from dotenv import load_dotenv from streamlit_extras.add_vertical_space import add_vertical_space import streamlit as st import os from PIL import Image #load_dotenv() # take environment variables from .env. import google.generativeai as genai #os.getenv("GOOGLE_API_KEY") #genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) ## Function to load Gemini Pro Vision model and get respones def get_gemini_response(input,image,prompt): model = genai.GenerativeModel('gemini-pro-vision') response = model.generate_content([input,image[0],prompt]) return response.text def input_image_setup(uploaded_file): # Check if a file has been uploaded if uploaded_file is not None: # Read the file into bytes bytes_data = uploaded_file.getvalue() image_parts = [ { "mime_type": uploaded_file.type, # Get the mime type of the uploaded file "data": bytes_data } ] return image_parts else: raise FileNotFoundError("No file uploaded") ##initialize our streamlit app st.set_page_config(page_title="GeminiPro Code Explanation") with st.sidebar: st.title('🤗Code Explanation') st.markdown(''' ## About GeminiPro based chatbot built using: - [Gemini Pro](https://deepmind.google/technologies/gemini/pro/) LLM model - [Streamlit](https://streamlit.io/) ''') add_vertical_space(5) st.write('Made with ❤️ by Harry') st.header("Gemini Application") input=st.text_input("Input Prompt: ",key="input") uploaded_file = st.file_uploader("Choose an image contains a snippet of your code...", type=["jpg", "jpeg", "png"]) image="" if uploaded_file is not None: image = Image.open(uploaded_file) st.image(image, caption="Uploaded Image.", use_column_width=True) submit=st.button("Tell me what the code is doing") input_prompt = """ You are an expert in algorithms and can explain different code """ ## If ask button is clicked if submit: image_data = input_image_setup(uploaded_file) response=get_gemini_response(input_prompt,image_data,input) st.subheader("The Response is") st.write(response)