app.py

import streamlit as st
import io
from PIL import Image
import os
from src.cnnClassfier.pipeline.predict import Prediction

st.set_page_config(page_title="Chicken Health Predictor", page_icon="🐔", layout="wide")

st.title("🐔 Chicken Health Predictor")
st.markdown("### Upload an image to predict if the chicken is healthy or has coccidiosis")

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

col1, col2 = st.columns(2)

if uploaded_file is not None:
    image = Image.open(uploaded_file)
    col1.image(image, caption="Uploaded Image", use_column_width=True)
    
    # Save the uploaded file temporarily
    temp_file = "temp_image.jpg"
    image.save(temp_file)
    
    with st.spinner("Analyzing the image..."):
        predictor = Prediction(temp_file)
        prediction = predictor.predict()
    
    # Remove the temporary file
    os.remove(temp_file)
    
    col2.markdown("## Prediction Result")
    if prediction == "Healthy":
        col2.success(f"The chicken appears to be **{prediction}**! 🎉")
        col2.markdown("Keep up the good care for your feathered friend!")
    else:
        col2.error(f"The chicken may have **{prediction}**. 😢")
        col2.markdown("Please consult with a veterinarian for proper treatment.")
    
    col2.markdown("### What is Coccidiosis?")
    col2.info("""
    Coccidiosis is a parasitic disease of the intestinal tract of animals caused by coccidian protozoa. 
    The disease spreads from one animal to another by contact with infected feces or ingestion of infected tissue. 
    Diarrhea, which may become bloody in severe cases, is the primary symptom.
    """)

st.sidebar.title("About")
st.sidebar.info(
    "This app uses a deep learning model to predict whether a chicken is healthy "
    "or has coccidiosis based on an uploaded image. Always consult with a "
    "veterinarian for accurate diagnosis and treatment."
)

st.sidebar.title("Instructions")
st.sidebar.markdown(
    """
    1. Upload a clear image of a chicken.
    2. Wait for the model to analyze the image.
    3. View the prediction result and additional information.
    """
)

st.markdown(
    """
    <style>
    .reportview-container {
        background: linear-gradient(to right, #FDFCFB, #E2D1C3);
    }
    .sidebar .sidebar-content {
        background: linear-gradient(to bottom, #FDFCFB, #E2D1C3);
    }
    </style>
    """,
    unsafe_allow_html=True,
)