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import streamlit as st |
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import io |
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from PIL import Image |
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import os |
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from cnnClassifier.pipeline.predict import Prediction |
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st.set_page_config(page_title="Chicken Health Predictor", page_icon="🐔", layout="wide") |
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st.title("🐔 Chicken Health Predictor") |
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st.markdown("### Upload an image to predict if the chicken is healthy or has coccidiosis") |
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uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) |
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col1, col2 = st.columns(2) |
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if uploaded_file is not None: |
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image = Image.open(uploaded_file) |
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col1.image(image, caption="Uploaded Image", use_column_width=True) |
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temp_file = "temp_image.jpg" |
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image.save(temp_file) |
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with st.spinner("Analyzing the image..."): |
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predictor = Prediction(temp_file) |
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prediction = predictor.predict() |
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os.remove(temp_file) |
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col2.markdown("## Prediction Result") |
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if prediction == "Normal": |
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col2.success(f"The chicken appears to be **{prediction}**! 🎉") |
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col2.markdown("Keep up the good care for your feathered friend!") |
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else: |
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col2.error(f"The kidney may have **{prediction}**. 😢") |
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col2.markdown("Please consult with a veterinarian for proper treatment.") |
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st.sidebar.title("About") |
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st.sidebar.info( |
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"This app uses a deep learning model to predict whether a chicken is healthy " |
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"or has coccidiosis based on an uploaded image. Always consult with a " |
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"veterinarian for accurate diagnosis and treatment." |
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) |
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st.sidebar.title("Instructions") |
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st.sidebar.markdown( |
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""" |
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1. Upload a clear image of a chicken. |
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2. Wait for the model to analyze the image. |
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3. View the prediction result and additional information. |
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""" |
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) |
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st.markdown( |
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""" |
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<style> |
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.reportview-container { |
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background: linear-gradient(to right, #FDFCFB, #E2D1C3); |
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} |
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.sidebar .sidebar-content { |
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background: linear-gradient(to bottom, #FDFCFB, #E2D1C3); |
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} |
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</style> |
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""", |
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unsafe_allow_html=True, |
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) |
