main.py

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)