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| # Import libraries | |
| from dotenv import load_dotenv | |
| import os | |
| import google.generativeai as genai | |
| from PIL import Image | |
| import streamlit as st | |
| # Load API Key | |
| load_dotenv() | |
| genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) | |
| # Function to load Google Gemini Vision model and get response | |
| def get_response_code(image, prompt): | |
| model = genai.GenerativeModel('gemini-pro-vision') | |
| response = model.generate_content([image[0], prompt]) | |
| return response.text | |
| # Function to preprocess image data | |
| def prep_image(uploaded_file): | |
| # Check if there is any data | |
| if uploaded_file is not None: | |
| # Read the file as bytes | |
| bytes_data = uploaded_file.getvalue() | |
| # Get the image part information | |
| image_parts = [ | |
| { | |
| "mime_type": uploaded_file.type, | |
| "data": bytes_data | |
| } | |
| ] | |
| return image_parts | |
| else: | |
| raise FileNotFoundError("No File is uploaded!") | |
| # Configuring Streamlit App | |
| st.set_page_config(page_title="Code Interpreter") | |
| st.image('LOGO.jpg', width=70) | |
| st.header("Code Interpreter") | |
| # Section for Code Interpreter | |
| upload_code_file = st.file_uploader("Choose an image of code...", type=["jpg", "jpeg", "png"]) | |
| if upload_code_file is not None: | |
| # Show the uploaded code image | |
| code_image = Image.open(upload_code_file) | |
| st.image(code_image, caption="Uploaded Code Image", use_column_width=True) | |
| # Prompt Template for code interpretation | |
| input_prompt_code = """ | |
| CONVERT THE IMAGES OF THE MCQ TO TEXT DONT FORGET THE OPTIONS | |
| """ | |
| # Button for code interpretation | |
| submit = st.button("Interpret Code!") | |
| if submit: | |
| code_image_data = prep_image(upload_code_file) | |
| response = get_response_code(code_image_data, input_prompt_code) | |
| st.subheader("Code AI: ") | |
| st.write(response) | |
| else: | |
| st.info("Please upload an image of the code to proceed.") | |