app.py

from transformers import MllamaForConditionalGeneration, AutoProcessor, TextIteratorStreamer , AutoModel,Qwen2VLForConditionalGeneration, AutoModelForImageTextToText , Qwen2_5_VLForConditionalGeneration
from qwen_vl_utils import process_vision_info
from PIL import Image
import requests
import torch
from threading import Thread
import gradio as gr
from gradio import FileData
import time
import spaces
import fitz  # PyMuPDF
import io
import numpy as np
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Load model and processor
ckpt ="Qwen/Qwen2.5-VL-7B-Instruct"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(ckpt, torch_dtype=torch.bfloat16,trust_remote_code=True).to("cuda")
processor = AutoProcessor.from_pretrained(ckpt,trust_remote_code=True)

class DocumentState:
    def __init__(self):
        self.current_doc_images = []
        self.current_doc_text = ""
        self.doc_type = None
        
    def clear(self):
        self.current_doc_images = []
        self.current_doc_text = ""
        self.doc_type = None
        
doc_state = DocumentState()

def process_pdf_file(file_path):
    """Convert PDF to images and extract text using PyMuPDF."""
    try:
        doc = fitz.open(file_path)
        images = []
        text = ""
        
        for page_num in range(doc.page_count):
            try:
                page = doc[page_num]
                page_text = page.get_text("text")
                if page_text.strip():
                    text += f"Page {page_num + 1}:\n{page_text}\n\n"
                
                zoom = 3
                mat = fitz.Matrix(zoom, zoom)
                pix = page.get_pixmap(matrix=mat, alpha=False)
                img_data = pix.tobytes("png")
                img = Image.open(io.BytesIO(img_data))
                img = img.convert("RGB")
                
                max_size = 1600
                if max(img.size) > max_size:
                    ratio = max_size / max(img.size)
                    new_size = tuple(int(dim * ratio) for dim in img.size)
                    img = img.resize(new_size, Image.Resampling.LANCZOS)
                
                images.append(img)
                
            except Exception as e:
                logger.error(f"Error processing page {page_num}: {str(e)}")
                continue
                
        doc.close()
        
        if not images:
            raise ValueError("No valid images could be extracted from the PDF")
            
        return images, text
        
    except Exception as e:
        logger.error(f"Error processing PDF file: {str(e)}")
        raise

def process_uploaded_file(file):
    """Process uploaded file and update document state."""
    try:
        doc_state.clear()
        
        if file is None:
            return "No file uploaded. Please upload a file."
        
        # Get the file path and extension
        if isinstance(file, dict):
            file_path = file["name"]
        else:
            file_path = file.name
            
        # Get file extension
        file_ext = file_path.lower().split('.')[-1]
        
        # Define allowed extensions
        image_extensions = {'png', 'jpg', 'jpeg', 'gif', 'bmp', 'webp'}
        
        if file_ext == 'pdf':
            doc_state.doc_type = 'pdf'
            try:
                doc_state.current_doc_images, doc_state.current_doc_text = process_pdf_file(file_path)
                return f"PDF processed successfully. Total pages: {len(doc_state.current_doc_images)}. You can now ask questions about the content."
            except Exception as e:
                return f"Error processing PDF: {str(e)}. Please try a different PDF file."
        elif file_ext in image_extensions:
            doc_state.doc_type = 'image'
            try:
                img = Image.open(file_path).convert("RGB")
                max_size = 1600
                if max(img.size) > max_size:
                    ratio = max_size / max(img.size)
                    new_size = tuple(int(dim * ratio) for dim in img.size)
                    img = img.resize(new_size, Image.Resampling.LANCZOS)
                doc_state.current_doc_images = [img]
                return "Image loaded successfully. You can now ask questions about the content."
            except Exception as e:
                return f"Error processing image: {str(e)}. Please try a different image file."
        else:
            return f"Unsupported file type: {file_ext}. Please upload a PDF or image file (PNG, JPG, JPEG, GIF, BMP, WEBP)."
    except Exception as e:
        logger.error(f"Error in process_file: {str(e)}")
        return "An error occurred while processing the file. Please try again."

