sleep(wait_seconds)
+            continue
+
+        response.raise_for_status()
+        return response.json()
+
+    raise requests.HTTPError("Semantic Scholar request failed after retries")
+
+
+def _semantic_scholar_post(path: str, body: Dict[str, Any], params: Optional[Dict[str, Any]] = None) -> Any:
+    global _last_semantic_scholar_call_ts
+    filtered_params = {k: v for k, v in (params or {}).items() if v is not None}
+
+    for attempt in range(SEMANTIC_SCHOLAR_MAX_RETRIES):
+        elapsed = time.time() - _last_semantic_scholar_call_ts
+        if elapsed < SEMANTIC_SCHOLAR_MIN_INTERVAL_SECONDS:
+            time.sleep(SEMANTIC_SCHOLAR_MIN_INTERVAL_SECONDS - elapsed)
+
+        response = requests.post(
+            f"{SEMANTIC_SCHOLAR_BASE_URL}{path}",
+            headers=_semantic_scholar_headers(),
+            params=filtered_params,
+            json=body,
+            timeout=25,
+        )
+        _last_semantic_scholar_call_ts = time.time()
+
+        if response.status_code == 429 and attempt < SEMANTIC_SCHOLAR_MAX_RETRIES - 1:
+            retry_after = response.headers.get("Retry-After")
+            if retry_after and retry_after.isdigit():
+                wait_seconds = float(retry_after)
+            else:
+                wait_seconds = (2 ** attempt) + random.uniform(0.2, 0.7)
+            time.sleep(wait_seconds)
+            continue
+
+        response.raise_for_status()
+        return response.json()
+
+    raise requests.HTTPError("Semantic Scholar request failed after retries")
+
+
+def s2_paper_autocomplete(query: str) -> Dict[str, Any]:
+    return _semantic_scholar_get("/paper/autocomplete", {"query": query[:100]})
+
+
+def s2_paper_batch(ids: List[str], fields: Optional[str] = None) -> Any:
+    return _semantic_scholar_post("/paper/batch", {"ids": ids[:500]}, {"fields": fields})
+
+
+def s2_paper_search(
+    query: str,
+    fields: Optional[str] = None,
+    limit: int = 100,
+    offset: int = 0,
+    year: Optional[str] = None,
+    fields_of_study: Optional[str] = None,
+    open_access_pdf: bool = False,
+) -> Dict[str, Any]:
+    params: Dict[str, Any] = {
+        "query": query,
+        "fields": fields,
+        "limit": min(max(limit, 1), 100),
+        "offset": max(offset, 0),
+        "year": year,
+        "fieldsOfStudy": fields_of_study,
+    }
+    if open_access_pdf:
+        params["openAccessPdf"] = ""
+    return _semantic_scholar_get("/paper/search", params)
+
+
+def s2_paper_search_bulk(
+    query: str,
+    fields: Optional[str] = None,
+    token: Optional[str] = None,
+    sort: Optional[str] = None,
+) -> Dict[str, Any]:
+    return _semantic_scholar_get(
+        "/paper/search/bulk",
+        {
+            "query": query,
+            "fields": fields,
+            "token": token,
+            "sort": sort,
+        },
+    )
+
+
+def s2_paper_search_match(query: str, fields: Optional[str] = None) -> Dict[str, Any]:
+    return _semantic_scholar_get("/paper/search/match", {"query": query, "fields": fields})
+
+
+def s2_paper_details(paper_id: str, fields: Optional[str] = None) -> Dict[str, Any]:
+    safe_id = quote_plus(paper_id)
+    return _semantic_scholar_get(f"/paper/{safe_id}", {"fields": fields})
+
+
+def s2_paper_authors(
+    paper_id: str,
+    fields: Optional[str] = None,
+    limit: int = 100,
+    offset: int = 0,
+) -> Dict[str, Any]:
+    safe_id = quote_plus(paper_id)
+    return _semantic_scholar_get(
+        f"/paper/{safe_id}/authors",
+        {"fields": fields, "limit": min(max(limit, 1), 1000), "offset": max(offset, 0)},
+    )
+
+
+def s2_paper_citations(
+    paper_id: str,
+    fields: Optional[str] = None,
+    limit: int = 100,
+    offset: int = 0,
+    publication_date_or_year: Optional[str] = None,
+) -> Dict[str, Any]:
+    safe_id = quote_plus(paper_id)
+    return _semantic_scholar_get(
+        f"/paper/{safe_id}/citations",
+        {
+            "fields": fields,
+            "limit": min(max(limit, 1), 1000),
+            "offset": max(offset, 0),
+            "publicationDateOrYear": publication_date_or_year,
+        },
+    )
+
+
+def s2_paper_references(
+    paper_id: str,
