app.py

"""
app.py
"""

import base64

import gradio as gr
from groq import Groq

# Initialize Groq client
client = Groq()

# Function to encode the image in base64
def encode_image_to_base64(image):
    # Convert PIL image to base64 string
    buffered = BytesIO()
    image.save(buffered, format="JPEG")
    return base64.b64encode(buffered.getvalue()).decode('utf-8')

# Function to process the uploaded image and extract receipt information
def extract_receipt_info(image):
    # Encode the image to base64
    base64_image = encode_image_to_base64(image)
`
    # Send request to Groq API
    chat_completion = client.chat.completions.create(
        model="llama-3.2-11b-vision-preview",
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "Your task is to extract key information from the provided receipt image.\n\nReply in table.\n\nThis is the schema:\n- item (str), description of the item\n- price (float), price of the item\n- quantity (int), quantity of the item\n- total (float), total cost for the item"
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}",
                        },
                    },
                ],
            },
            {
                "role": "assistant",
                "content": "```markdown"
            }
        ],
        temperature=0.1,
        max_tokens=8192,
        top_p=1,
        stop="```",
    )

    # Return the response from the model
    return chat_completion.choices[0].message.content

# Create the Gradio app
def gradio_app():
    # Gradio interface
    gr.Interface(
        fn=extract_receipt_info,
        inputs=gr.inputs.Image(type="pil", label="Upload Receipt Image"),
        outputs="text",
        title="Receipt Information Extractor",
        description="Upload a receipt image and the model will extract the items, quantities, and prices from the receipt."
    ).launch()

# Start the Gradio app
if __name__ == "__main__":
    gradio_app()