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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} ", 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)
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