app.py

# Load all environment variables
from dotenv import load_dotenv
load_dotenv()

# Import required packages and libraries
import streamlit as st  # Streamlit to create a web application
import os
import google.generativeai as genai  # The primary LLM model that resides inside genAI library
from PIL import Image

# Fetch the API key that is defined inside .env
google_api_key = os.getenv("GOOGLE_API_KEY")

if google_api_key is None:
    st.error("API key not found. Please check your .env file.")
    st.stop()

# Configure genAI library with the fetched API key
genai.configure(api_key=google_api_key)

# Function that loads Gemini Model and returns the response
def get_model_response(query, user_image):
    # Create a GenerativeModel instance with the 'gemini-pro-vision' model
    model = genai.GenerativeModel('gemini-pro-vision')

    # Check if a query is provided and generate content accordingly
    if query != "":
        model_response = model.generate_content([query, user_image])
    else:
        model_response = model.generate_content(user_image)

    # Extract and return the text from the model response
    return model_response.text

# Set page configuration
st.set_page_config(page_title="Visualize Image-Text")
st.header("Poulta App Image Analyses")

# Add logo
logo_image_url = "https://poulta.com/assets/site-images/poulta-logo.png"
st.image(logo_image_url, width=200)  # Adjust width as needed

# User input for query
user_input = st.text_input("What do you want to know about the image? ", key="user_input")

# File uploader for image
uploaded_file = st.file_uploader("Pick your image...", 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)

# Button to trigger image analysis
submit = st.button("See the magic ✨")
if submit:
    # Perform model analysis and display results
    model_response = get_model_response(user_input, image)
    st.subheader("Results here 👇🏻")
    st.write(model_response)