app.py

import gradio as gr
from paddleocr import PaddleOCR
from groq import Groq
from openai import OpenAI
import os
import json

##################################
# Initialize Models
##################################
print("Loading PaddleOCR model...")

# Available languages in PaddleOCR
AVAILABLE_LANGUAGES = {
    'English': 'en',
    'Chinese Simplified': 'ch',
    'French': 'fr',
    'German': 'german',
    'Korean': 'korean',
    'Japanese': 'japan',
    'Italian': 'it',
    'Spanish': 'es',
    'Portuguese': 'pt',
    'Russian': 'ru',
    'Arabic': 'ar',
    'Hindi': 'hi',
    'Vietnamese': 'vi',
    'Thai': 'th'
}

# Available LLM providers
PROVIDERS = ["None", "Groq", "OpenAI"]

# Dictionary to store OCR models for different languages
ocr_models = {}

def get_ocr_model(lang_code):
    if lang_code not in ocr_models:
        ocr_models[lang_code] = PaddleOCR(
            use_angle_cls=True, 
            lang=lang_code, 
            show_log=False,
            enable_mkldnn=True  # Better CPU performance
        )
    return ocr_models[lang_code]

##################################
# Groq Processing Functions
##################################
def format_with_groq(text: str, api_key: str) -> str:
    client = Groq(api_key=api_key)
    completion = client.chat.completions.create(
        model="llama3-8b-8192",
        messages=[
            {
                "role": "system",
                "content": (
                    "You are a receipt data extraction expert. Extract and format the receipt data into a clear JSON structure.\n"
                    "Look for these key pieces of information:\n"
                    "1. Restaurant/store name\n"
                    "2. Restaurant Address /store address\n"
                    "3. Date and time\n"
                    "4. Individual items with quantities and prices\n"
                    "5. Table number if present\n"
                    "6. Server name if present\n"
                    "7. Payment details\n"
                    "8. Receipt/order number\n"
                    "Format numbers as actual numbers, not strings."
                )
            },
            {
                "role": "user",
                "content": f"Convert this receipt text to structured data:\n\n{text}"
            }
        ],
        temperature=0.1,
        max_tokens=1024,
        top_p=1,
        stream=True
    )
    
    formatted_text = ""
    for chunk in completion:
        content = getattr(chunk.choices[0].delta, "content", None)
        if content:
            formatted_text += content
    
    return formatted_text.strip()

def refine_json_with_groq(initial_text: str, api_key: str) -> str:
    client = Groq(api_key=api_key)
    completion = client.chat.completions.create(
        model="llama3-8b-8192",
        messages=[
            {
                "role": "system",
                "content": (
                    "Convert the receipt data into this exact JSON format:\n"
                    "{\n"
                    "  'restaurant_name': string,\n"
                    "  'restaurant_address': string,\n"
                    "  'date': string,\n"
                    "  'time': string,\n"
                    "  'table_number': string or number,\n"
                    "  'server_name': string,\n"
                    "  'payment_method': string,\n"
                    "  'items': [{'name': string, 'quantity': number, 'price': number}],\n"
                    "  'subtotal': number,\n"
                    "  'tax': number,\n"
                    "  'tip': number or null,\n"
                    "  'total': number,\n"
                    "  'receipt_number': string or null\n"
                    "}\n"
                    "Rules:\n"
                    "1. Use ONLY double quotes for JSON compliance\n"
                    "2. All numbers must be actual numbers, not strings\n"
                    "3. Return ONLY the JSON, no explanations\n"
                    "4. Ensure math is correct"
                )
            },
            {
                "role": "user",
                "content": f"Format this receipt data as valid JSON:\n\n{initial_text}"
            }
        ],
        temperature=0.1,
        max_tokens=1024,
        top_p=1,
        stream=True
    )
    
    refined_text = ""
    for chunk in completion:
        content = getattr(chunk.choices[0].delta, "content", None)
        if content:
            refined_text += content
    
    try:
        # Clean up any potential extra text
        json_start = refined_text.find('{')
        json_end = refined_text.rfind('}') + 1
        if json_start >= 0 and json_end > 0:
            refined_text = refined_text[json_start:json_end]
        
        # Validate JSON and reformat
        parsed_json = json.loads(refined_text)
        return json.dumps(parsed_json, indent=2)
    except json.JSONDecodeError:
        return refined_text

