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import base64
import requests
from app_config import config
import gradio as gr
import mongo_utils as mongo
def clear():
return None, ""
# Function to encode the local image into base64 to be send over HTTP
def __encode_image(image_path: str) -> bytes:
"""
Encodes the passed image to base64 byte-stream.
This is required for passing the image over a HTTP get/post call.
"""
with open(image_path, "rb") as image_file:
base64_image = base64.b64encode(image_file.read()).decode("utf-8")
return base64_image
def solve(image_path: str):
"""
Invokes the GuruZee API passing the raw image bytes and returns the response
to client
"""
image_data = __encode_image(image_path=image_path)
headers = {"Content-Type": "application/json"}
payload = {"data": image_data}
end_point = config.GURUZEE_API_ENDPOINT + "/solve"
response = requests.post(end_point, headers=headers, json=payload)
# increment the openai access counter and compute count stats
mongo.increment_curr_access_count()
max_count = config.openai_max_access_count
curr_count = config.openai_curr_access_count
available_count = max_count - curr_count
return response.json(), max_count, curr_count, available_count
def create_interface():
js_enable_darkmode = """() =>
{
document.querySelector('body').classList.add('dark');
}"""
js_toggle_darkmode = """() =>
{
if (document.querySelectorAll('.dark').length) {
document.querySelector('body').classList.remove('dark');
} else {
document.querySelector('body').classList.add('dark');
}
}"""
with gr.Blocks(title=config.title, theme=config.theme, css=config.css) as app:
# enable darkmode
app.load(fn=None, inputs=None, outputs=None, _js=js_enable_darkmode)
with gr.Row():
darkmode_checkbox = gr.Checkbox(
label="Dark Mode", value=True, interactive=True
)
# toggle darkmode on/off when checkbox is checked/unchecked
darkmode_checkbox.change(
None, None, None, _js=js_toggle_darkmode, api_name=False
)
with gr.Row():
with gr.Column():
gr.Markdown(
"""
# GuruZee
***Where Math Meets Marvel with AI Wizardry for Primary School
Prowess! 🧙♂📚✨***
**GuruZee whips up question papers with answers and detailed
explanations, all based on those elusive course chapter images.
Not just that, it's a math-solving maestro, tackling problems
presented as images with ease..
<br>
Select/upload an `Image` containing the problem Then hit
`GO!` button.
Alternatively, just select one of the pre-configured `Example` image
from the Example section at the bottom**
<br>
Visit the [project's repo](https://github.com/sssingh/GuruZee)
<br>
***Please exercise patience, as the models employed are extensive
and may require a few seconds to load. If you encounter an unrelated
response, it is likely still loading; wait a moment and try again.***
"""
)
with gr.Column():
max_count = gr.Textbox(
label="Max allowed OpenAI requests:",
value=config.openai_max_access_count,
)
curr_count = gr.Textbox(
label="Used up OpenAI requests:",
value=config.openai_curr_access_count,
)
available_count = gr.Textbox(
label="Available OpenAI requests:",
value=config.openai_max_access_count
- config.openai_curr_access_count,
)
with gr.Row():
with gr.Column():
image = gr.Image(
type="filepath",
)
with gr.Row():
submit_button = gr.Button(value="GO!", elem_classes="orange-button")
clear_button = gr.ClearButton(elem_classes="gray-button")
with gr.Column():
answer = gr.Textbox(
label="Answer:",
placeholder="Answer will appear here.",
lines=20,
)
with gr.Row():
with gr.Accordion("Expand for examples:", open=False):
gr.Examples(
examples=[
[
"assets/examples/1.png",
],
[
"assets/examples/2.png",
],
[
"assets/examples/3.png",
],
[
"assets/examples/4.png",
],
[
"assets/examples/5.png",
],
[
"assets/examples/6.png",
],
[
"assets/examples/7.png",
],
[
"assets/examples/8.png",
],
[
"assets/examples/9.png",
],
[
"assets/examples/10.png",
],
],
fn=solve,
inputs=[image],
outputs=[answer, max_count, curr_count, available_count],
run_on_click=True,
)
submit_button.click(
fn=solve,
inputs=[image],
outputs=[answer, max_count, curr_count, available_count],
)
clear_button.click(fn=clear, inputs=[], outputs=[image, answer])
image.clear(fn=clear, inputs=[], outputs=[image, answer])
return app
if __name__ == "__main__":
mongo.fetch_curr_access_count()
app = create_interface()
app.launch()
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