app.py

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


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


st.set_page_config(page_title="Animal Species Identifier🐾", layout="wide", page_icon="🐾")

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


file_name = st.file_uploader("Upload an animal image 📸")

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 = animal_pipeline(image)
    col2.markdown("### 🐾 Predictions & Confidence Levels")

    for p in predictions[:3]:
        species = p['label'].lower()
        confidence = round(p['score'] * 100, 1)
        col2.subheader(f"**{species.capitalize()}**: {confidence}%")

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
)