app.py

import streamlit as st
from langchain_community.embeddings import OpenAIEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from dotenv import load_dotenv
from langchain_core.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI
import base64, httpx
from PIL import Image
import io
import os
from Prompts import prompt

# Load environment variables from .env file
load_dotenv()
gemini_api_key = os.getenv("GEMINI_API_KEY")

if "GOOGLE_API_KEY" not in os.environ:
    os.environ["GOOGLE_API_KEY"] = gemini_api_key

model = ChatGoogleGenerativeAI(model="gemini-1.5-flash")  # Initialize the model

# Display title and instructions in the Streamlit app
st.set_page_config("Sagar's Food Classification App")
st.title("Food Classification App")
st.write("Upload an image, and the app will classify if the food is healthy or unhealthy.")

# Sidebar for file uploader with padding
with st.sidebar:
    st.markdown("<div style='padding: 10px;'></div>", unsafe_allow_html=True)
    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

# Main area for displaying image and classification results
if uploaded_file is not None:
    # Display uploaded image on the right side
    image = Image.open(uploaded_file)
    col1, col2 = st.columns([3, 2])  # Adjust column widths (col2 is wider)
    
    with col1:
        st.image(image, caption='Uploaded Image', use_container_width=True)

    # Convert image to byte stream for processing
    img_bytes = io.BytesIO()
    image.save(img_bytes, format='PNG')
    img_data = img_bytes.getvalue()
    image_data = base64.b64encode(img_data).decode("utf-8")

    # Create a message with the image
    message = HumanMessage(
        content=[
            {"type": "text", "text": prompt.foodClassificationPrompts},
            {
                "type": "image_url",
                "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
            },
        ]
    )

    # Process the image and get classification response
    with st.spinner("Classifying..."):
        response = model.invoke([message])

    # Display the response in the main area (right side)
    with col2:
        st.write("Food Classification:")
        st.write(response.content)
else:
    st.sidebar.write("Please upload an image.")