app.py

import os
import base64

import gradio as gr

from openai import AzureOpenAI


def generate_data_uri(png_file_path):

    with open(png_file_path, 'rb') as image_file:
        image_data = image_file.read()
    
    # Encode the binary image data to base64
    base64_encoded_data = base64.b64encode(image_data).decode('utf-8')
    
    # Construct the data URI
    data_uri = f"data:image/png;base64,{base64_encoded_data}"
    
    return data_uri


def decision(png_file_path, client, lmm: str) -> str:

    image_data = generate_data_uri(png_file_path)

    system_message = """
    You are an expert in describing images and plots presented in company annual reports.
    For plots, ensure that you describe the plot and also the key trends/findings observed in the plot.
    Be detailed in your exposition.
    """
    
    decision_prompt = [
        {
            'role': 'system',
            'content': system_message
        },
        {
            'role': 'user', 
            'content': [
                {"type": "image_url", "image_url": {"url": image_data}}
            ]
        }
    ]

    try:
        response = client.chat.completions.create(
            model=lmm,
            messages=decision_prompt,
            temperature=0
        )
        decision = response.choices[0].message.content
        decision = decision.replace('```json\n', '')
        decision = decision.replace('```', '')
    except Exception as e:
        decision = e

    return decision

def predict(image):

    lmm = "gpt-4o-mini"

    client = AzureOpenAI(
        api_key = os.environ["AZURE_OPENAI_KEY"],
        azure_endpoint = os.environ["AZURE_OPENAI_ENDPOINT"],
        api_version = "2024-02-01"
    )
    
    verdict = decision(image, client, lmm)
    return verdict

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="filepath", label="Upload your image"),
    outputs=gr.Text(label="Verdict"),
    title="Plot Explainer",
    description="This web API presents an interface to explain plots in detail.",
    examples='images',
    cache_examples=False,
    theme=gr.themes.Base(),
    concurrency_limit=16
)

demo.queue()
demo.launch(auth=("demouser", os.getenv('PASSWD')))