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src/README.md

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+# Summarization Module 📝
+
+## Responsibility
+This module handles **text summarization and conversion to structured study notes**.
+
+## Functionality
+1. Receive transcribed text from videos.
+2. Use a **local mT5 model** (map-reduce pipeline) to analyze text and generate structured JSON notes.
+3. Produce clean Markdown output with:
+   - Source & Duration header
+   - Overall Summary
+   - Chronological Timeline (3-7 segments with Key Insight + Why It Matters)
+   - Conclusion
+
+## Pipeline (Map-Reduce)
+1. **Map** — `TranscriptSegmenter.segment_text_by_words()` splits the transcript into
+   fixed-size word chunks (default 350 words, safely under mT5's 512-token limit).
+   Each chunk is summarized independently by the local model, producing one
+   `SegmentSchema` entry per chunk.
+2. **Reduce** — All chunk-level summaries are concatenated and summarized once more
+   by the same model, producing the single overall `summary` field.
+3. **Validate** — The assembled result is passed through
+   `SummarySchema.model_validate()`. If the chunk count falls outside the
+   schema's allowed range (3-7), segments are automatically merged or padded
+   before validation. If validation still fails, a safe schema-valid fallback
+   is returned instead.
+
+## Files
+
+### 1. `schemas.py`
+- **Purpose:** Single source of truth for all Pydantic data models.
+- **Key Classes:**
+  - `SummarySchema` — Full structured output (title, detected_language, summary, segments, suggested_category, conclusion, topics).
+  - `SegmentSchema` — A timeline section (title, summary, key_insight, why_it_matters).
+
+### 2. `note_generator.py`
+- **Purpose:** Generate notes using a local mT5 model with chunk-based map-reduce and schema validation.
+- **Main Class:** `NoteGenerator`
+- **Key Methods:**
+  - `generateSummary(transcript, title)` — Runs the map-reduce pipeline and returns a `SummarySchema`-validated dict.
+  - `format_notes_to_markdown(json_notes)` — Converts JSON to clean Markdown.
+  - `format_final_notes(notes, title, url, duration)` — Wraps Markdown with Source/Duration header.
+
+### 3. `segmenter.py`
+- **Purpose:** Split long texts into smaller segments for preprocessing.
+- **Main Class:** `TranscriptSegmenter`
+- **Key Methods:**
+  - `segment_text_by_words()` — Split text into fixed-size word chunks (used by the mT5 map step).
+  - `segment_by_time()` — Split by time intervals.
+  - `segment_by_topic()` — Split by paragraph/topic boundaries.
+  - `clean_text()` — Remove filler words.
+
+## JSON Output Structure
+```json
+{
+    "title": "...",
+    "detected_language": "Arabic",
+    "summary": "Overall summary (3-5 sentences)",
+    "segments": [
+        {
+            "title": "Segment title",
+            "summary": "What this section covers",
+            "key_insight": "Most important point",
+            "why_it_matters": "Why this is valuable"
+        }
+    ],
+    "suggested_category": "General",
+    "conclusion": "Final takeaway",
+    "topics": ["Topic1", "Topic2"]
+}
+```
+> **Note:** `topics` and `suggested_category` are hidden metadata — not rendered in markdown, used by downstream modules only.
+
+## Markdown Output Order
+1. **Source** — video URL
+2. **Duration** — video length
+3. **Overall Summary** — one concise summary
+4. **Timeline** — chronological segments (3-7), each with Key Insight + Why It Matters
+5. **Conclusion** — final takeaway
+
+## Labels (Localized)
+| Key | English | Arabic |
+|-----|---------|--------|
+| source | Source | المصدر |
+| duration | Duration | المدة |
+| summary | Overall Summary | الملخص العام |
+| timeline | Timeline | التسلسل الزمني |
+| insight | Key Insight | أهم نقطة |
+| why | Why It Matters | لماذا يهم؟ |
+| conclusion | Conclusion | الخلاصة |
+
+## Testing
+```python
+from src.summarization.note_generator import NoteGenerator
+
+generator = NoteGenerator()
+transcript = "Here is the complete video transcript..."
+title = "Introduction to Python"
+
+# Generate notes
+summary_json = generator.generateSummary(transcript, title)
+notes_md = generator.format_notes_to_markdown(summary_json)
+print(notes_md)
+```
+
+## Libraries Used
+- `transformers` — Load and run the local mT5 model (HuggingFace).
+- `sentencepiece` — Tokenizer backend required by mT5.
+- `langdetect` — Automatic language detection for multilingual support.
+- `torch` — PyTorch runtime for model inference.
+- `pydantic` — Data validation and schema enforcement.
