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app.py

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+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. Date and time\n"
+                    "3. Individual items with quantities and prices\n"
+                    "4. Table number if present\n"
+                    "5. Server name if present\n"
+                    "6. Payment details\n"
+                    "7. 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"
+                    "  '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"
+                        "  '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
+)

requirements.txt

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+paddlepaddle
+paddleocr>=2.0.1
+gradio==4.14.0
+groq==0.3.2
+openai==1.11.0
+Pillow==10.0.0
+numpy>=1.21.6