page2.py

import streamlit as st
from langchain_core.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI
from streamlit_chat import message
from PIL import Image
import base64
import io
from IPython.display import display
from IPython.display import Markdown

import pathlib
import textwrap
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory

# Streamlit app
def image():


    def process_image(uploaded_file):
        # Display the uploaded image
        image = Image.open(uploaded_file)
        st.image(image, caption='Uploaded Image', use_column_width=True)

        # Process the image and return the URL or other information
        # For demonstration purposes, convert the image to base64 and return a data URL
        buffered = io.BytesIO()
        image.save(buffered, format="JPEG")
        image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
        image_url = f"data:image/jpeg;base64,{image_base64}"

        return image_url
    apiKey = "AIzaSyAXkkcrrUBjPEgj93tZ9azy7zcS1wI1jUA"

    llm = ChatGoogleGenerativeAI(model="gemini-pro-vision", google_api_key=apiKey)

    image_url = None  # Initialize image_url outside the if statement

    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
    if uploaded_file is not None:
        image_url = process_image(uploaded_file)


    if 'messages' not in st.session_state:
        st.session_state['messages'] = []

    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])
    prompt = st.chat_input("Say something")
    message = HumanMessage(
        content=[
            {
                "type": "text",
                "text": prompt,
            },  # You can optionally provide text parts
            {"type": "image_url", "image_url": image_url},
        ]
    )

    if prompt:
        with st.chat_message("user").markdown(prompt):
            st.session_state.messages.append(
                {
                    "role": "user",
                    "content": prompt
                }
            )
        response = llm.invoke([message])
        text_output = response.content

        with st.chat_message("assistant").markdown(text_output):
            st.session_state.messages.append(
                {
                    "role": "assistant",
                    "content": text_output
                }
            )