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import os
from PIL import Image
import streamlit as st
from streamlit_option_menu import option_menu
from gemini_utility import (load_gemini_pro_model,
gemini_pro_response,
gemini_pro_vision_response,
embeddings_model_response)
working_dir = os.path.dirname(os.path.abspath(__file__))
st.set_page_config(
page_title="Gemini AI",
page_icon="🧠",
layout="centered",
)
with st.sidebar:
selected = option_menu('Gemini AI',
['ChatBot',
'Image Captioning',
'Embed text',
'Ask me anything'],
menu_icon='robot', icons=['chat-dots-fill', 'image-fill', 'textarea-t', 'patch-question-fill'],
default_index=0
)
# Function to translate roles between Gemini-Pro and Streamlit terminology
def translate_role_for_streamlit(user_role):
if user_role == "model":
return "assistant"
else:
return user_role
# chatbot page
if selected == 'ChatBot':
model = load_gemini_pro_model()
# Initialize chat session in Streamlit if not already present
if "chat_session" not in st.session_state: # Renamed for clarity
st.session_state.chat_session = model.start_chat(history=[])
# Display the chatbot's title on the page
st.title("🤖 ChatBot")
# Display the chat history
for message in st.session_state.chat_session.history:
with st.chat_message(translate_role_for_streamlit(message.role)):
st.markdown(message.parts[0].text)
# Input field for user's message
user_prompt = st.chat_input("Ask Gemini-Pro...") # Renamed for clarity
if user_prompt:
# Add user's message to chat and display it
st.chat_message("user").markdown(user_prompt)
# Send user's message to Gemini-Pro and get the response
gemini_response = st.session_state.chat_session.send_message(user_prompt) # Renamed for clarity
# Display Gemini-Pro's response
with st.chat_message("assistant"):
st.markdown(gemini_response.text)
# Image captioning page
if selected == "Image Captioning":
st.title("📷 Snap Narrate")
uploaded_image = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
if st.button("Generate Caption"):
image = Image.open(uploaded_image)
col1, col2 = st.columns(2)
with col1:
resized_img = image.resize((800, 500))
st.image(resized_img)
default_prompt = "write a short caption for this image" # change this prompt as per your requirement
# get the caption of the image from the gemini-pro-vision LLM
caption = gemini_pro_vision_response(default_prompt, image)
with col2:
st.info(caption)
# text embedding model
if selected == "Embed text":
st.title("🔡 Embed Text")
# text box to enter prompt
user_prompt = st.text_area(label='', placeholder="Enter the text to get embeddings")
if st.button("Get Response"):
response = embeddings_model_response(user_prompt)
st.markdown(response)
# text embedding model
if selected == "Ask me anything":
st.title("❓ Ask me a question")
# text box to enter prompt
user_prompt = st.text_area(label='', placeholder="Ask me anything...")
if st.button("Get Response"):
response = gemini_pro_response(user_prompt)
st.markdown(response) |