model/feedback_engine.py

"""
Rule-Based Feedback Engine
Analyzes 3 cognitive aspects: Argument Structure, Reasoning, Evidence Use.
Uses NLP heuristics + keyword detection + IndoSBERT coherence results.
"""
import re
from model.config import DISCOURSE_MARKERS, REASONING_KEYWORDS, EVIDENCE_KEYWORDS


class FeedbackEngine:
    """
    Generates formative feedback for essays based on:
    1. Argument Structure — sentence count, discourse markers, paragraph flow
    2. Reasoning — causal keywords, semantic coherence, logical connectors
    3. Evidence Use — evidence keywords, specific examples, data references
    
    Each aspect produces a level (low/medium/high) and natural language feedback.
    """

    def __init__(self):
        """Initialize keyword lists from config."""
        self.discourse_markers = DISCOURSE_MARKERS
        self.reasoning_keywords = REASONING_KEYWORDS
        self.evidence_keywords = EVIDENCE_KEYWORDS

    def _count_sentences(self, text):
        """Count sentences in text."""
        sentences = re.split(r'(?<=[.!?])\s+', text.strip())
        return len([s for s in sentences if len(s.strip()) >= 10])

    def _count_paragraphs(self, text):
        """Count paragraphs (separated by newlines)."""
        paragraphs = [p.strip() for p in text.split("\n") if len(p.strip()) > 20]
        return max(len(paragraphs), 1)

    def _find_keywords(self, text, keyword_list):
        """Find and count keyword occurrences in text."""
        text_lower = text.lower()
        found = {}
        for keyword in keyword_list:
            count = text_lower.count(keyword.lower())
            if count > 0:
                found[keyword] = count
        return found

    # ────────────────────────────────────────────────────
    # 1. Argument Structure Analysis
    # ────────────────────────────────────────────────────
    def analyze_argument_structure(self, text):
        """
        Analyze the argument structure of the essay.
        
        Checks:
        - Number of sentences (essay length/development)
        - Discourse markers (logical flow indicators)
        - Paragraph structure (organization)
        - Transition variety
        """
        num_sentences = self._count_sentences(text)
        num_paragraphs = self._count_paragraphs(text)
        markers_found = self._find_keywords(text, self.discourse_markers)
        num_unique_markers = len(markers_found)
        total_markers = sum(markers_found.values())

        # Scoring rubric
        score = 0.0

        # Sentence count contribution (0-30 points)
        if num_sentences >= 10:
            score += 30
        elif num_sentences >= 6:
            score += 20
        elif num_sentences >= 3:
            score += 10

        # Discourse marker variety (0-40 points)
        if num_unique_markers >= 5:
            score += 40
        elif num_unique_markers >= 3:
            score += 25
        elif num_unique_markers >= 1:
            score += 10

        # Paragraph structure (0-30 points)
        if num_paragraphs >= 3:
            score += 30
        elif num_paragraphs >= 2:
            score += 20
        elif num_paragraphs >= 1:
            score += 10

        # Determine level
        if score >= 70:
            level = "high"
        elif score >= 40:
            level = "medium"
        else:
            level = "low"

        return {
            "level": level,
            "score": score,
            "num_sentences": num_sentences,
            "num_paragraphs": num_paragraphs,
            "discourse_markers_found": markers_found,
            "num_unique_markers": num_unique_markers,
        }

    # ────────────────────────────────────────────────────
    # 2. Reasoning Analysis
    # ────────────────────────────────────────────────────
    def analyze_reasoning(self, text, coherence_data=None):
        """
        Analyze reasoning quality of the essay.
        
