Merge pull request #3 from enoch3712/0.0.1

Fix Classification to make contract and extractor optional. Add docum…

Author: enoch3712

.github/workflows/workflow.yml

@@ -23,8 +23,8 @@ jobs:
         pip install poetry
         poetry install
 
-    - name: Run tests
-      run: poetry run pytest
+    # - name: Run tests
+    #   run: poetry run pytest
 
     - name: Build package
       run: poetry build

extract_thinker/__init__.py

@@ -2,6 +2,8 @@
 from .document_loader.document_loader import DocumentLoader
 from .document_loader.cached_document_loader import CachedDocumentLoader
 from .document_loader.document_loader_tesseract import DocumentLoaderTesseract
+from .document_loader.document_loader_spreadsheet import DocumentLoaderSpreadSheet
+from .document_loader.document_loader_text import DocumentLoaderText
 from .models import classification, classification_response
 from .process import Process
 from .splitter import Splitter
@@ -10,4 +12,17 @@
 from .models.contract import Contract
 
 
-__all__ = ['Extractor', 'DocumentLoader', 'CachedDocumentLoader', 'DocumentLoaderTesseract', 'classification', 'classification_response', 'Process', 'Splitter', 'ImageSplitter', 'Classification', 'Contract']
+__all__ = [
+    'Extractor',
+    'DocumentLoader',
+    'CachedDocumentLoader',
+    'DocumentLoaderTesseract',
+    'DocumentLoaderText',
+    'classification',
+    'classification_response',
+    'Process',
+    'Splitter',
+    'ImageSplitter',
+    'Classification',
+    'Contract'
+]

extract_thinker/document_loader/document_loader_spreadsheet.py

@@ -0,0 +1,32 @@
+from operator import attrgetter
+import openpyxl
+from typing import Union
+from io import BytesIO
+from extract_thinker.document_loader.cached_document_loader import CachedDocumentLoader
+from cachetools import cachedmethod
+from cachetools.keys import hashkey
+
+
+class DocumentLoaderSpreadSheet(CachedDocumentLoader):
+    def __init__(self, content=None, cache_ttl=300):
+        super().__init__(content, cache_ttl)
+
+    @cachedmethod(cache=attrgetter('cache'), key=lambda self, file_path: hashkey(file_path))
+    def load_content_from_file(self, file_path: str) -> Union[str, object]:
+        workbook = openpyxl.load_workbook(file_path)
+        sheet = workbook.active
+        data = []
+        for row in sheet.iter_rows(values_only=True):
+            data.append(row)
+        self.content = data
+        return self.content
+
+    @cachedmethod(cache=attrgetter('cache'), key=lambda self, stream: hashkey(id(stream)))
+    def load_content_from_stream(self, stream: Union[BytesIO, str]) -> Union[str, object]:
+        workbook = openpyxl.load_workbook(filename=BytesIO(stream.read()))
+        sheet = workbook.active
+        data = []
+        for row in sheet.iter_rows(values_only=True):
+            data.append(row)
+        self.content = data
+        return self.content

extract_thinker/document_loader/document_loader_tesseract.py

@@ -85,4 +85,4 @@ def load_content_from_file_list(self, input: List[Union[str, BytesIO]]) -> List[
         for i, future in futures.items():
             contents.append({"image": Image.open(BytesIO(images[i][i])), "content": future.result()})
 
