Add user location support for tax calculations in document processing
This commit is contained in:
@@ -17,28 +17,55 @@ class DocumentProcessor:
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self.client = groq.Groq(api_key=settings.GROQ_API_KEY)
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self.model = "meta-llama/llama-4-scout-17b-16e-instruct" # Vision model
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async def process_file(self, file_path: str, file_type: str) -> Dict[str, Any]:
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"""Process uploaded file and extract receipt data"""
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async def process_file(
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self, file_path: str, file_type: str, user_location: str = None
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) -> Dict[str, Any]:
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"""Process uploaded file and extract receipt data
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Args:
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file_path: Path to the file to process
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file_type: Type of file (jpg, pdf, etc.)
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user_location: User's location string in format "State/Province, Country" (e.g., "Ontario, Canada")
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"""
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try:
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if file_type.lower() in ["jpg", "jpeg", "png", "gif", "bmp"]:
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return await self._process_image(file_path)
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return await self._process_image(file_path, user_location)
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elif file_type.lower() == "pdf":
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return await self._process_pdf(file_path)
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return await self._process_pdf(file_path, user_location)
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else:
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raise ValueError(f"Unsupported file type: {file_type}")
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except Exception as e:
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return {"error": str(e)}
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async def _process_image(self, image_path: str) -> Dict[str, Any]:
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"""Extract data from image using Groq vision"""
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async def _process_image(
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self, image_path: str, user_location: str = None
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) -> Dict[str, Any]:
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"""Extract data from image using Groq vision
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Args:
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image_path: Path to the image file
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user_location: User's location string in format "State/Province, Country" (e.g., "Ontario, Canada")
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"""
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try:
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# Encode image to base64
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base64_image = self._encode_image(image_path)
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# Build user location context
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user_location_context = ""
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if user_location:
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user_location_context = f"""
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USER LOCATION CONTEXT:
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The user is located in {user_location}.
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- If the receipt location is MISSING or UNCLEAR, use the user's location ({user_location}) for tax calculations.
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- If the receipt clearly shows a different location, use the receipt's location instead.
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- Apply depreciation rules based on the user's location.
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"""
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# Create Groq vision prompt
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prompt = """
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prompt = f"""
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Analyze this receipt image and extract the following information in JSON format:
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{
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{{
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"vendor": "Store/company name",
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"description": "Detailed description of items/services purchased",
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"total_amount": 0.00,
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@@ -53,7 +80,7 @@ class DocumentProcessor:
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"cca_rate": null,
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"useful_life": null,
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"residual_value": null
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}
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}}
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Rules:
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- Extract vendor name as it appears on receipt
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@@ -64,11 +91,11 @@ class DocumentProcessor:
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- Categorize based on vendor type (Starbucks=Food, Shell=Transport, etc.)
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- Confidence score 0-1 based on how clear the receipt is
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- Currency should be the currency used on the receipt (e.g., "USD", "EUR", "CAD")
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{user_location_context}
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LOCATION & TAX RULES:
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- Extract location from receipt (look for store address, province/state, country)
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- Format location as "Province/State, Country" (e.g., "Ontario, Canada" or "California, USA")
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- If location not shown, return null for location
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- If location not shown on receipt, return null for location (system will use user location as fallback)
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- If tax_amount is NOT clearly shown on receipt, calculate it based on location:
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* Ontario, Canada: 13% HST
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@@ -132,12 +159,19 @@ class DocumentProcessor:
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode("utf-8")
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async def _process_pdf(self, pdf_path: str) -> Dict[str, Any]:
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"""Extract data from PDF by converting to image first"""
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async def _process_pdf(
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self, pdf_path: str, user_location: str = None
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) -> Dict[str, Any]:
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"""Extract data from PDF by converting to image first
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Args:
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pdf_path: Path to the PDF file
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user_location: User's location string in format "State/Province, Country" (e.g., "Ontario, Canada")
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"""
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try:
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# For now, extract text from PDF and process as text
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text_content = self._extract_text_from_pdf(pdf_path)
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return self._process_text_content(text_content)
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return self._process_text_content(text_content, user_location)
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except Exception as e:
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return {"error": f"PDF processing error: {str(e)}"}
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@@ -154,9 +188,28 @@ class DocumentProcessor:
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except Exception:
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return ""
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def _process_text_content(self, text_content: str) -> Dict[str, Any]:
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"""Process text content using Groq (fallback for PDFs)"""
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def _process_text_content(
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self, text_content: str, user_location: str = None
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) -> Dict[str, Any]:
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"""Process text content using Groq (fallback for PDFs)
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Args:
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text_content: Extracted text from PDF
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user_location: User's location string in format "State/Province, Country" (e.g., "Ontario, Canada")
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"""
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try:
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# Build user location context
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user_location_context = ""
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if user_location:
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user_location_context = f"""
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USER LOCATION CONTEXT:
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The user is located in {user_location}.
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- If the receipt location is MISSING or UNCLEAR, use the user's location ({user_location}) for tax calculations.
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- If the receipt clearly shows a different location, use the receipt's location instead.
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- Apply depreciation rules based on the user's location.
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"""
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prompt = f"""
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Analyze this receipt text and extract the following information in JSON format:
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@@ -190,11 +243,11 @@ class DocumentProcessor:
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- Categorize based on vendor type
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- Confidence score 0-1 based on clarity
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- Currency should be the currency used on the receipt (e.g., "USD", "EUR", "CAD")
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{user_location_context}
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LOCATION & TAX RULES:
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- Extract location from receipt (look for store address, province/state, country)
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- Format location as "Province/State, Country" (e.g., "Ontario, Canada" or "California, USA")
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- If location not shown, return null for location
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- If location not shown on receipt, return null for location (system will use user location as fallback)
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- If tax_amount is NOT clearly shown on receipt, calculate it based on location:
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* Ontario, Canada: 13% HST
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