added duplicate check and database

app/database.py

@@ -1,14 +1,19 @@
+# app/database.py
 #!/usr/bin/env python3
 
-from sqlalchemy import create_engine, Column, String, Integer
+from sqlalchemy import create_engine
 from sqlalchemy.ext.declarative import declarative_base
 from sqlalchemy.orm import sessionmaker
-from .config import settings
+from app.config import settings
 
 Base = declarative_base()
 engine = create_engine(settings.database_url, connect_args={"check_same_thread": False})
 SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
 
+def init_db():
+    """Call this once (e.g. on startup) to create tables if they don't exist."""
+    Base.metadata.create_all(bind=engine)
+
 def get_db():
     db = SessionLocal()
     try:

app/main.py

@@ -2,6 +2,7 @@
 
 import os
 from fastapi import FastAPI, HTTPException, UploadFile, File
+from app.database import init_db
 from app.config import settings
 from app.tasks.upload_to_s3 import upload_to_s3
 from app.tasks.upload_to_dropbox import upload_to_dropbox
@@ -12,6 +13,10 @@ from app.frontend import router as frontend_router
 
 app = FastAPI(title="Document Processing API")
 
+@app.on_event("startup")
+def on_startup():
+    init_db()  # Create tables if they don't exist
+
 @app.get("/")
 def root():
     return {"message": "Document Processing API"}

app/models.py

@@ -1,7 +1,9 @@
+# app/models.py
 #!/usr/bin/env python3
 
-from .database import Base
+from sqlalchemy import Column, String, Integer, DateTime, func
-from sqlalchemy import Column, String, Integer
+from sqlalchemy.ext.declarative import declarative_base
+from app.database import Base
 
 class DocumentMetadata(Base):
     __tablename__ = "documents"
@@ -12,3 +14,35 @@ class DocumentMetadata(Base):
     recipient = Column(String)
     tags = Column(String)
     summary = Column(String)
+
+class FileRecord(Base):
+    __tablename__ = "files"
+
+    id = Column(Integer, primary_key=True, index=True)
+    
+    # Hash of the file content (e.g. SHA-256)
+    filehash = Column(String, unique=True, index=True, nullable=False)
+
+    # The name of the file as it was originally uploaded (if known)
+    original_filename = Column(String)
+
+    # The name/path we store on disk (e.g. /workdir/tmp/<uuid>.pdf)
+    local_filename = Column(String, nullable=False)
+
+    # Size of the file in bytes
+    file_size = Column(Integer, nullable=False)
+
+    # MIME type or extension (optional)
+    mime_type = Column(String)
+
+    # Timestamp when we inserted this record
+    created_at = Column(DateTime(timezone=True), server_default=func.now())
+
+class ProcessingLog(Base):
+    __tablename__ = "processing_logs"
+    id = Column(Integer, primary_key=True, index=True)
+    file_id = Column(Integer, ForeignKey("files.id"))
+    step_name = Column(String)       # e.g. "OCR", "convert_to_pdf", "upload_s3"
+    status = Column(String)          # "success" / "failure"
+    message = Column(String)         # error text or success note
+    timestamp = Column(DateTime(timezone=True), server_default=func.now())

app/tasks/upload_to_s3.py

@@ -4,15 +4,20 @@ import os
 import uuid
 import boto3
 import shutil
+import mimetypes
 import fitz  # PyMuPDF for checking embedded text
+
 from app.config import settings
 from app.tasks.retry_config import BaseTaskWithRetry
 from app.tasks.process_with_textract import process_with_textract
 from app.tasks.extract_metadata_with_gpt import extract_metadata_with_gpt
-
-# Import the shared Celery instance
 from app.celery_app import celery
 
+# NEW imports for the DB
+from app.database import SessionLocal
+from app.models import FileRecord
+from app.utils import hash_file
+
 # Initialize S3 client
 s3_client = boto3.client(
     "s3",
@@ -21,13 +26,20 @@ s3_client = boto3.client(
     region_name=settings.aws_region,
 )
 
