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backend/app/services/processors/audio_processor.py

@@ -0,0 +1,20 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+from backend.app.services.markdown_builder import title_from_path
+from backend.app.services.processors.base import ProcessedContent
+from backend.app.services.providers.deepgram_client import DeepgramClient
+
+
+class AudioProcessor:
+    def process(self, path: Path) -> ProcessedContent:
+        client = DeepgramClient()
+        data = client.transcribe_file(path)
+        transcript, confidence = client.transcript_from_response(data)
+        confidence_line = f"\n\n_Confianza estimada: {confidence * 100:.1f}%_" if isinstance(confidence, (int, float)) else ""
+        return ProcessedContent(
+            title=f"Transcripcion {title_from_path(path)}",
+            body=f"## Transcripcion\n\n{transcript}{confidence_line}",
+            processor="deepgram",
+        )

backend/app/services/processors/base.py

@@ -0,0 +1,10 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+
+@dataclass
+class ProcessedContent:
+    title: str
+    body: str
+    processor: str

backend/app/services/processors/docx_processor.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+from docx import Document
+
+from backend.app.services.markdown_builder import title_from_path
+from backend.app.services.processors.base import ProcessedContent
+
+
+class DocxProcessor:
+    def process(self, path: Path) -> ProcessedContent:
+        doc = Document(path)
+        blocks: list[str] = []
+        for paragraph in doc.paragraphs:
+            text = paragraph.text.strip()
+            if text:
+                blocks.append(text)
+        for table in doc.tables:
+            rows = [[cell.text.strip().replace("\n", " ") for cell in row.cells] for row in table.rows]
+            if rows:
+                blocks.append(self._table_to_markdown(rows))
+        return ProcessedContent(title=title_from_path(path), body="\n\n".join(blocks), processor="docx")
+
+    def _table_to_markdown(self, rows: list[list[str]]) -> str:
+        header = rows[0]
+        lines = [f"| {' | '.join(header)} |", f"| {' | '.join(['---'] * len(header))} |"]
+        for row in rows[1:]:
+            lines.append(f"| {' | '.join(row)} |")
+        return "\n".join(lines)

backend/app/services/processors/pdf_processor.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import fitz
+
+from backend.app.services.markdown_builder import title_from_path
+from backend.app.services.processors.base import ProcessedContent
+from backend.app.services.providers.mistral_client import MistralClient
+
+
+class PdfProcessor:
+    def __init__(self, use_ocr: bool = False, page_ranges: str | None = None):
+        self.use_ocr = use_ocr
+        self.page_ranges = page_ranges
+
+    def process(self, path: Path) -> ProcessedContent:
+        if self.use_ocr:
+            return self._process_ocr(path)
+        doc = fitz.open(path)
+        pages: list[str] = []
+        for index in self._selected_pages(len(doc)):
+            page = doc[index - 1]
+            text = page.get_text("text").strip()
+            if text:
+                pages.append(f"## Pagina {index}\n\n{text}")
+        body = "\n\n".join(pages).strip()
+        if len(body) < 80:
+            raise RuntimeError("El PDF tiene poco texto extraible; procesalo con OCR")
+        return ProcessedContent(title=title_from_path(path), body=body, processor="pdf-text")
+
+    def _process_ocr(self, path: Path) -> ProcessedContent:
+        doc = fitz.open(path)
+        client = MistralClient()
+        parts: list[str] = []
+        temp_paths: list[Path] = []
+        try:
+            for index in self._selected_pages(len(doc)):
+                page = doc[index - 1]
+                pix = page.get_pixmap(matrix=fitz.Matrix(2, 2), alpha=False)
+                image_path = path.with_name(f"{path.stem}-page-{index}.jpg")
+                pix.save(image_path)
+                temp_paths.append(image_path)
+                parts.append(f"## Pagina {index}\n\n{client.ocr_image(image_path)}")
+        finally:
+            for temp_path in temp_paths:
+                temp_path.unlink(missing_ok=True)
+        return ProcessedContent(title=title_from_path(path), body="\n\n".join(parts), processor="mistral-ocr")
+
+    def _selected_pages(self, total_pages: int) -> list[int]:
+        if total_pages < 1:
+            raise RuntimeError("El PDF no tiene paginas")
+        if not self.page_ranges or not self.page_ranges.strip():
+            return list(range(1, total_pages + 1))
+
+        selected: list[int] = []
+        for raw_part in self.page_ranges.split(","):
+            part = raw_part.strip()
+            if not part:
+                continue
+            if "-" in part:
+                start_text, end_text = [piece.strip() for piece in part.split("-", 1)]
+                if not start_text.isdigit() or not end_text.isdigit():
+                    raise RuntimeError(f"Rango de paginas invalido: {part}")
+                start = int(start_text)
+                end = int(end_text)
+                if start > end:
+                    raise RuntimeError(f"Rango de paginas invertido: {part}")
+                selected.extend(range(start, end + 1))
+            else:
+                if not part.isdigit():
+                    raise RuntimeError(f"Pagina invalida: {part}")
+                selected.append(int(part))
+
+        unique_pages = sorted(set(selected))
+        invalid = [page for page in unique_pages if page < 1 or page > total_pages]
+        if invalid:
+            raise RuntimeError(f"Paginas fuera de rango: {', '.join(map(str, invalid))}. El PDF tiene {total_pages} paginas")
+        if not unique_pages:
+            raise RuntimeError("No se seleccionaron paginas para procesar")
+        return unique_pages

backend/app/services/processors/text_processor.py

@@ -0,0 +1,15 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+from backend.app.services.markdown_builder import title_from_path
+from backend.app.services.processors.base import ProcessedContent
+
+
+class TextProcessor:
+    def process(self, path: Path) -> ProcessedContent:
+        return ProcessedContent(
+            title=title_from_path(path),
+            body=path.read_text(encoding="utf-8", errors="replace"),
+            processor="text",
+        )

backend/app/services/processors/video_processor.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+import subprocess
+from pathlib import Path
+
+from backend.app.services.processors.audio_processor import AudioProcessor
+from backend.app.services.processors.base import ProcessedContent
+
+
+class VideoProcessor:
+    def __init__(self, tmp_dir: Path):
+        self.tmp_dir = tmp_dir
+
+    def process(self, path: Path) -> ProcessedContent:
+        self.tmp_dir.mkdir(parents=True, exist_ok=True)
+        audio_path = self.tmp_dir / f"{path.stem}.wav"
+        try:
+            subprocess.run(
+                [
+                    "ffmpeg",
+                    "-y",
+                    "-i",
+                    str(path),
+                    "-vn",
+                    "-acodec",
+                    "pcm_s16le",
+                    "-ar",
+                    "16000",
+                    "-ac",
+                    "1",
+                    str(audio_path),
+                ],
+                check=True,
+                capture_output=True,
+                text=True,
+            )
+            processed = AudioProcessor().process(audio_path)
+            processed.title = f"Transcripcion {path.stem}"
+            processed.processor = "ffmpeg+deepgram"
+            return processed
+        except FileNotFoundError as exc:
+            raise RuntimeError("ffmpeg no esta instalado o no esta disponible en PATH") from exc
+        except subprocess.CalledProcessError as exc:
+            raise RuntimeError(f"No se pudo extraer audio del video: {exc.stderr}") from exc
+        finally:
+            audio_path.unlink(missing_ok=True)