Files Uploaded

app.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+import pandas as pd
+
+model = load_model('cnn_model.h5')
+
+def process_image(img):
+    img = img.resize((32, 32))  
+    img = np.array(img)
+    img = img / 255.0  
+    img = np.expand_dims(img, axis=0)  
+    return img
+
+st.markdown("""
+    <style>
+        .title {
+            font-size: 50px;
+            color: #A8D5FF;  # Pastel Blue color
+            font-weight: bold;
+            text-align: center;
+        }
+        .description {
+            font-size: 16px;
+            color: #555555;
+            text-align: center;
+        }
+        .uploaded-image {
+            display: block;
+            margin-left: auto;
+            margin-right: auto;
+            padding: 20px;
+        }
+        .prediction {
+            font-size: 20px;
+            font-weight: bold;
+            text-align: center;
+        }
+        .confidence {
+            font-size: 18px;
+            text-align: center;
+            color: #4CAF50;
+        }
+        .bar-chart {
+            text-align: center;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+st.markdown('<div class="title">CIFAR-10 Image Classification 🔎</div>', unsafe_allow_html=True)
+st.markdown('<div class="description">Upload an image to classify it into one of the CIFAR-10 categories.</div>', unsafe_allow_html=True)
+
+file = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
+
+if file is not None:
+    img = Image.open(file)
+    st.markdown('<div class="uploaded-image">', unsafe_allow_html=True)
+    st.image(img, caption='Uploaded Image', use_column_width=True, output_format='PNG')
+    st.markdown('</div>', unsafe_allow_html=True)
+
+    with st.spinner('Processing...'):
+        image = process_image(img)
+        predictions = model.predict(image)
+        predicted_class = np.argmax(predictions)
+        class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
+
+        confidence = np.max(predictions) * 100  # Confidence score
+
+        st.markdown(f'<div class="prediction">Prediction: {class_names[predicted_class]}</div>', unsafe_allow_html=True)
+        st.markdown(f'<div class="confidence">Confidence: {confidence:.2f}%</div>', unsafe_allow_html=True)
+
+        st.markdown('<div class="bar-chart">Class Probabilities:</div>', unsafe_allow_html=True)
+        prob_df = pd.DataFrame(predictions[0], index=class_names, columns=["Probability"])
+        st.bar_chart(prob_df)
+
+
+st.markdown('---')
+st.markdown("<div style='text-align: center;'>Powered by Streamlit and TensorFlow</div>", unsafe_allow_html=True)

cnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:75ae33cd1a6e220fdbe563473e986b4dd59f800916865506f0acca7ae4a40e43
+size 17545776

requirements.txt

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+streamlit
+tensorflow