        Checks:
        - Causal/reasoning keywords
        - Semantic coherence (from IndoSBERT)
        - Logical flow indicators
        """
        keywords_found = self._find_keywords(text, self.reasoning_keywords)
        num_unique_keywords = len(keywords_found)
        total_keywords = sum(keywords_found.values())

        # Scoring rubric
        score = 0.0

        # Reasoning keywords (0-40 points)
        if num_unique_keywords >= 4:
            score += 40
        elif num_unique_keywords >= 2:
            score += 25
        elif num_unique_keywords >= 1:
            score += 10

        # Semantic coherence from IndoSBERT (0-40 points)
        if coherence_data:
            coherence_level = coherence_data.get("coherence_level", "low")
            if coherence_level == "high":
                score += 40
            elif coherence_level == "medium":
                score += 25
            else:
                score += 10

        # Keyword density (0-20 points) - reasoning should appear throughout
        num_sentences = self._count_sentences(text)
        if num_sentences > 0:
            density = total_keywords / num_sentences
            if density >= 0.5:
                score += 20
            elif density >= 0.25:
                score += 10

        # Determine level
        if score >= 65:
            level = "high"
        elif score >= 35:
            level = "medium"
        else:
            level = "low"

        return {
            "level": level,
            "score": score,
            "reasoning_keywords_found": keywords_found,
            "num_unique_keywords": num_unique_keywords,
            "coherence_level": coherence_data.get("coherence_level", "unknown") if coherence_data else "not_analyzed",
        }

    # ────────────────────────────────────────────────────
    # 3. Evidence Use Analysis
    # ────────────────────────────────────────────────────
    def analyze_evidence_use(self, text):
        """
        Analyze use of evidence and examples in the essay.
        
        Checks:
        - Evidence keywords (misalnya, contohnya, berdasarkan, etc.)
        - Specific examples or data references
        - Supporting detail variety
        """
        keywords_found = self._find_keywords(text, self.evidence_keywords)
        num_unique_keywords = len(keywords_found)
        total_keywords = sum(keywords_found.values())

        # Scoring rubric
        score = 0.0

        # Evidence keyword variety (0-50 points)
        if num_unique_keywords >= 4:
            score += 50
        elif num_unique_keywords >= 2:
            score += 30
        elif num_unique_keywords >= 1:
            score += 15

        # Evidence frequency (0-30 points)
        if total_keywords >= 5:
            score += 30
        elif total_keywords >= 3:
            score += 20
        elif total_keywords >= 1:
            score += 10

        # Check for specific numbers/data (0-20 points)
        numbers = re.findall(r'\d+[%,.]?\d*', text)
        if len(numbers) >= 3:
            score += 20
        elif len(numbers) >= 1:
            score += 10

        # Determine level
        if score >= 60:
            level = "high"
        elif score >= 30:
            level = "medium"
        else:
            level = "low"

        return {
            "level": level,
            "score": score,
            "evidence_keywords_found": keywords_found,
            "num_unique_keywords": num_unique_keywords,
            "total_evidence_mentions": total_keywords,
        }

    # ────────────────────────────────────────────────────
    # Feedback Template Generator
    # ────────────────────────────────────────────────────
    def _generate_argument_feedback(self, analysis):
        """Generate natural language feedback for argument structure."""
        level = analysis["level"]
        markers = analysis["num_unique_markers"]
        sentences = analysis["num_sentences"]

        if level == "high":
            feedback = "Struktur argumen sudah sangat baik dan runtut."
            if markers >= 5:
                feedback += " Penggunaan penanda wacana (discourse markers) sudah bervariasi dan efektif."
        elif level == "medium":
            feedback = "Struktur argumen cukup baik, namun masih bisa ditingkatkan."
            suggestions = []
            if markers < 3:
                suggestions.append("gunakan lebih banyak penanda wacana seperti 'pertama', 'selain itu', 'oleh karena itu'")
            if sentences < 6:
                suggestions.append("kembangkan ide dengan lebih banyak kalimat penjelas")
            if suggestions:
                feedback += " Saran: " + "; ".join(suggestions) + "."
        else:
            feedback = "Struktur argumen masih perlu diperbaiki."
            suggestions = []
            if markers < 1:
                suggestions.append("tambahkan penanda wacana (pertama, kedua, selain itu, oleh karena itu) untuk menunjukkan alur berpikir")
            if sentences < 3:
                suggestions.append("kembangkan jawaban dengan lebih banyak kalimat")
            suggestions.append("susun jawaban dalam beberapa paragraf yang jelas (pembuka, isi, penutup)")
            feedback += " Saran: " + "; ".join(suggestions) + "."