+    fields: Optional[str] = None,
+    limit: int = 100,
+    offset: int = 0,
+) -> Dict[str, Any]:
+    safe_id = quote_plus(paper_id)
+    return _semantic_scholar_get(
+        f"/paper/{safe_id}/references",
+        {"fields": fields, "limit": min(max(limit, 1), 1000), "offset": max(offset, 0)},
+    )
+
+
+def s2_author_batch(ids: List[str], fields: Optional[str] = None) -> Any:
+    return _semantic_scholar_post("/author/batch", {"ids": ids[:1000]}, {"fields": fields})
+
+
+def s2_author_search(
+    query: str,
+    fields: Optional[str] = None,
+    limit: int = 100,
+    offset: int = 0,
+) -> Dict[str, Any]:
+    return _semantic_scholar_get(
+        "/author/search",
+        {
+            "query": query,
+            "fields": fields,
+            "limit": min(max(limit, 1), 1000),
+            "offset": max(offset, 0),
+        },
+    )
+
+
+def s2_author_details(author_id: str, fields: Optional[str] = None) -> Dict[str, Any]:
+    safe_id = quote_plus(author_id)
+    return _semantic_scholar_get(f"/author/{safe_id}", {"fields": fields})
+
+
+def s2_author_papers(
+    author_id: str,
+    fields: Optional[str] = None,
+    limit: int = 100,
+    offset: int = 0,
+    publication_date_or_year: Optional[str] = None,
+) -> Dict[str, Any]:
+    safe_id = quote_plus(author_id)
+    return _semantic_scholar_get(
+        f"/author/{safe_id}/papers",
+        {
+            "fields": fields,
+            "limit": min(max(limit, 1), 1000),
+            "offset": max(offset, 0),
+            "publicationDateOrYear": publication_date_or_year,
+        },
+    )
+
+
+def s2_snippet_search(
+    query: str,
+    fields: Optional[str] = None,
+    limit: int = 10,
+    year: Optional[str] = None,
+    fields_of_study: Optional[str] = None,
+) -> Dict[str, Any]:
+    return _semantic_scholar_get(
+        "/snippet/search",
+        {
+            "query": query,
+            "fields": fields,
+            "limit": min(max(limit, 1), 1000),
+            "year": year,
+            "fieldsOfStudy": fields_of_study,
+        },
+    )
+
+
+def build_search_query(text: str, max_terms: int = 10) -> str:
+    """Builds a compact keyword query to improve search recall and reduce noisy long queries."""
+    stopwords = {
+        "the", "and", "for", "that", "with", "this", "from", "into", "our", "their",
+        "were", "have", "has", "had", "been", "are", "was", "will", "would", "can",
+        "could", "should", "about", "through", "using", "based", "than", "then", "also",
+        "such", "these", "those", "while", "where", "when", "what", "which", "who",
+    }
+    words = re.findall(r"[A-Za-z0-9]+", text.lower())
+    keywords = [w for w in words if len(w) > 2 and w not in stopwords]
+    return " ".join(keywords[:max_terms]) if keywords else " ".join(words[:max_terms])
+
+def search_google_serper(query: str) -> List[Dict]:
+    """Searches the open web using Google Serper API."""
+    url = "https://google.serper.dev/search"
+    payload = {"q": query}
+    headers = {
+        'X-API-KEY': SERPER_API_KEY,
+        'Content-Type': 'application/json'
+    }
+    
+    try:
+        response = requests.post(url, headers=headers, json=payload)
+        response.raise_for_status()
+        data = response.json()
+        
+        results = []
+        for item in data.get("organic", [])[:3]: # Top 3 web results
+            results.append({
+                "text": item.get("snippet", ""),
+                "url": item.get("link", ""),
+                "source_type": "Web (Google)"
+            })
+        return results
+    except Exception as e:
+        print(f"Serper Error: {e}")
+        return []
+
+def search_semantic_scholar(query: str) -> List[Dict]:
+    """Searches academic papers using Semantic Scholar API."""
+    prepared_query = build_search_query(query, max_terms=10)
+    normalized_query = " ".join(prepared_query.split()).lower()
+    if normalized_query in semantic_query_cache:
+        return semantic_query_cache[normalized_query]
+
+    try:
+        results = []
+
+        # Try snippet search first because it returns passage-level text better suited for chunk comparison.
