Example_2.py

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.prompts import ChatPromptTemplate
from dotenv import load_dotenv
import base64
import os
import streamlit as st

# Load the Gemini API key and set it to the model
load_dotenv()
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", api_key=GEMINI_API_KEY)

def encode_image(image_file):
	return base64.b64encode(image_file.read()).decode()

# Built a web app interface using Streamlit
st.title("Image Analysis Using Gemini")

col1, col2 = st.columns(2)

with col1:
    uploaded_file = st.file_uploader("Upload your image", type=["jpg", "png"])

with col2:
    if uploaded_file is not None:
        st.image(uploaded_file)
    else:
        st.write("Image Preview")

question = st.text_input("Enter a question")

if question and uploaded_file is not None:
	image = encode_image(uploaded_file)

	# Create a chat prompt template to specify the image analytic role for LLM
	prompt = ChatPromptTemplate.from_messages(
			[
					("system", "You are a helpful assistant that can describe images."),
					(
							"human",
							[
									{"type": "text", "text": "{input}"},
									{
											"type": "image_url",
											"image_url": {
													"url": f"data:image/jpeg;base64,{image}",
													"detail": "low",
											},
									},
							],
					),
			]
	)

	# Invoke the LLM to describe the image details
	chain = prompt | llm
     
	res = chain.invoke({"input": question, "image": image})
	st.write(res.content)