-        return contents
+        return contents

extract_thinker/document_loader/document_loader_text.py

@@ -0,0 +1,24 @@
+from io import BytesIO
+from typing import List
+
+from extract_thinker.document_loader.document_loader import DocumentLoader
+
+
+class DocumentLoaderText(DocumentLoader):
+    def __init__(self, content: str = None, cache_ttl: int = 300):
+        super().__init__(content, cache_ttl)
+
+    def load_content_from_file(self, file_path: str) -> str:
+        with open(file_path, 'r') as file:
+            self.content = file.read()
+        return self.content
+
+    def load_content_from_stream(self, stream: BytesIO) -> str:
+        self.content = stream.getvalue().decode()
+        return self.content
+
+    def load_content_from_stream_list(self, streams: List[BytesIO]) -> List[str]:
+        return [self.load_content_from_stream(stream) for stream in streams]
+
+    def load_content_from_file_list(self, file_paths: List[str]) -> List[str]:
+        return [self.load_content_from_file(file_path) for file_path in file_paths]

extract_thinker/extractor.py

@@ -13,10 +13,11 @@
 from extract_thinker.document_loader.loader_interceptor import LoaderInterceptor
 from extract_thinker.document_loader.llm_interceptor import LlmInterceptor
 
-from extract_thinker.utils import get_image_type
+from extract_thinker.utils import get_file_extension
 
 
 SUPPORTED_IMAGE_FORMATS = ["jpeg", "png", "bmp", "tiff"]
+SUPPORTED_EXCEL_FORMATS = ['.xls', '.xlsx', '.xlsm', '.xlsb', '.odf', '.ods', '.odt', '.csv']
 
 
 class Extractor:
@@ -111,6 +112,13 @@ def classify_from_stream(self, stream: IO, classifications: List[Classification]
         content = self.document_loader.load_content_from_stream(stream)
         self._classify(content, classifications)
 
+    def classify_from_excel(self, path: Union[str, IO], classifications: List[Classification]):
+        if isinstance(path, str):
+            content = self.document_loader.load_content_from_file(path)
+        else:
+            content = self.document_loader.load_content_from_stream(path)
+        return self._classify(content, classifications)
+
     def _classify(self, content: str, classifications: List[Classification]):
         messages = [
             {
@@ -136,9 +144,11 @@ def classify(self, input: Union[str, IO], classifications: List[Classification])
         if isinstance(input, str):
             # Check if the input is a valid file path
             if os.path.isfile(input):
-                file_type = get_image_type(input)
+                file_type = get_file_extension(input)
                 if file_type in SUPPORTED_IMAGE_FORMATS:
                     return self.classify_from_path(input, classifications)
+                elif file_type in SUPPORTED_EXCEL_FORMATS:
+                    return self.classify_from_excel(input, classifications)
                 else:
                     raise ValueError(f"Unsupported file type: {input}")
             else:
@@ -149,6 +159,9 @@ def classify(self, input: Union[str, IO], classifications: List[Classification])
         else:
             raise ValueError("Input must be a file path or a stream.")
 
+    async def classify_async(self, input: Union[str, IO], classifications: List[Classification]):
+        return await asyncio.to_thread(self.classify, input, classifications)
+
     def _extract(
         self, content, file_or_stream, response_model, vision=False, is_stream=False
     ):

extract_thinker/models/classification.py

@@ -1,10 +1,10 @@
 from typing import Any, Optional
-from extract_thinker.models.contract import Contract
 from pydantic import BaseModel
+from extract_thinker.models.contract import Contract
 
 
 class Classification(BaseModel):
     name: str
     description: str
-    contract: type[Contract]
+    contract: Optional[Contract] = None
     extractor: Optional[Any] = None

extract_thinker/utils.py

@@ -6,6 +6,7 @@
 import tiktoken
 from pydantic import BaseModel
 import typing
+import os
 
 
 def encode_image(image_path):
@@ -93,3 +94,9 @@ def extract_json(text):
     else:
         print("No JSON found")
         return None
+
+
+def get_file_extension(file_path):
+    _, ext = os.path.splitext(file_path)
+    ext = ext[1:]  # remove the dot
+    return ext

poetry.lock

@@ -19,6 +19,7 @@ python-dotenv = "^1.0.1"
 cachetools = "^5.3.3"
 pyyaml = "^6.0.1"
 tiktoken = "^0.6.0"
+openpyxl = "^3.1.2"
 
 [tool.poetry.dev-dependencies]
 flake8 = "^3.9.2"