+        snippet_data = s2_snippet_search(
+            query=prepared_query,
+            fields="snippet.text,snippet.snippetKind",
+            limit=3,
+        )
+        for item in snippet_data.get("data", []):
+            snippet = item.get("snippet", {})
+            paper = item.get("paper", {})
+            snippet_text = snippet.get("text", "")
+            if snippet_text:
+                corpus_id = paper.get("corpusId")
+                paper_url = f"https://www.semanticscholar.org/paper/{corpus_id}" if corpus_id else None
+                results.append({
+                    "text": snippet_text,
+                    "url": paper_url,
+                    "source_type": "Academic (Semantic Scholar Snippet)",
+                })
+
+        # Keep paper abstract search as fallback/secondary source.
+        data = s2_paper_search(
+            query=prepared_query,
+            limit=2,
+            fields="title,abstract,url",
+        )
+
+        for item in data.get("data", []):
+            if item.get("abstract"): # Only keep if abstract exists to compare text
+                results.append({
+                    "text": item["abstract"],
+                    "url": item.get("url", f"https://www.semanticscholar.org/paper/{item['paperId']}"),
+                    "source_type": "Academic (Semantic Scholar)"
+                })
+        semantic_query_cache[normalized_query] = results
+        return results
+    except Exception as e:
+        print(f"Semantic Scholar Error: {e}")
+        return []
+
+def aggregate_search(query: str) -> List[Dict]:
+    """Combines Academic and Web sources and implements caching."""
+    # Use the first 15 words to make the search query efficient
+    search_query = " ".join(query.split()[:15])
+    
+    if search_query in query_cache:
+        return query_cache[search_query]
+
+    # Run both searches
+    web_results = search_google_serper(search_query)
+    academic_results = search_semantic_scholar(search_query)
+    
+    combined = web_results + academic_results
+    query_cache[search_query] = combined # Save to cache
+    
+    # Sleep to respect rate limits
+    time.sleep(1)
+    
+    return combined
+
+# ==========================================
+# 4. Core Comparison Logic
+# ==========================================
+
+def calculate_exact_similarity(text1: str, text2: str) -> float:
+    return SequenceMatcher(None, text1.lower(), text2.lower()).ratio()
+
+def check_paraphrasing_with_llm(chunk: str, source_text: str) -> bool:
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", "You are an expert academic plagiarism detector. Determine if TEXT A is a direct paraphrase, stolen idea, or highly similar structure to TEXT B. Ignore generic academic phrases like 'In this paper we demonstrate'. Respond ONLY with 'YES' or 'NO'."),
+        ("user", "TEXT A: {chunk}\n\nTEXT B: {source_text}")
+    ])
+    chain = prompt | llm
+    response = chain.invoke({"chunk": chunk, "source_text": source_text})
+    return "YES" in response.content.upper()
+
+def generate_detailed_report_with_llm(
+    filename: str, 
+    match_reports: List[MatchReport], 
+    total_words: int, 
+    overall_score: float
+) -> DetailedPlagiarismReport:
+    """Generate a comprehensive report using LLM analysis"""
+    from datetime import datetime
+    
+    # 1. Aggregate data for analysis
+    plagiarized_reports = [r for r in match_reports if r.is_plagiarized]
+    plagiarism_types = {}
+    sources_by_type = {"Academic": [], "Web": []}
+    
+    for report in plagiarized_reports:
+        ptype = report.plagiarism_type or "Unknown"
+        plagiarism_types[ptype] = plagiarism_types.get(ptype, 0) + 1
+        
+        if report.source_type:
+            if "Academic" in report.source_type:
+                if report.source_url not in sources_by_type["Academic"]:
+                    sources_by_type["Academic"].append({
+                        "url": report.source_url,
+                        "type": report.source_type,
+                        "max_similarity": report.similarity_score
+                    })
+            else:
+                if report.source_url not in sources_by_type["Web"]:
+                    sources_by_type["Web"].append({
+                        "url": report.source_url,
+                        "type": report.source_type,
+                        "max_similarity": report.similarity_score
+                    })
+    
+    # 2. Determine severity level
+    if overall_score < 15:
+        severity = "Low"
+        risk_level = "Minimal - Normal citation variations detected"
+    elif overall_score < 30:
+        severity = "Medium"
+        risk_level = "Moderate - Multiple sources match detected"
+    elif overall_score < 50:
+        severity = "High"
+        risk_level = "Significant - Substantial plagiarism detected"
+    else:
+        severity = "Very High"
+        risk_level = "Critical - Extensive plagiarism detected"
+    
+    # 3. Use LLM to generate detailed analysis
+    plagiarism_context = f"""
+Document: {filename}
+Total Words: {total_words}
+Plagiarism Score: {overall_score}%
+Plagiarism Types Found: {plagiarism_types}
+Academic Matches: {len(sources_by_type['Academic'])}
+Web Matches: {len(sources_by_type['Web'])}
+
+Suspicious Sections (samples):
+{chr(10).join([f"- {r.chunk_text[:100]}..." for r in plagiarized_reports[:5]])}
+"""
+    
+    analysis_prompt = ChatPromptTemplate.from_messages([
+        ("system", """You are an expert academic integrity analyzer and plagiarism report generator.