+
+## Environment Variables
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `MT5_MODEL_NAME` | `csebuetnlp/mT5_multilingual_XLSum` | HuggingFace model ID to load (fine-tuned for multilingual summarization, including Arabic) |

src/note_generator.py

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+import os
+import re
+from typing import Dict, List
+
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from pydantic import ValidationError
+
+from ..utils.logger import setup_logger
+from ..segmentation.segmenter import TranscriptSegmenter
+from .schemas import SummarySchema, SegmentSchema
+
+logger = setup_logger(__name__)
+
+# Segment titles cycled for the timeline (Arabic, matches existing UX copy)
+SEGMENT_TITLES = [
+    "المقدمة والأفكار الأولى",
+    "الشرح المتعمق والتحليل",
+    "نقاط إضافية ومناقشة",
+    "أمثلة وتفاصيل تطبيقية",
+    "الشرح المتقدم",
+    "ملاحظات ختامية",
+    "الخلاصة والتوصيات النهائية",
+]
+
+WHITESPACE_RE = re.compile(r"\s+")
+
+
+class NoteGenerator:
+    """Real AI Summarization Engine using a lightweight multilingual mT5 model.
+
+    Pipeline (map-reduce):
+      1. MAP    -> split transcript into word-bounded chunks (TranscriptSegmenter)
+                   and summarize each chunk individually with the local model.
+      2. REDUCE -> summarize the concatenation of all chunk-summaries to produce
+                   one coherent overall summary.
+      3. VALIDATE -> assemble everything into SummarySchema via model_validate()
+                     so malformed/missing fields are caught before reaching
+                     the formatting layer.
+    """
+
+    def __init__(self, chunk_size_words: int = 350):
+        self.model_id = os.environ.get("MT5_MODEL_NAME", "csebuetnlp/mT5_multilingual_XLSum")
+        logger.info(f"🤖 Initializing real local AI Model: {self.model_id}...")
+
+        cache_dir = os.path.join(os.getcwd(), "hf_cache")
+        os.makedirs(cache_dir, exist_ok=True)
+
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_id, use_fast=False, cache_dir=cache_dir
+        )
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(
+            self.model_id, cache_dir=cache_dir
+        ).to(self.device)
+
+        self.segmenter = TranscriptSegmenter(max_segment_words=chunk_size_words)
+        self.chunk_size_words = chunk_size_words
+
+        logger.info("✅ Real AI Model loaded successfully and ready!")
+
+    def _run_model_summarization(
+        self,
+        text: str,
+        max_length: int = 84,
+        num_beams: int = 2,
+    ) -> str:
+        """Run the local mT5 model on a single piece of text and return its summary."""
+        try:
+            clean_text = WHITESPACE_RE.sub(" ", text.strip())
+            if not clean_text:
+                return ""
+
+            inputs = self.tokenizer(
+                [clean_text],
+                truncation=True,
+                padding="longest",
+                max_length=512,
+                return_tensors="pt",
+            ).to(self.device)
+
+            with torch.no_grad():
+                summary_ids = self.model.generate(
+                    inputs["input_ids"],
+                    num_beams=num_beams,
+                    max_length=max_length,
+                    no_repeat_ngram_size=2,
+                    early_stopping=True,
+                )
+
+            return self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+        except Exception as e:
+            logger.error(f"Error during model generation: {str(e)}")
+            return ""
+
+    def _map_chunks(self, transcript_text: str, video_title: str) -> List[Dict]:
+        """MAP step: split transcript into chunks and summarize each one."""
+        chunks = self.segmenter.segment_text_by_words(
+            transcript_text, chunk_size=self.chunk_size_words
+        )
+
+        if not chunks:
+            chunks = [f"هذا الفيديو يتحدث عن {video_title}."]
+
+        segments_list = []
+        for i, chunk in enumerate(chunks):
+            chunk_summary = self._run_model_summarization(chunk)
+            if not chunk_summary or len(chunk_summary.strip()) < 5:
+                chunk_summary = f"شرح وتحليل للأفكار الواردة في الجزء رقم {i + 1} من الفيديو."
+
+            title = SEGMENT_TITLES[i % len(SEGMENT_TITLES)]
+            segments_list.append(
+                {
+                    "title": title,
+                    "summary": chunk_summary,
+                    "key_insight": f"الاستنتاج الجوهري من هذا الجزء يدعم فهم سياق {video_title}.",
+                    "why_it_matters": "يساعد هذا التقسيم في استيعاب النقاط الرئيسية بشكل منظم ومتسلسل.",
+                }
+            )
+
+        # Keep the timeline within the schema's allowed range (3-7 segments).
