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"""
app.py
"""
import base64
from io import BytesIO
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\nONLY reply with a table.\n\Follow this schema:\n1. item (str), name of the item\n2. price (float), price of the item\n3. quantity (int), quantity of the item\n4. total (float), total cost for the item.\n\nRemember to just end with the table."
},
{
"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.Image(type="pil", label="Upload Receipt Image"),
outputs=gr.Markdown(),
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()
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