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import streamlit as st
import google.generativeai as genai
import io
import os
from dotenv import load_dotenv
from PIL import Image
load_dotenv()
# Configure the API key
genai.configure(api_key= "AIzaSyBaxMCjBV5fBlsKUmFb-8SGgkiirv1ZKck")
# Set up the model
model = genai.GenerativeModel('gemini-1.5-flash-latest')
def get_gemini_response(image_blob, prompt):
response = model.generate_content([prompt, image_blob])
return response.text
# Streamlit app
st.set_page_config(page_title="Image Insights Generator", page_icon="📷", layout="wide")
# Sidebar with instructions and additional information
st.sidebar.title("Instructions")
st.sidebar.write("""
1. Upload an image using the file uploader.
2. Enter a question about the image in the text input box.
3. Click the "Generate Insights" button to get AI insights about the image.
""")
st.sidebar.markdown("---")
st.sidebar.caption("Created with Streamlit and Gemini Pro Vision")
# Main content
st.title("📷 Image Insights Generator")
st.write("Upload an image and ask a question about it. Our AI will analyze the image and provide insights!")
# File uploader for image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Display the uploaded image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Text input for the prompt
prompt = st.text_input("What would you like to know about this image?", placeholder="Enter your question here...")
if st.button("Generate Insights"):
with st.spinner("Analyzing the image..."):
# Prepare the image for the Gemini API
img_byte_arr = io.BytesIO()
image.save(img_byte_arr, format='PNG')
img_byte_arr = img_byte_arr.getvalue()
# Create a Blob from the image data
image_blob = {
"mime_type": "image/png",
"data": img_byte_arr
}
# Progress bar
progress_bar = st.progress(0)
for i in range(1, 101):
progress_bar.progress(i)
if i == 100:
# Get the response from Gemini Pro Vision
response = get_gemini_response(image_blob, prompt)
# Display the response
st.subheader("🔍 AI Insights:")
st.write(response)
st.balloons() # Add some celebration!
# Custom CSS for styling
st.markdown("""
<style>
body {
font-family: 'Arial', sans-serif;
}
</style>
""", unsafe_allow_html=True)
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