import streamlit as st
import base64
import os
import requests
from PIL import Image
from io import BytesIO

# Function to compress and resize the image before base64 encoding
def compress_and_resize_image(image, max_size=(1024, 1024), quality=85):
    img = Image.open(image)
    img.thumbnail(max_size)  # Resize image while maintaining aspect ratio
    with BytesIO() as byte_io:
        img.save(byte_io, format="JPEG", quality=quality)  # Save with reduced quality
        byte_io.seek(0)
        return byte_io

# Function to convert uploaded image to base64
def convert_image_to_base64(image):
    compressed_image = compress_and_resize_image(image)
    image_bytes = compressed_image.read()
    encoded_image = base64.b64encode(image_bytes).decode("utf-8")
    return encoded_image

# Function to generate caption using Nebius API
def generate_caption(encoded_image):
    API_URL = "https://api.studio.nebius.ai/v1/chat/completions"
    API_KEY = os.environ.get("NEBIUS_API_KEY")

    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }

    payload = {
        "model": "Qwen/Qwen2-VL-72B-Instruct",
        "messages": [
            {
                "role": "system",
                "content": """You are an image to prompt converter. Your work is to observe each and every detail of the image and craft a detailed prompt under 75 words in this format: [image content/subject, description of action, state, and mood], [art form, style], [artist/photographer reference if needed], [additional settings such as camera and lens settings, lighting, colors, effects, texture, background, rendering]."""
            },
            {
                "role": "user",
                "content": "Write a caption for this image"
            },
            {
                "role": "user",
                "content": f"data:image/png;base64,{encoded_image}"  # This is where the image is passed as base64 directly
            }
        ],
        "temperature": 0
    }

    # Send request to Nebius API
    response = requests.post(API_URL, headers=headers, json=payload)

    if response.status_code == 200:
        result = response.json()
        caption = result.get("choices", [{}])[0].get("message", {}).get("content", "No caption generated.")
        return caption
    else:
        st.error(f"API Error {response.status_code}: {response.text}")
        return None

# Streamlit app layout
def main():
    st.set_page_config(page_title="Image Caption Generator", layout="centered", initial_sidebar_state="collapsed")
    st.title("🖼️ Image to Caption Generator")

    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

    if uploaded_file:
        # Display the uploaded image
        st.image(uploaded_file, caption="Uploaded Image", use_container_width=True)

        if st.button("Generate Caption"):
            # Convert the uploaded image to base64
            with st.spinner("Generating caption..."):
                encoded_image = convert_image_to_base64(uploaded_file)

                # Debugging: Ensure the encoded image is valid and not too large
                st.write(f"Encoded image length: {len(encoded_image)} characters")

                # Get the generated caption from the API
                caption = generate_caption(encoded_image)

                if caption:
                    st.subheader("Generated Caption:")
                    st.text_area("", caption, height=100, key="caption_area")
                    st.success("Caption generated successfully!")

if __name__ == "__main__":
    main()