@spaces.GPU()
def bot_streaming(prompt_option, max_new_tokens=4096):
    try:
        # Define predetermined prompts
        prompts = {
            "NOC Timesheet": (
                """Extract structured information from the provided timesheet. The extracted details should include:

Name

Position Title

Work Location

Contractor 

NOC ID

Month and Year

Regular Service Days (ONSHORE)

Standby Days (ONSHORE in Doha)

Offshore Days

Standby & Extended Hitch Days (OFFSHORE)

Extended Hitch Days (ONSHORE Rotational)

Service during Weekends & Public Holidays

ONSHORE Overtime Hours (Over 8 hours)

OFFSHORE Overtime Hours (Over 12 hours)

Per Diem Days (ONSHORE/OFFSHORE Rotational Personnel)

Training Days

Travel Days

Noc representative appoval's name as approved_by

Noc representative's date approval_date

Noc representative status as approval_status




The output should be formatted as a JSON instance that conforms to the JSON schema below.\n\nAs an example, for the schema {"properties": {"foo": {"title": "Foo", "description": "a list of strings", "type": "array", "items": {"type": "string"}}}, "required": ["foo"]}\nthe object {"foo": ["bar", "baz"]} is a well-formatted instance of the schema. The object {"properties": {"foo": ["bar", "baz"]}} is not well-formatted."""
            ),
            "NOC Basic": (
                "Based on the provided timesheet details, extract the following information:\n"
                "   - Full name of the person\n"
                "   - Position title of the person\n"
                "   - Work location\n"
                "   - Contractor's name\n"
                "   - NOC ID\n"
                "   - Month and year (in MM/YYYY format)"
            ),
            "NOC Structured test": (
                "You are an advanced data extraction assistant. Your task is to parse structured input text and extract key data points into clearly defined categories. Focus only on the requested details, ensuring accuracy and proper grouping. Below is the format for extracting the data:\n\n"
                "---\n"
                "Project Information\n\n"
                "Project Name:\n\n"
                "Project and Package:\n\n"
                "RPO Number:\n\n"
                "PMC Name:\n\n"
                "Project Location:\n\n"
                "Year:\n\n"
                "Month:\n\n"
                "Timesheet Details\n\n"
                "Week X (Date)\n\n"
                "Holidays:\n\n"
                "Regular Hours:\n\n"
                "Overtime Hours:\n\n"
                "Total Hours:\n\n"
                "Comments:\n\n"
                "Additional Data\n\n"
                "Reviewed By:\n\n"
                "Date of Review:\n\n"
                "Position:\n\n"
                "Supervisor Business:\n\n"
                "Date of Approval:\n\n"
                "---\n\n"
                "Ensure the extracted data strictly follows the format above and is organized by category. Ignore unrelated text. Respond only with the formatted output."
            ),
             "Aramco Full Timesheet and Invoice in one structured": (
                """You are a document parsing assistant designed to extract structured data from various document types, including invoices, timesheets, purchase orders, and travel bookings. Your goal is to return highly accurate, properly formatted JSON for each document type.
                General Rules:
                1. Always return ONLY valid JSON—no explanations, comments, or additional text.
                2. Use null for any fields that are not present or cannot be extracted.
                3. Ensure all JSON keys are enclosed in double quotes and properly formatted.
                4. Validate financial, time tracking, and contract details carefully before output.
                
                Extraction Instructions:
                
                1. Invoice:
                   - Parse and extract financial and invoice-specific details.
                   - JSON structure:
                     ```json
                     {
                         "invoice": {
                             "date": null,
                             "dueDate": null,
                             "accountNumber": null,
                             "invoiceNumber": null,
                             "customerContact": null,
                             "kintecContact": null,
                             "accountsContact": null,
                             "periodEnd": null,
                             "contractNo": null,
                             "specialistsName": null,
                             "rpoNumber": null,
                             "assignmentProject": null,
                             "workLocation": null,
                             "expenses": null,
                             "regularHours": null,
                             "overtime": null,
                             "mobilisationAllowance": null,
                             "dailyHousing": null,
                             "opPipTechnical": null,
                             "code": null,
                             "vatBasis": null,
                             "vatRate": null,
                             "vatAmount": null,
                             "totalExclVat": null,
                             "totalInclVat": null
                         }
                     }
                     ```
                
