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import gradio as gr
import base64
import requests
import io
from PIL import Image
import numpy as np
import os
URL = os.environ['URL']
def sketch_to_text(image, api_key):
if image is None or not isinstance(image, dict) or 'composite' not in image:
return "Please write something first."
# Extract the image data from the dictionary
image_data = image['composite']
# Convert the image data to a PIL Image
pil_image = Image.fromarray(image_data.astype(np.uint8))
# Convert the image to base64
buffered = io.BytesIO()
pil_image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
if api_key:
API_KEY = api_key
else:
API_KEY = os.environ['API_KEY']
# Prepare the API request
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}
payload = {
"model": "Llama-3.2-11B-Vision-Instruct",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Please read the forumla in the image and transcribe it into latex code, respond with code only, make sure the code is enclosed within a pair of $$"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{img_str}"
}
}
]
}
],
"max_tokens": 300
}
# Make the API request
response = requests.post(URL, headers=headers, json=payload)
if response.status_code == 200:
return response.json()["choices"][0]["message"]["content"], response.json()["choices"][0]["message"]["content"]
else:
return f"Error: {response.status_code}, {response.text}", f"Error: {response.status_code}, {response.text}"
# Create the Gradio interface
with gr.Blocks() as iface:
gr.Markdown("# Pix2Latex")
gr.Markdown("Transcribing handwritten forumla into latex with Llama3.2 instruct. [Powered by SambaNova Cloud, Get Your API Key Here](https://cloud.sambanova.ai/apis)")
with gr.Row():
api_key = gr.Textbox(label="API Key", type="password", placeholder="(Optional) Enter your API key here for more availability. ")
with gr.Column(scale=1):
input_image = gr.ImageEditor()
with gr.Row():
with gr.Column(scale=1):
output1 = gr.Textbox(label="Raw")
with gr.Column(scale=1):
output2 = gr.Markdown(label="Rendered")
input_image.change(fn=sketch_to_text, inputs=[input_image, api_key], outputs=[output1, output2])
gr.Markdown("How to use: 1. write your formula in the box above. 2. See it in real time, have fun doing math?")
# Launch the app
iface.launch() |