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from PIL import Image
import gradio as gr
from minerva import Minerva
from formatter import AutoGenFormatter
title = "Minerva: AI Guardian for Scam Protection"
description = """
Built with AutoGen 0.4.0 and OpenAI. </br></br>
Minerva analyzes the content of a screenshot for potential scams </br>
and provides an analysis in the language of the extracted text</br></br>
Agents coordinated as an AutoGen Team in a RoundRobin fashion: </br>
- *OCR Specialist* </br>
- *Link Checker* </br>
- *Content Analyst* </br>
- *Decision Maker* </br>
- *Summary Specialist* </br>
- *Language Translation Specialist* </br></br>
Try out one of the examples to perform a scam analysis. </br>
Agentic Workflow is streamed for demonstration purposes. </br></br>
https://github.com/dcarpintero/minerva </br>
Submission for RBI Berkeley, CS294/194-196, LLM Agents (Diego Carpintero)
"""
inputs = gr.components.Image()
outputs = [
gr.components.Textbox(label="Analysis Result"),
gr.HTML(label="Agentic Workflow (Streaming)")
]
examples = "examples"
example_labels = ["EN:Gift:Social", "ES:Banking:Social", "EN:Billing:SMS", "EN:Multifactor:Email", "EN:CustomerService:Twitter"]
model = Minerva()
formatter = AutoGenFormatter()
def to_html(texts):
formatted_html = ''
for text in texts:
formatted_html += text.replace('\n', '<br>') + '<br>'
return f'<pre>{formatted_html}</pre>'
async def predict(img):
try:
img = Image.fromarray(img)
stream = await model.analyze(img)
streams = []
messages = []
async for s in stream:
streams.append(s)
messages.append(await formatter.to_output(s))
yield ["Pondering, stand by...", to_html(messages)]
if streams[-1]:
prediction = streams[-1].messages[-1].content
else:
prediction = "No analysis available. Try again later."
await model.reset()
yield [prediction, to_html(messages)]
except Exception as e:
print(e)
yield ["Error during analysis. Try again later.", ""]
with gr.Blocks() as demo:
with gr.Tab("Minerva: AI Guardian for Scam Protection"):
with gr.Row():
gr.Interface(
fn=predict,
inputs=inputs,
outputs=outputs,
examples=examples,
example_labels=example_labels,
description=description,
).queue(default_concurrency_limit=5)
demo.launch() |