                2. Timesheet:
                   - Extract time tracking, work details, and approvals.
                   - JSON structure:
                     ```json
                     {
                         "timesheet": {
                             "Year": null,
                             "RPO_Number": null,
                             "PMC_Name": null,
                             "Project_Location": null,
                             "Project_and_Package": null,
                             "Month": null,
                             "Timesheet_Details": [
                                 {
                                     "Week": null,
                                     "Regular_Hours": null,
                                     "Overtime_Hours": null,
                                     "Total_Hours": null,
                                     "Comments": null
                                 },
                                 {
                                     "Week": null,
                                     "Regular_Hours": null,
                                     "Overtime_Hours": null,
                                     "Total_Hours": null,
                                     "Comments": null
                                 }
                             ],
                             "Monthly_Totals": {
                                 "Regular_Hours": null,
                                 "Overtime_Hours": null,
                                 "Total_Hours": null
                             },
                             "reviewedBy": {
                                 "name": null,
                                 "position": null,
                                 "date": null
                             },
                             "approvedBy": {
                                 "name": null,
                                 "position": null,
                                 "date": null
                             }
                         }
                     }
                     ```
                
                3. Purchase Order:
                   - Extract contract and pricing details with precision.
                   - JSON structure:
                     ```json
                     {
                         "purchaseOrder": {
                             "contractNo": null,
                             "relPoNo": null,
                             "version": null,
                             "title": null,
                             "startDate": null,
                             "endDate": null,
                             "costCenter": null,
                             "purchasingGroup": null,
                             "contractor": null,
                             "location": null,
                             "workDescription": null,
                             "pricing": {
                                 "regularRate": null,
                                 "overtimeRate": null,
                                 "totalBudget": null
                             }
                         }
                     }
                     ```
                
                4. Travel Booking:
                   - Parse travel-specific and employee information.
                   - JSON structure:
                     ```json
                     {
                         "travelBooking": {
                             "requestId": null,
                             "approvalStatus": null,
                             "employee": {
                                 "name": null,
                                 "id": null,
                                 "email": null,
                                 "firstName": null,
                                 "lastName": null,
                                 "gradeCodeGroup": null
                             },
                             "defaultManager": {
                                 "name": null,
                                 "email": null
                             },
                             "sender": {
                                 "name": null,
                                 "email": null
                             },
                             "travel": {
                                 "startDate": null,
                                 "endDate": null,
                                 "requestPolicy": null,
                                 "requestType": null,
                                 "employeeType": null,
                                 "travelActivity": null,
                                 "tripType": null
                             },
                             "cost": {
                                 "companyCode": null,
                                 "costObject": null,
                                 "costObjectId": null
                             },
                             "transport": {
                                 "type": null,
                                 "comments": null
                             },
                             "changeRequired": null,
                             "comments": null
                         }
                     }
                     ```
                
                Use these structures for parsing documents and ensure compliance with the rules and instructions provided for each type.
                """
            ),
             "Aramco Timesheet only": (
                """ Extract time tracking, work details, and approvals.
                   - JSON structure:
                     ```json
                     {
                         "timesheet": {
                             "Year": null,
                             "RPO_Number": null,
                             "PMC_Name": null,
                             "Project_Location": null,
                             "Project_and_Package": null,
                             "Month": null,
                             "Timesheet_Details": [
                                 {
                                     "Week": null,
                                     "Regular_Hours": null,
                                     "Overtime_Hours": null,
                                     "Total_Hours": null,
                                     "Comments": null
                                 },
                                 {
                                     "Week": null,
                                     "Regular_Hours": null,
                                     "Overtime_Hours": null,
                                     "Total_Hours": null,
                                     "Comments": null
                                 }
                             ],
                             "Monthly_Totals": {
                                 "Regular_Hours": null,
                                 "Overtime_Hours": null,
                                 "Total_Hours": null
                             },
                             "reviewedBy": {
                                 "name": null,
                                 "position": null,
                                 "date": null
                             },
                             "approvedBy": {
                                 "name": null,
                                 "position": null,
                                 "date": null
                             }
                         }
                     }
                     ```"""
            ),
             "NOC Invoice": (
                """You are a highly accurate data extraction system.  Your task is to analyze the provided image of an invoice and extract all data, paying close attention to the structure and formatting of the document.  Organize the extracted data in a clear, structured format, such as JSON.  Do not invent any information. If a field cannot be read with high confidence, indicate that with "UNCLEAR" or a similar designation.  Be as specific as possible, and do not summarize or combine fields unless explicitly indicated.  Do not provide any additional information that is not present in the document.