+        Generate a professional, detailed plagiarism analysis report.
+        Focus on: severity assessment, academic integrity concerns, specific problem areas, and recommendations.
+        Be thorough but concise."""),
+        ("user", """Create a detailed plagiarism analysis for this document:
+
+{plagiarism_context}
+
+Provide:
+1. Executive Summary (2-3 sentences)
+2. Key Findings (3-4 bullet points)
+3. Detailed Analysis (2-3 paragraphs explaining the plagiarism pattern)
+4. Recommendations (3-4 specific actions to remediate)
+
+Format clearly with section headers.""")
+    ])
+
+    chain = analysis_prompt | llm
+    llm_response = chain.invoke({"plagiarism_context": plagiarism_context})
+    llm_analysis = llm_response.content
+    
+    # 4. Extract findings from LLM response
+    lines = llm_analysis.split('\n')
+    key_findings = []
+    recommendations = []
+    detailed_analysis = ""
+    
+    in_findings = False
+    in_recommendations = False
+    
+    for line in lines:
+        if 'Key Findings' in line:
+            in_findings = True
+            in_recommendations = False
+        elif 'Recommendations' in line:
+            in_findings = False
+            in_recommendations = True
+        elif 'Detailed Analysis' in line or 'Analysis' in line:
+            in_findings = False
+            in_recommendations = False
+        elif in_findings and line.strip().startswith(('-', '*', '•')):
+            key_findings.append(line.strip().lstrip('-*•').strip())
+        elif in_recommendations and line.strip().startswith(('-', '*', '•')):
+            recommendations.append(line.strip().lstrip('-*•').strip())
+        elif not in_findings and not in_recommendations and line.strip():
+            detailed_analysis += line + "\n"
+    
+    if not key_findings:
+        key_findings = [
+            f"Overall plagiarism score: {overall_score}%",
+            f"Primary plagiarism type: {max(plagiarism_types.keys(), key=plagiarism_types.get) if plagiarism_types else 'Not detected'}",
+            f"Multiple sources detected: {len(sources_by_type['Academic']) + len(sources_by_type['Web'])} sources"
+        ]
+    
+    if not recommendations:
+        recommendations = [
+            "Properly cite all sources according to your institution's guidelines",
+            "Use quotation marks for direct quotes and provide page numbers",
+            "Paraphrase content properly and cite original sources",
+            "Use plagiarism detection tools during the writing process"
+        ]
+    
+    # 5. Affected sections
+    affected_sections = []
+    for i, report in enumerate(plagiarized_reports[:10]):
+        affected_sections.append({
+            "section_number": i + 1,
+            "text_snippet": report.chunk_text[:150],
+            "similarity_score": report.similarity_score,
+            "plagiarism_type": report.plagiarism_type,
+            "source": report.source_url,
+            "source_type": report.source_type
+        })
+    
+    return DetailedPlagiarismReport(
+        filename=filename,
+        scan_timestamp=datetime.now().isoformat(),
+        executive_summary=llm_analysis.split('\n')[0] if llm_analysis else f"Document contains {overall_score}% plagiarized content",
+        overall_score=round(overall_score, 2),
+        severity_level=severity,
+        matched_sources=sources_by_type["Academic"] + sources_by_type["Web"],
+        key_findings=key_findings,
+        plagiarism_breakdown={
+            "total_plagiarism_percentage": round(overall_score, 2),
+            "types": plagiarism_types,
+            "academic_sources": len(sources_by_type["Academic"]),
+            "web_sources": len(sources_by_type["Web"])
+        },
+        detailed_analysis=detailed_analysis or llm_analysis,
+        affected_sections=affected_sections,
+        recommendations=recommendations,
+        academic_integrity_risk=risk_level
+    )
+
+def analyze_chunk(chunk: str) -> MatchReport:
+    search_results = aggregate_search(chunk)
+    
+    best_score = 0.0
+    best_url = None
+    best_source_type = None
+    plagiarism_type = None
+    is_plagiarized = False
+    
+    for result in search_results:
+        source_text = result['text']
+        
+        # 1. Math/Deterministic Check
+        exact_sim = calculate_exact_similarity(chunk, source_text)
