app.py

import PIL.Image
import gradio as gr
import base64
import time
import os
import google.generativeai as genai

import pathlib

txt_model = genai.GenerativeModel('gemini-pro')
vis_model = genai.GenerativeModel('gemini-pro-vision')

txt_prompt_1 = 'I have upload the image. The image contains two sample images, A images contains 4 objects--Lens, Aducam Board, Anti-Static Strap, and Raspberry Pi Board. Determine if all 4 objects are also in Image B. If missing, list the names.' 
txt_display_1 = 'name the missing items on B'

import os

GOOGLE_API_KEY=os.getenv('GOOGLE_API_KEY')

genai.configure(api_key=GOOGLE_API_KEY)

# Image to Base 64 Converter
def image_to_base64(image_path):
    with open(image_path, 'rb') as img:
        encoded_string = base64.b64encode(img.read())
    return encoded_string.decode('utf-8')


def output_query_message(img):
    if not img:
        return txt_prompt_1
    base64 = image_to_base64(img)
    data_url = f"data:image/jpeg;base64,{base64}"
    outputText = [(f"{txt_display_1} ![]({data_url})", None)]
    return outputText

# Function that takes User Inputs, generates Response and displays on Chat UI
def output_llm_response(img):
    if not img:
        response = txt_model.generate_content(txt_prompt_1)
        return response.text

    else:
        img = PIL.Image.open(img)
        response = vis_model.generate_content([txt_prompt_1,img])
        return response.text


    
with gr.Blocks(theme='snehilsanyal/scikit-learn') as app1:
    with gr.Column():    
        outputbox = gr.Textbox(label="line clearance...")
        image_box = gr.Image(type="filepath")
        
    btn = gr.Button("Check This")
    clicked = btn.click(output_query_message,
                        [image_box],
                        outputbox
                        ).then(output_llm_response,
                                [image_box],
                                outputbox
                                )
    gr.Markdown("""
    ## SOP-302: Line Clearance ##
    
    <h5 align="center"><i>"XXXX here here."</i></h5>
    
    Multimodal-CoT incorporates vision features in a decoupled training framework. The framework consists of two training stages: (i) rationale generation and (ii) answer inference. Both stages share the same model architecture but differ in the input and output.
    """)


app1.queue()
app1.launch()