import streamlit as st
from transformers import pipeline
from PIL import Image
from streamlit_extras.add_vertical_space import add_vertical_space


flower_pipeline = pipeline(task="image-classification", model="microsoft/resnet-50")


st.set_page_config(page_title="Flower Identifier 🌸", layout="wide", page_icon="🌼")


st.markdown(
    """
    <div style="text-align: center; padding: 10px;">
        <h1 style="color: #2D6A4F; font-size: 50px;">Flower Identifier 🌸</h1>
        <p style="color: #40916C; font-size: 20px;">Snap it, upload it, and identify the bloom!</p>
    </div>
    """, 
    unsafe_allow_html=True
)


file_name = st.file_uploader("Upload a flower image 📸", type=["jpg", "jpeg", "png"])

add_vertical_space(1)

if file_name is not None:
    col1, col2 = st.columns([1, 2])

    image = Image.open(file_name)
    col1.image(
        image, 
        use_container_width=True, 
        caption="Uploaded Image", 
        output_format="auto"
    )


    predictions = flower_pipeline(image)

    col2.markdown("### 🌺 Predictions & Confidence Levels")
    
    for p in predictions:
        col2.write(f"**{p['label']}**")
        col2.progress(p["score"])


st.markdown(
    """
    <hr style="border-top: 3px solid #40916C;">
    <div style="text-align: center;">
        <p style="color: #1B4332;">Powered by AgentsValley 🌿</p>
    </div>
    """, 
    unsafe_allow_html=True
)