+        
+        if exact_sim > best_score:
+            best_score = exact_sim
+            best_url = result['url']
+            best_source_type = result['source_type']
+            
+        if exact_sim > 0.50: # Lowered to 50% because we are comparing against abstracts/snippets
+            is_plagiarized = True
+            plagiarism_type = "Exact/Heavy Match"
+            break 
+            
+        # 2. Agentic Check for Mosaic Plagiarism
+        elif exact_sim > 0.25: 
+            if check_paraphrasing_with_llm(chunk, source_text):
+                is_plagiarized = True
+                plagiarism_type = "Paraphrased Match (Mosaic)"
+                best_url = result['url']
+                best_source_type = result['source_type']
+                best_score = max(best_score, 0.85) 
+                break
+                
+    return MatchReport(
+        chunk_text=chunk,
+        is_plagiarized=is_plagiarized,
+        plagiarism_type=plagiarism_type,
+        source_url=best_url,
+        source_type=best_source_type,
+        similarity_score=round(best_score, 2)
+    )
+
+# ==========================================
+# 6. Report Formatting Functions
+# ==========================================
+
+def format_report_json(detailed_report: DetailedPlagiarismReport) -> Dict[str, Any]:
+    """Format report as JSON"""
+    return {
+        "filename": detailed_report.filename,
+        "scan_timestamp": detailed_report.scan_timestamp,
+        # Backward-compatible top-level fields expected by existing clients.
+        "overall_score": detailed_report.overall_score,
+        "severity_level": detailed_report.severity_level,
+        "academic_integrity_risk": detailed_report.academic_integrity_risk,
+        "summary": {
+            "overall_plagiarism_score": detailed_report.overall_score,
+            "severity_level": detailed_report.severity_level,
+            "academic_integrity_risk": detailed_report.academic_integrity_risk
+        },
+        "executive_summary": detailed_report.executive_summary,
+        "key_findings": detailed_report.key_findings,
+        "plagiarism_breakdown": detailed_report.plagiarism_breakdown,
+        "matched_sources": detailed_report.matched_sources,
+        "affected_sections": detailed_report.affected_sections,
+        "detailed_analysis": detailed_report.detailed_analysis,
+        "recommendations": detailed_report.recommendations
+    }
+
+def format_report_text(detailed_report: DetailedPlagiarismReport) -> str:
+    """Format report as plain text"""
+    report = "=" * 80 + "\n"
+    report += "DETAILED PLAGIARISM DETECTION REPORT\n"
+    report += "=" * 80 + "\n\n"
+    
+    report += f"FILE: {detailed_report.filename}\n"
+    report += f"SCAN DATE: {detailed_report.scan_timestamp}\n"
+    report += "-" * 80 + "\n\n"
+    
+    report += "SUMMARY\n"
+    report += "-" * 80 + "\n"
+    report += f"Overall Plagiarism Score: {detailed_report.overall_score}%\n"
+    report += f"Severity Level: {detailed_report.severity_level}\n"
+    report += f"Academic Integrity Risk: {detailed_report.academic_integrity_risk}\n\n"
+    
+    report += "EXECUTIVE SUMMARY\n"
+    report += "-" * 80 + "\n"
+    report += f"{detailed_report.executive_summary}\n\n"
+    
+    report += "KEY FINDINGS\n"
+    report += "-" * 80 + "\n"
+    for i, finding in enumerate(detailed_report.key_findings, 1):
+        report += f"{i}. {finding}\n"
+    report += "\n"
+    
+    report += "PLAGIARISM BREAKDOWN\n"
+    report += "-" * 80 + "\n"
+    report += f"Total Plagiarism %: {detailed_report.plagiarism_breakdown['total_plagiarism_percentage']}%\n"
+    report += f"Academic Sources: {detailed_report.plagiarism_breakdown['academic_sources']}\n"
+    report += f"Web Sources: {detailed_report.plagiarism_breakdown['web_sources']}\n"
+    if detailed_report.plagiarism_breakdown.get('types'):
+        report += "Types Detected:\n"
+        for ptype, count in detailed_report.plagiarism_breakdown['types'].items():
+            report += f"  - {ptype}: {count} instances\n"
+    report += "\n"
+    
+    report += "MATCHED SOURCES\n"
+    report += "-" * 80 + "\n"
+    if detailed_report.matched_sources:
+        for i, source in enumerate(detailed_report.matched_sources[:10], 1):