##################################
# OpenAI Processing Functions
##################################
def process_with_openai(text: str, api_key: str) -> dict:
    client = OpenAI(api_key=api_key)
    try:
        completion = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {
                    "role": "system",
                    "content": (
                        "Convert the receipt data into this exact JSON format:\n"
                        "{\n"
                        "  'restaurant_name': string,\n"
                        "  'restaurant_address': string,\n"
                        "  'date': string,\n"
                        "  'time': string,\n"
                        "  'table_number': string or number,\n"
                        "  'server_name': string,\n"
                        "  'payment_method': string,\n"
                        "  'items': [{'name': string, 'quantity': number, 'price': number}],\n"
                        "  'subtotal': number,\n"
                        "  'tax': number,\n"
                        "  'tip': number or null,\n"
                        "  'total': number,\n"
                        "  'receipt_number': string or null\n"
                        "}\n"
                        "Rules:\n"
                        "1. Use ONLY double quotes for JSON compliance\n"
                        "2. All numbers must be actual numbers, not strings\n"
                        "3. Return ONLY the JSON, no explanations"
                    )
                },
                {
                    "role": "user",
                    "content": f"Convert this receipt text to JSON:\n\n{text}"
                }
            ],
            temperature=0.1
        )
        return completion.choices[0].message.content
    except Exception as e:
        return json.dumps({"error": str(e)})

##################################
# Main Processing
##################################
def process_receipt(image, selected_language, provider="None", api_key=""):
    try:
        os.makedirs("temp", exist_ok=True)
        
        image_path = os.path.join("temp", "temp_image.jpg")
        image.save(image_path)
        
        # Get OCR model and process image
        lang_code = AVAILABLE_LANGUAGES[selected_language]
        ocr_model = get_ocr_model(lang_code)
        result = ocr_model.ocr(image_path, cls=True)
        
        # Extract text from results
        extracted_text = "\n".join([line[1][0] for page in result for line in page])
        
        # If no provider/api key, return raw OCR
        if not api_key or provider == "None":
            return {
                "raw_ocr_text": extracted_text,
                "note": "Provide API key and select a provider for structured JSON output"
            }
        
        try:
            if provider == "Groq":
                # Two-step Groq processing
                initial_text = format_with_groq(extracted_text, api_key)
                final_json = refine_json_with_groq(initial_text, api_key)
                return json.loads(final_json)
            
            elif provider == "OpenAI":
                # OpenAI processing
                result = process_with_openai(extracted_text, api_key)
                return json.loads(result)
            
        except json.JSONDecodeError:
            return {
                "error": "Failed to parse response",
                "raw_ocr_text": extracted_text
            }

    except Exception as e:
        return {
            "error": str(e),
            "type": "processing_error"
        }
    finally:
        if os.path.exists(image_path):
            try:
                os.remove(image_path)
            except:
                pass

##################################
# Gradio Interface
##################################
css = """
.gradio-container {max-width: 1100px !important}
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown("# Multi-Language Receipt OCR")
    
    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.Image(
                type="pil",
                label="Upload Receipt Image",
                height=400
            )
            language_dropdown = gr.Dropdown(
                choices=list(AVAILABLE_LANGUAGES.keys()),
                value="English",
                label="Select Language",
                info="Choose the primary language of the receipt"
            )
            
            with gr.Row():
                provider_dropdown = gr.Dropdown(
                    choices=PROVIDERS,
                    value="None",
                    label="Select LLM Provider",
                    info="Choose provider for JSON formatting"
                )
                api_key_input = gr.Textbox(
                    label="API Key",
                    placeholder="Enter your API key",
                    type="password",
                    info="Required for JSON formatting"
                )
            
            submit_button = gr.Button("Process Receipt", variant="primary")
        
        with gr.Column(scale=1):
            json_output = gr.JSON(
                label="Extracted Receipt Data",
                height=500
            )

    gr.Markdown("""
    ### Usage Instructions
    1. Upload a clear image of your receipt
    2. Select the receipt's primary language
    3. (Optional) Choose a provider and enter API key for JSON formatting
    4. Click 'Process Receipt'
    
    ### Notes
    - Without an API key, you'll receive raw OCR text
    - For best results, ensure receipt image is clear and well-lit
    - Supported languages include English, Chinese, French, German, and more
    """)
    
    submit_button.click(
        fn=process_receipt,
        inputs=[
            image_input,
            language_dropdown,
            provider_dropdown,
            api_key_input
        ],
        outputs=[json_output],
    )

# Close any existing gradio instances
gr.close_all()

# Launch the app
demo.queue(max_size=10)
demo.launch(
    server_name="0.0.0.0",
    server_port=7860,
    show_api=False,
    share=False
)