Here's the expected output format, in JSON, with all required fields:

```json
{
  "invoiceDetails": {
    "pleaseQuote": "string", // Text related to quoting
    "invoiceNumber": "string",
    "workPeriod": "string",
    "invoiceDate": "string",
    "assignmentReference": "string"
  },
  "from": {
    "companyName": "string",
    "addressLine1": "string",
    "addressLine2": "string",
    "city": "string",
    "postalCode": "string",
    "country": "string"
  },
  "to": {
    "companyName": "string",
    "office": "string",
    "floor": "string",
	"building":"string",
    "addressLine1": "string",
    "poBox": "string",
    "city": "string"
  },
  "services": [
    {
      "serviceDetails": "string",
      "fromDate": "string",
      "toDate": "string",
      "currency": "string",
      "fx": "string", //If it is present
      "noOfDays": "number or string (if range)",
      "rate": "number",
      "total": "number"
    },
   // ... more service entries
  ],
  "totals": {
    "subTotal": "number",
    "tax": "number",
    "totalDue": "number"
  },
  "bankDetails": {
    "bankName": "string",
    "descriptionReferenceField": "string",
    "bankAddress": "string",
    "swiftBicCode": "string",
    "ibanNumber": "string",
    "accountNumber": "string",
    "beneficiaryName": "string",
    "accountCurrency": "string",
    "expectedAmount": "string"
  }
}
                    """
            )
        }
        
        # Get the selected prompt
        selected_prompt = prompts.get(prompt_option, "Invalid prompt selected.")
        
        messages = []
        
        # Include document context
        if doc_state.current_doc_images:
            context = f"\nDocument context:\n{doc_state.current_doc_text}" if doc_state.current_doc_text else ""
            current_msg = f"{selected_prompt}{context}"
            messages.append({"role": "user", "content": [{"type": "text", "text": current_msg}, {"type": "image"}]})
        else:
            messages.append({"role": "user", "content": [{"type": "text", "text": selected_prompt}]})

        # Process inputs
        texts = processor.apply_chat_template(messages, add_generation_prompt=True)
        
        try:
            if doc_state.current_doc_images:
                inputs = processor(
                    text=texts,
                    images=doc_state.current_doc_images[0:1],
                    return_tensors="pt"
                ).to("cuda")
            else:
                inputs = processor(text=texts, return_tensors="pt").to("cuda")
                
            streamer = TextIteratorStreamer(processor, skip_special_tokens=True, skip_prompt=True)
            generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens)
            
            thread = Thread(target=model.generate, kwargs=generation_kwargs)
            thread.start()
            
            buffer = ""
            for new_text in streamer:
                buffer += new_text
                time.sleep(0.01)
                yield buffer
                
        except Exception as e:
            logger.error(f"Error in model processing: {str(e)}")
            yield "An error occurred while processing your request. Please try again."
            
    except Exception as e:
        logger.error(f"Error in bot_streaming: {str(e)}")
        yield "An error occurred. Please try again."

def clear_context():
    """Clear the current document context."""
    doc_state.clear()
    return "Document context cleared. You can upload a new document."

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Document Analyzer with Predetermined Prompts")
    gr.Markdown("Upload a PDF or image (PNG, JPG, JPEG, GIF, BMP, WEBP) and select a prompt to analyze its contents.")
    
    with gr.Row():
        file_upload = gr.File(
            label="Upload Document",
            file_types=[".pdf", ".png", ".jpg", ".jpeg", ".gif", ".bmp", ".webp"]
        )
        upload_status = gr.Textbox(
            label="Upload Status",
            interactive=False
        )
    
    with gr.Row():
        prompt_dropdown = gr.Dropdown(
            label="Select Prompt",
            choices=[
                "NOC Timesheet",
                "NOC Invoice",
                "Aramco Full Timesheet and invoice structured",
                "Aramco Timesheet only"
            ],
            value="Options"
        )
        generate_btn = gr.Button("Generate")
    
    clear_btn = gr.Button("Clear Document Context")
    
    output_text = gr.Textbox(
        label="Output",
        interactive=False
    )
    
    file_upload.change(
        fn=process_uploaded_file,
        inputs=[file_upload],
        outputs=[upload_status]
    )
    
    generate_btn.click(
        fn=bot_streaming,
        inputs=[prompt_dropdown],
        outputs=[output_text]
    )
    
    clear_btn.click(
        fn=clear_context,
        outputs=[upload_status]
    )

# Launch the interface
demo.launch(debug=True)