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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 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
with st.sidebar:
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image_url = process_image(uploaded_file)
with st.chat_message("assistant"):
st.write("Hello 👋, upload an image and ask questions related to it!")
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
}
)
with st.spinner('Generating...'):
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
}
)
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