+
 @celery.task(base=BaseTaskWithRetry)
 def upload_to_s3(original_local_file: str):
     """
     Uploads a file to S3 with a UUID-based filename and triggers processing.
-    - If the PDF already contains embedded text, skip Textract and extract text locally.
+
-    - Otherwise, upload to S3 and process with Textract.
+    Steps:
+      1. Check if we have a FileRecord entry (via SHA-256 hash). If found, skip re-processing.
+      2. If not found, insert a new DB row and continue with the pipeline:
+         - Copy file to /workdir/tmp
+         - Check for embedded text. If present, skip S3 and run local GPT extraction
+         - Otherwise, upload to S3 and queue Textract-based OCR
     """
+
     bucket_name = settings.s3_bucket_name
     if not bucket_name:
         print("[ERROR] S3 bucket name not set.")
@@ -37,22 +49,54 @@ def upload_to_s3(original_local_file: str):
         print(f"[ERROR] File {original_local_file} not found.")
         return {"error": "File not found"}
 
-    # Generate UUID and create a new filename
+    # 0. Compute the file hash and check for duplicates
-    file_ext = os.path.splitext(original_local_file)[1]  # Preserve original file extension
+    filehash = hash_file(original_local_file)
+    original_filename = os.path.basename(original_local_file)
+    file_size = os.path.getsize(original_local_file)
+    mime_type, _ = mimetypes.guess_type(original_local_file)
+    if not mime_type:
+        mime_type = "application/octet-stream"
+
+    # Acquire DB session in the task
+    with SessionLocal() as db:
+        existing = db.query(FileRecord).filter_by(filehash=filehash).one_or_none()
+        if existing:
+            print(f"[INFO] Duplicate file detected (hash={filehash[:10]}...) Skipping processing.")
+            return {
+                "status": "duplicate_file",
+                "file_id": existing.id,
+                "detail": "File already processed."
+            }
+
+        # Not a duplicate -> insert a new record
+        new_record = FileRecord(
+            filehash=filehash,
+            original_filename=original_filename,
+            local_filename="",  # Will fill in after we move it
+            file_size=file_size,
+            mime_type=mime_type,
+        )
+        db.add(new_record)
+        db.commit()
+        db.refresh(new_record)
+
+        # 1. Generate a UUID-based filename and place it in /workdir/tmp
+        file_ext = os.path.splitext(original_local_file)[1]
         file_uuid = str(uuid.uuid4())
         new_filename = f"{file_uuid}{file_ext}"
 
-    # Construct the new local path using settings.workdir and a 'tmp' subdirectory
         tmp_dir = os.path.join(settings.workdir, "tmp")
-    new_local_path = os.path.join(tmp_dir, new_filename)
-
-    # Ensure the target tmp directory exists
         os.makedirs(tmp_dir, exist_ok=True)
+        new_local_path = os.path.join(tmp_dir, new_filename)
 
         # Copy the file instead of moving it
         shutil.copy(original_local_file, new_local_path)
 
-    # Check for embedded text
+        # Update the DB with final local filename
+        new_record.local_filename = new_local_path
+        db.commit()
+
+    # 2. Check for embedded text (outside the DB session to avoid long open transactions)
     pdf_doc = fitz.open(new_local_path)
     has_text = any(page.get_text() for page in pdf_doc)
     pdf_doc.close()
@@ -72,6 +116,7 @@ def upload_to_s3(original_local_file: str):
 
         return {"file": new_local_path, "status": "Text extracted locally"}
 
+    # 3. If no embedded text, upload to S3 and queue Textract processing
     try:
         print(f"[INFO] Uploading {new_local_path} to s3://{bucket_name}/{new_filename}...")
         s3_client.upload_file(new_local_path, bucket_name, new_filename)

app/utils.py

@@ -0,0 +1,16 @@
+# app/utils.py
+import hashlib
+
+def hash_file(filepath, chunk_size=65536):
+    """
+    Returns the SHA-256 hash of the file at 'filepath'.
+    Reads the file in chunks to handle large files efficiently.
+    """
+    sha256 = hashlib.sha256()
+    with open(filepath, "rb") as f:
+        while True:
+            data = f.read(chunk_size)
+            if not data:
+                break
+            sha256.update(data)
+    return sha256.hexdigest()