+            report += f"{i}. URL: {source.get('url', 'N/A')}\n"
+            report += f"   Type: {source.get('type', 'N/A')}\n"
+            report += f"   Similarity: {source.get('max_similarity', 'N/A')}\n\n"
+    else:
+        report += "No sources matched.\n\n"
+    
+    report += "DETAILED ANALYSIS\n"
+    report += "-" * 80 + "\n"
+    report += f"{detailed_report.detailed_analysis}\n\n"
+    
+    if detailed_report.affected_sections:
+        report += "AFFECTED SECTIONS (Top Issues)\n"
+        report += "-" * 80 + "\n"
+        for section in detailed_report.affected_sections[:5]:
+            report += f"\nSection {section['section_number']}:\n"
+            report += f"Text Snippet: {section['text_snippet']}\n"
+            report += f"Similarity Score: {section['similarity_score']}\n"
+            report += f"Plagiarism Type: {section['plagiarism_type']}\n"
+            report += f"Source: {section['source']}\n"
+    report += "\n"
+    
+    report += "RECOMMENDATIONS\n"
+    report += "-" * 80 + "\n"
+    for i, rec in enumerate(detailed_report.recommendations, 1):
+        report += f"{i}. {rec}\n"
+    report += "\n"
+    
+    report += "=" * 80 + "\n"
+    report += "End of Report\n"
+    report += "=" * 80 + "\n"
+    
+    return report
+
+def format_report_html(detailed_report: DetailedPlagiarismReport) -> str:
+    """Format report as HTML"""
+    html = f"""
+    <!DOCTYPE html>
+    <html lang="en">
+    <head>
+        <meta charset="UTF-8">
+        <meta name="viewport" content="width=device-width, initial-scale=1.0">
+        <title>Plagiarism Detection Report - {detailed_report.filename}</title>
+        <style>
+            body {{ font-family: Arial, sans-serif; margin: 40px; background-color: #f5f5f5; }}
+            .container {{ background-color: white; padding: 30px; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); }}
+            h1 {{ color: #333; border-bottom: 3px solid #2196F3; padding-bottom: 10px; }}
+            h2 {{ color: #2196F3; margin-top: 30px; }}
+            .summary {{ background-color: #f0f7ff; padding: 15px; border-left: 4px solid #2196F3; margin: 20px 0; }}
+            .score {{ font-size: 24px; font-weight: bold; color: #d32f2f; }}
+            .severity-low {{ color: #4caf50; }}
+            .severity-medium {{ color: #ff9800; }}
+            .severity-high {{ color: #f44336; }}
+            .severity-very-high {{ color: #c41c3b; }}
+            .findings {{ background-color: #fff3e0; padding: 15px; border-left: 4px solid #ff9800; }}
+            .source-item {{ background-color: #f5f5f5; padding: 10px; margin: 10px 0; border-radius: 4px; }}
+            .recommendation {{ background-color: #e8f5e9; padding: 10px; margin: 10px 0; border-left: 3px solid #4caf50; }}
+            table {{ width: 100%; border-collapse: collapse; margin: 15px 0; }}
+            th, td {{ padding: 10px; text-align: left; border-bottom: 1px solid #ddd; }}
+            th {{ background-color: #2196F3; color: white; }}
+            .affected-section {{ background-color: #fce4ec; padding: 15px; margin: 10px 0; border-radius: 4px; }}
+        </style>
+    </head>
+    <body>
+        <div class="container">
+            <h1>🔍 Plagiarism Detection Report</h1>
+            
+            <div class="summary">
+                <p><strong>File:</strong> {detailed_report.filename}</p>
+                <p><strong>Scan Date:</strong> {detailed_report.scan_timestamp}</p>
+                <p><strong>Overall Plagiarism Score:</strong> <span class="score">{detailed_report.overall_score}%</span></p>
+                <p><strong>Severity Level:</strong> <span class="severity-{detailed_report.severity_level.lower().replace(' ', '-')}">{detailed_report.severity_level}</span></p>
+                <p><strong>Academic Integrity Risk:</strong> {detailed_report.academic_integrity_risk}</p>
+            </div>
+            
+            <h2>Executive Summary</h2>
+            <p>{detailed_report.executive_summary}</p>
+            
+            <h2>Key Findings</h2>
+            <div class="findings">
+                <ul>
+                    {"".join([f"<li>{finding}</li>" for finding in detailed_report.key_findings])}
+                </ul>
+            </div>
+            
+            <h2>Plagiarism Breakdown</h2>
+            <table>
+                <tr>
