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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 TO TEXT , IF THERE ARE OPTIONS DO NOT FORGET TO INCLUDE OPTIONS IF THERE ARE THERE
"""
# 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.")