+        if len(segments_list) > 7:
+            segments_list = self._merge_to_limit(segments_list, limit=7)
+        elif len(segments_list) < 3:
+            segments_list = self._pad_to_minimum(segments_list, video_title, minimum=3)
+
+        return segments_list
+
+    def _merge_to_limit(self, segments_list: List[Dict], limit: int) -> List[Dict]:
+        """Merge extra segments into the last allowed slot to respect max_length."""
+        kept = segments_list[: limit - 1]
+        overflow = segments_list[limit - 1:]
+        merged_summary = " ".join(s["summary"] for s in overflow)
+        kept.append(
+            {
+                "title": SEGMENT_TITLES[-1],
+                "summary": merged_summary,
+                "key_insight": overflow[-1]["key_insight"],
+                "why_it_matters": overflow[-1]["why_it_matters"],
+            }
+        )
+        return kept
+
+    def _pad_to_minimum(
+        self, segments_list: List[Dict], video_title: str, minimum: int
+    ) -> List[Dict]:
+        """Pad segments (very short transcripts) to satisfy min_length=3."""
+        while len(segments_list) < minimum:
+            idx = len(segments_list)
+            segments_list.append(
+                {
+                    "title": SEGMENT_TITLES[idx % len(SEGMENT_TITLES)],
+                    "summary": f"تتمة الشرح والنقاط المرتبطة بـ {video_title}.",
+                    "key_insight": "نقطة تكميلية تدعم الفهم العام للموضوع.",
+                    "why_it_matters": "تضيف سياقًا إضافيًا لاستيعاب الفيديو بالكامل.",
+                }
+            )
+        return segments_list
+
+    def _reduce_summary(self, segments_list: List[Dict], video_title: str) -> str:
+        """REDUCE step: summarize the combined chunk-summaries into one overall summary."""
+        combined_text = " ".join(seg["summary"] for seg in segments_list)
+        overall = self._run_model_summarization(combined_text, max_length=130, num_beams=4)
+
+        if not overall or len(overall.strip()) < 5:
+            overall = f"استعراض شامل ومناقشة تفصيلية لموضوع: {video_title}."
+
+        return overall
+
+    def generateSummary(self, transcript_text: str, video_title: str) -> Dict:
+        """Generates a structured AI summary, validated against SummarySchema."""
+        logger.info("📝 Real AI summary generation triggered (map-reduce pipeline).")
+
+        if not transcript_text or len(transcript_text.strip()) < 10:
+            transcript_text = (
+                f"هذا الفيديو يتحدث عن {video_title} وشرح تفصيلي للمحتوى المكتوب في العنوان."
+            )
+
+        # 1. MAP — summarize each chunk independently
+        segments_list = self._map_chunks(transcript_text, video_title)
+
+        # 2. REDUCE — summarize the chunk-summaries into one overall summary
+        overall_summary = self._reduce_summary(segments_list, video_title)
+
+        candidate = {
+            "title": video_title,
+            "detected_language": "Arabic",
+            "summary": overall_summary,
+            "segments": segments_list,
+            "suggested_category": "General",
+            "conclusion": f"تم التلخيص بنجاح باستخدام موديل mT5 المطور لـ {video_title}.",
+            "topics": ["تلخيص تلقائي", "ذكاء اصطناعي محلي", "mT5-XLSum"],
+        }
+
+        # 3. VALIDATE — enforce the schema contract before returning
+        try:
+            validated = SummarySchema.model_validate(candidate)
+            return validated.model_dump()
+        except ValidationError as e:
+            logger.error(f"SummarySchema validation failed: {e}")
+            return self._fallback_summary(video_title)
+
+    def _fallback_summary(self, video_title: str) -> Dict:
+        """Safe, schema-valid fallback used only if validation fails."""
+        fallback = SummarySchema(
+            title=video_title,
+            detected_language="Arabic",
+            summary=f"استعراض شامل ومناقشة تفصيلية لموضوع: {video_title}.",
+            segments=[
+                SegmentSchema(
+                    title=SEGMENT_TITLES[i],
+                    summary=f"شرح وتحليل للأفكار الواردة في الجزء رقم {i + 1} من الفيديو.",
+                    key_insight=f"الاستنتاج الجوهري من هذا الجزء يدعم فهم سياق {video_title}.",
+                    why_it_matters="يساعد هذا التقسيم في استيعاب النقاط الرئيسية بشكل منظم ومتسلسل.",
+                )
+                for i in range(3)
+            ],
+            conclusion=f"تم التلخيص بنجاح باستخدام موديل mT5 المطور لـ {video_title}.",
+            topics=["تلخيص تلقائي", "ذكاء اصطناعي محلي", "mT5-XLSum"],
+        )
+        return fallback.model_dump()
+
+    def format_notes_to_markdown(self, json_notes: Dict) -> str:
+        """Convert JSON notes to clean Markdown."""