+                    <th>Category</th>
+                    <th>Value</th>
+                </tr>
+                <tr>
+                    <td>Total Plagiarism %</td>
+                    <td>{detailed_report.plagiarism_breakdown['total_plagiarism_percentage']}%</td>
+                </tr>
+                <tr>
+                    <td>Academic Sources</td>
+                    <td>{detailed_report.plagiarism_breakdown['academic_sources']}</td>
+                </tr>
+                <tr>
+                    <td>Web Sources</td>
+                    <td>{detailed_report.plagiarism_breakdown['web_sources']}</td>
+                </tr>
+            </table>
+            
+            <h2>Matched Sources</h2>
+            {"".join([f'<div class="source-item"><strong>{source.get("type", "Unknown")}</strong><br/><a href="{source.get("url", "#")}" target="_blank">{source.get("url", "N/A")}</a><br/>Similarity: {source.get("max_similarity", "N/A")}</div>' for source in detailed_report.matched_sources[:10]])}
+            
+            <h2>Detailed Analysis</h2>
+            <p>{detailed_report.detailed_analysis.replace(chr(10), "<br/>")}</p>
+            
+            {"<h2>Affected Sections (Top Issues)</h2>" + "".join([f'<div class="affected-section"><strong>Section {section["section_number"]}</strong><br/><em>Text:</em> {section["text_snippet"]}...<br/><em>Similarity:</em> {section["similarity_score"]}<br/><em>Type:</em> {section["plagiarism_type"]}</div>' for section in detailed_report.affected_sections[:5]]) if detailed_report.affected_sections else ""}
+            
+            <h2>Recommendations</h2>
+            <div>
+                {"".join([f'<div class="recommendation"><strong>✓</strong> {rec}</div>' for rec in detailed_report.recommendations])}
+            </div>
+        </div>
+    </body>
+    </html>
+    """
+    return html
+
+# ==========================================
+# 5. API Endpoints & Utility
+# ==========================================
+
+def extract_text_from_pdf(file_bytes) -> str:
+    reader = PdfReader(file_bytes)
+    return "".join([page.extract_text() + "\n" for page in reader.pages if page.extract_text()])
+
+def chunk_text(text: str, words_per_chunk: int = 40) -> List[str]:
+    words = text.split()
+    chunks = []
+    for i in range(0, len(words), words_per_chunk - 10): 
+        chunk = " ".join(words[i:i + words_per_chunk])
+        if len(chunk.split()) > 15: 
+            chunks.append(chunk)
+    return chunks
+
+@app.post("/scan-paper", response_model=PlagiarismReport)
+async def scan_paper(file: UploadFile = File(...)):
+    text = extract_text_from_pdf(file.file)
+    total_words = len(text.split())
+    
+    if total_words == 0:
+        raise HTTPException(status_code=400, detail="Could not extract text. Is this a scanned PDF?")
+
+    chunks = chunk_text(text)
+    
+    # Cap chunks for safety during testing (remove in production)
+    if len(chunks) > 20:
+        chunks = chunks[:20]
+
+    detailed_reports = []
+    plagiarized_word_count = 0
+    
+    for chunk in chunks:
+        report = analyze_chunk(chunk)
+        detailed_reports.append(report)
+        
+        if report.is_plagiarized:
+            plagiarized_word_count += len(chunk.split())
+
+    plagiarized_word_count = min(plagiarized_word_count, total_words) 
+    overall_score = (plagiarized_word_count / total_words) * 100
+    
+    # Determine severity level
+    if overall_score < 15:
+        severity = "Low"
+    elif overall_score < 30:
+        severity = "Medium"
+    elif overall_score < 50:
+        severity = "High"
+    else:
+        severity = "Very High"
+
+    return PlagiarismReport(
+        filename=file.filename,
+        total_words=total_words,
+        plagiarized_words=plagiarized_word_count,
+        overall_plagiarism_score=round(overall_score, 2),
+        severity_level=severity,
+        details=detailed_reports
+    )
+
+@app.post("/generate-detailed-report")
+async def generate_detailed_report(file: UploadFile = File(...)):
+    """Generate comprehensive plagiarism report with LLM analysis"""
+    text = extract_text_from_pdf(file.file)
+    total_words = len(text.split())
+    
+    if total_words == 0:
+        raise HTTPException(status_code=400, detail="Could not extract text. Is this a scanned PDF?")
