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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) |