app.py

import PIL.Image
import gradio as gr
import base64
import time
import os
import google.generativeai as genai
from dotenv import load_dotenv
load_dotenv()



# Set Google API key
genai.configure(api_key=os.getenv("'GOOGLE_API_KEY'"))

# os.environ['GOOGLE_API_KEY'] = ""
# genai.configure(api_key=os.environ['GOOGLE_API_KEY'])

# Create the Model
txt_model = genai.GenerativeModel('gemini-pro')
vis_model = genai.GenerativeModel('gemini-pro-vision')


# 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')


# Function that takes User Inputs and displays it on ChatUI
def query_message(history, txt, img):
    if not img:
        history.append((txt, None))
        return history
    base64_data = image_to_base64(img)
    data_url = f"data:image/jpeg;base64,{base64_data}"
    history.append((f"{txt} ![]({data_url})", None))
    return history


# Function that takes User Inputs, generates Response and displays on Chat UI
def llm_response(history, text, img):
    if not img:
        response = txt_model.generate_content(text)
        history.append((None, response.text))
        return history

    else:
        img = PIL.Image.open(img)
        response = vis_model.generate_content([text, img])
        history.append((None, response.text))
        return history


# Interface Code
with gr.Blocks() as app:
    with gr.Row():
        image_box = gr.Image(type="filepath")

        chatbot = gr.Chatbot(
            scale=2,
            height=600
        )
    text_box = gr.Textbox(
        placeholder="Enter text and press enter, or upload an image",
        container=False,
    )

    btn = gr.Button("Submit")
    clicked = btn.click(query_message,
                        [chatbot, text_box, image_box],
                        chatbot
                        ).then(llm_response,
                               [chatbot, text_box, image_box],
                               chatbot
                               )
app.queue()
app.launch(debug=True)