Upload 4 files

app (3).py

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+import numpy as np
+import PIL.Image as Image
+import tensorflow as tf
+import streamlit as st
+from streamlit_extras.add_vertical_space import add_vertical_space
+from warnings import filterwarnings
+filterwarnings('ignore')
+
+def streamlit_config():
+    st.set_page_config(page_title='Classification', layout='centered')
+    
+    base64_image = """
+    <style>
+    body, .stApp {
+        background-color: #006400 !important;
+    }
+    [data-testid="stHeader"] {
+        background: rgba(0,0,0,0) !important;
+    }
+    .bottom-right {
+        position: fixed;
+        bottom: 10px;
+        right: 10px;
+        background-color: rgba(0, 0, 0, 0.7);
+        padding: 10px;
+        border-radius: 10px;
+        color: white;
+        font-size: 16px;
+    }
+    </style>
+    """
+    st.markdown(base64_image, unsafe_allow_html=True)
+    
+    st.markdown(f'<h1 style="text-align: center; color: white;">Potato Disease Finder </h1>', unsafe_allow_html=True)
+    add_vertical_space(4)
+    
+    # Show "by Varun Tiwari" at the start page
+    st.markdown('<div class="bottom-right">by Varun Tiwari</div>', unsafe_allow_html=True)
+
+streamlit_config()
+
+def prediction(image_path, class_names=['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']):
+    img = Image.open(image_path)
+    img_resized = img.resize((256, 256))
+    img_array = tf.keras.preprocessing.image.img_to_array(img_resized)
+    img_array = np.expand_dims(img_array, axis=0)
+
+    model = tf.keras.models.load_model('model.h5')
+    prediction = model.predict(img_array)
+    predicted_class = class_names[np.argmax(prediction)]
+    confidence = round(np.max(prediction) * 100, 2)
+
+    st.markdown(
+        f'<div class="bottom-right">Predicted Class: {predicted_class}<br>Confidence: {confidence}%</div>', 
+        unsafe_allow_html=True
+    )
+    st.image(img.resize((400, 300)))
+
+col1, col2, col3 = st.columns([0.1, 0.9, 0.1])
+with col2:
+    input_image = st.file_uploader(label='Upload the Image', type=['jpg', 'jpeg', 'png'])
+
+if input_image is not None:
+    col1, col2, col3 = st.columns([0.2, 0.8, 0.2])
+    with col2:
+        prediction(input_image)
+

model (1).h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8e69552fb43cb1ac43301ca71b19f20e7f7ff4fe2ff13ef308feaf93b2fb45ff
+size 2286592

requirements (6).txt

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+numpy
+pillow
+tensorflow
+streamlit
+streamlit_extras

streamlit_config.toml.txt

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+[theme]
+base="light"
+primaryColor="#FF4B4B"
+backgroundColor="#DFFFD6"
+secondaryBackgroundColor="#B8E986"
+textColor="#0E1117"
+font="sans serif"