+
+    chunks = chunk_text(text)
+    
+    # Cap chunks
+    if len(chunks) > 20:
+        chunks = chunks[:20]
+
+    detailed_reports = []
+    plagiarized_word_count = 0
+    
+    for chunk in chunks:
+        report = analyze_chunk(chunk)
+        detailed_reports.append(report)
+        
+        if report.is_plagiarized:
+            plagiarized_word_count += len(chunk.split())
+
+    plagiarized_word_count = min(plagiarized_word_count, total_words)
+    overall_score = (plagiarized_word_count / total_words) * 100
+
+    # Generate detailed report with LLM analysis
+    detailed_report = generate_detailed_report_with_llm(
+        filename=file.filename,
+        match_reports=detailed_reports,
+        total_words=total_words,
+        overall_score=overall_score
+    )
+    
+    return format_report_json(detailed_report)
+
+@app.post("/report/text")
+async def report_text(file: UploadFile = File(...)):
+    """Generate detailed plagiarism report as plain text"""
+    text = extract_text_from_pdf(file.file)
+    total_words = len(text.split())
+    
+    if total_words == 0:
+        raise HTTPException(status_code=400, detail="Could not extract text. Is this a scanned PDF?")
+
+    chunks = chunk_text(text)
+    
+    if len(chunks) > 20:
+        chunks = chunks[:20]
+
+    detailed_reports = []
+    plagiarized_word_count = 0
+    
+    for chunk in chunks:
+        report = analyze_chunk(chunk)
+        detailed_reports.append(report)
+        
+        if report.is_plagiarized:
+            plagiarized_word_count += len(chunk.split())
+
+    plagiarized_word_count = min(plagiarized_word_count, total_words)
+    overall_score = (plagiarized_word_count / total_words) * 100
+
+    # Generate detailed report
+    detailed_report = generate_detailed_report_with_llm(
+        filename=file.filename,
+        match_reports=detailed_reports,
+        total_words=total_words,
+        overall_score=overall_score
+    )
+    
+    from fastapi.responses import PlainTextResponse
+    return PlainTextResponse(format_report_text(detailed_report))
+
+@app.post("/report/html")
+async def report_html(file: UploadFile = File(...)):
+    """Generate detailed plagiarism report as HTML"""
+    text = extract_text_from_pdf(file.file)
+    total_words = len(text.split())
+    
+    if total_words == 0:
+        raise HTTPException(status_code=400, detail="Could not extract text. Is this a scanned PDF?")
+
+    chunks = chunk_text(text)
+    
+    if len(chunks) > 20:
+        chunks = chunks[:20]
+
+    detailed_reports = []
+    plagiarized_word_count = 0
+    
+    for chunk in chunks:
+        report = analyze_chunk(chunk)
+        detailed_reports.append(report)
+        
+        if report.is_plagiarized:
+            plagiarized_word_count += len(chunk.split())
+
+    plagiarized_word_count = min(plagiarized_word_count, total_words)
+    overall_score = (plagiarized_word_count / total_words) * 100
+
+    # Generate detailed report
+    detailed_report = generate_detailed_report_with_llm(
+        filename=file.filename,
+        match_reports=detailed_reports,
+        total_words=total_words,
+        overall_score=overall_score
+    )
+    
+    from fastapi.responses import HTMLResponse
+    return HTMLResponse(format_report_html(detailed_report))
+
+@app.get("/")
+async def root():
+    return {
+        "message": "Pro Plagiarism Detector API",
+        "endpoints": {
+            "scan": "/scan-paper (POST - basic scan)",
+            "detailed_report": "/generate-detailed-report (POST - JSON report with LLM analysis)",
+            "text_report": "/report/text (POST - plain text report)",
+            "html_report": "/report/html (POST - HTML report)"
+        }
+    }
+
+if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=8000)