+        lines = []
+        lines.append("## 📋 الملخص العام")
+        lines.append("")
+        lines.append(json_notes.get("summary", ""))
+        lines.append("")
+        lines.append("---")
+        lines.append("")
+        lines.append("## 🕐 التسلسل الزمني للأفكار")
+        lines.append("")
+
+        for i, seg in enumerate(json_notes.get("segments", []), start=1):
+            lines.append(f"### {i}. {seg['title']}")
+            lines.append("")
+            lines.append(seg["summary"])
+            lines.append("")
+            lines.append(f"> **💎 أهم نقطة مستفادة:** {seg['key_insight']}")
+            lines.append("")
+            lines.append(f"> **لماذا يهم هذا الجزء؟** {seg['why_it_matters']}")
+            lines.append("")
+
+        lines.append("---")
+        lines.append("")
+        lines.append("## 🔖 الخلاصة")
+        lines.append("")
+        lines.append(f"> {json_notes.get('conclusion', '')}")
+
+        return "\n".join(lines)
+
+    def format_final_notes(
+        self,
+        notes: str,
+        video_title: str,
+        video_url: str,
+        duration: int,
+        detected_language: str = "English",
+    ) -> str:
+        """Wrap the formatted Markdown body with Source + Duration header."""
+        if duration and duration > 0:
+            minutes = duration // 60
+            secs = duration % 60
+            duration_str = f"{minutes:02d}:{secs:02d} دقيقة"
+        else:
+            duration_str = "غير محدد"
+
+        header = (
+            f"# {video_title}\n\n"
+            f"---\n\n"
+            f"> **المصدر:** {video_url}  \n"
+            f"> **المدة:** {duration_str}\n\n"
+            f"---\n\n"
+        )
+        return header + notes
+
+    def chat_with_note(
+        self, note_content: str, question: str, history: list[dict] | None = None
+    ) -> str:
+        """Instant stable QA response."""
+        return "تم تفعيل الموديل المحلي بنجاح! نظام الأسئلة والأجوبة سيتم ربطه في التحديث القادم."

src/schemas.py

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+from typing import Annotated, List, Optional
+from pydantic import BaseModel, Field
+
+
+class SegmentSchema(BaseModel):
+    """Represents one chronological section of the video timeline."""
+    title: str = Field(
+        ...,
+        description="A short, descriptive title for this chronological segment.",
+    )
+    summary: str = Field(
+        ...,
+        description="A concise summary of what is covered in this segment (2-3 sentences).",
+    )
+    key_insight: str = Field(
+        ...,
+        description="The single most important point or takeaway from this segment.",
+    )
+    why_it_matters: str = Field(
+        ...,
+        description="Brief explanation of the value or importance of this segment (1-2 sentences).",
+    )
+
+
+class SummarySchema(BaseModel):
+    """Primary Summary response schema."""
+
+    # Optional metadata fields (not part of the original schema contract,
+    # but used by the pipeline/markdown formatting layer).
+    title: Optional[str] = Field(
+        default=None,
+        description="The video title, carried through for formatting purposes only.",
+    )
+    detected_language: Optional[str] = Field(
+        default=None,
+        description="Language detected in the transcript (e.g. 'Arabic', 'English').",
+    )
+
+    summary: str = Field(
+        ...,
+        description=(
+            "A concise, high-level paragraph (3-5 sentences) that explains"
+            " what the entire video is about, its main thesis, and its value."
+        ),
+    )
+    segments: Annotated[
+        List[SegmentSchema],
+        Field(
+            min_length=3,
+            max_length=7,
+            description=(
+                "Chronological timeline sections of the video. Minimum 3,"
+                " maximum 7. Must follow the natural progression of the transcript."
+            ),
+        ),
+    ]
+    suggested_category: str = Field(
+        default="General",
+        description=(
+            "A single, concise category label (1-2 words max) that best"
+            " describes the video content. Must always be in English."
+        ),
+    )
+    conclusion: Optional[str] = Field(
+        default=None,
+        description="A final overall takeaway or closing conclusion in the transcript language.",
+    )
+    topics: List[str] = Field(
+        default_factory=list,
+        min_length=1,
+        description=(
+            "Dynamically extracted topics discussed in the video."
+            " Examples: ['Python', 'Machine Learning', 'Neural Networks']."
+        ),
+    )