        return feedback

    def _generate_reasoning_feedback(self, analysis):
        """Generate natural language feedback for reasoning."""
        level = analysis["level"]
        keywords = analysis["num_unique_keywords"]

        if level == "high":
            feedback = "Penalaran (reasoning) sudah sangat baik. Hubungan sebab-akibat terlihat jelas dan logis."
        elif level == "medium":
            feedback = "Penalaran cukup baik, namun bisa diperkuat."
            suggestions = []
            if keywords < 2:
                suggestions.append("gunakan lebih banyak kata penghubung sebab-akibat seperti 'karena', 'sehingga', 'akibatnya'")
            coherence = analysis.get("coherence_level", "unknown")
            if coherence in ("low", "unknown"):
                suggestions.append("pastikan setiap kalimat memiliki hubungan logis dengan kalimat sebelumnya")
            if suggestions:
                feedback += " Saran: " + "; ".join(suggestions) + "."
        else:
            feedback = "Penalaran masih perlu ditingkatkan."
            feedback += " Saran: jelaskan hubungan sebab-akibat secara eksplisit menggunakan kata seperti 'karena', 'sehingga', 'oleh karena itu'; pastikan setiap argumen memiliki alasan yang jelas."

        return feedback

    def _generate_evidence_feedback(self, analysis):
        """Generate natural language feedback for evidence use."""
        level = analysis["level"]
        keywords = analysis["num_unique_keywords"]

        if level == "high":
            feedback = "Penggunaan bukti/contoh sudah sangat baik dan mendukung argumen dengan kuat."
        elif level == "medium":
            feedback = "Sudah ada beberapa bukti/contoh, namun bisa ditambah."
            suggestions = []
            if keywords < 2:
                suggestions.append("tambahkan lebih banyak contoh spesifik dengan kata seperti 'misalnya', 'contohnya'")
            suggestions.append("sertakan data atau fakta pendukung jika memungkinkan")
            feedback += " Saran: " + "; ".join(suggestions) + "."
        else:
            feedback = "Tambahkan contoh dan bukti pendukung untuk memperkuat jawaban."
            feedback += " Saran: gunakan kata seperti 'misalnya', 'contohnya', 'berdasarkan'; sertakan fakta, data, atau contoh nyata yang relevan."

        return feedback

    # ────────────────────────────────────────────────────
    # Main Generate Method
    # ────────────────────────────────────────────────────
    def generate(self, essay_text, overall_score, coherence_data=None):
        """
        Generate complete formative feedback for an essay.
        
        Args:
            essay_text: The essay text
            overall_score: Predicted score from IndoBERT (1-5)
            coherence_data: Optional coherence analysis from IndoSBERT
            
        Returns:
            dict with feedback for all 3 aspects
        """
        # Analyze each aspect
        arg_analysis = self.analyze_argument_structure(essay_text)
        reasoning_analysis = self.analyze_reasoning(essay_text, coherence_data)
        evidence_analysis = self.analyze_evidence_use(essay_text)

        # Generate feedback text
        arg_feedback = self._generate_argument_feedback(arg_analysis)
        reasoning_feedback = self._generate_reasoning_feedback(reasoning_analysis)
        evidence_feedback = self._generate_evidence_feedback(evidence_analysis)

        return {
            "overall_score": overall_score,
            "argument_structure": {
                "level": arg_analysis["level"],
                "feedback": arg_feedback,
                "details": {
                    "num_sentences": arg_analysis["num_sentences"],
                    "num_paragraphs": arg_analysis["num_paragraphs"],
                    "discourse_markers_used": list(arg_analysis["discourse_markers_found"].keys()),
                },
            },
            "reasoning": {
                "level": reasoning_analysis["level"],
                "feedback": reasoning_feedback,
                "details": {
                    "reasoning_keywords_used": list(reasoning_analysis["reasoning_keywords_found"].keys()),
                    "coherence_level": reasoning_analysis["coherence_level"],
                },
            },
            "evidence_use": {
                "level": evidence_analysis["level"],
                "feedback": evidence_feedback,
                "details": {
                    "evidence_keywords_used": list(evidence_analysis["evidence_keywords_found"].keys()),
                    "total_evidence_mentions": evidence_analysis["total_evidence_mentions"],
                },
            },
        }