Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import cv2
+from tensorflow.keras.models import load_model
+import pickle
+from PIL import Image
+import os
+
+# Set page configuration
+st.set_page_config(
+    page_title="Image Detection App",
+    page_icon="📸",
+    layout="centered",
+    initial_sidebar_state="expanded"
+)
+
+# Load the trained model and label encoder
+@st.cache_resource
+def load_resources():
+    model = load_model("captains_cv2_model.keras")
+    with open("label_encoder.pkl", "rb") as f:
+        le = pickle.load(f)
+    return model, le
+
+model, label_encoder = load_resources()
+
+# Function to preprocess the uploaded image
+def preprocess_image(uploaded_file):
+    # Save the uploaded file temporarily to disk
+    temp_path = "temp_image.jpg"
+    with open(temp_path, "wb") as f:
+        f.write(uploaded_file.read())
+    
+    # Read the image using cv2.imread
+    img = cv2.imread(temp_path)
+    # Resize to the model's expected input size (64, 64)
+    img = cv2.resize(img, (64, 64))  # Note: cv2.resize takes (width, height), not (height, width, channels)
+    # Add new axis for batch dimension
+    img = img[np.newaxis, :, :, :]
+    
+    # Clean up the temporary file
+    os.remove(temp_path)
+    return img
+
+# Sidebar
+st.sidebar.title("About")
+st.sidebar.info(
+    "This app uses a Convolutional Neural Network (CNN) to classify images into one of 10 categories. "
+    "Upload an image, and the model will predict its class!"
+)
+st.sidebar.markdown("### Classes")
+st.sidebar.write(
+    "The model can predict: lifeboat, ladybug, pizza, bell pepper, school bus, koala, espresso, red panda, orange, sports car."
+)
+
+# Main content
+st.title("📸 Image Classification App")
+st.markdown("Upload an image below, and let the model predict its class!")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    uploaded_file.seek(0)  # Reset file pointer after reading for display
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Preprocess the image
+    processed_image = preprocess_image(uploaded_file)
+
+    # Make prediction
+    with st.spinner("Predicting..."):
+        # Predict and decode as per your specified steps
+        prediction = model.predict(processed_image)
+        predicted_class_idx = np.argmax(prediction, axis=1)[0]
+        predicted_class = label_encoder.inverse_transform([predicted_class_idx])[0]
+
+    # Display the prediction
+    st.success("Prediction complete!")
+    st.markdown(f"### Predicted Class: **{predicted_class}**")
+    st.write(f"Prediction Confidence: {prediction[0][predicted_class_idx]:.4f}")
+
+    # Optional: Display confidence scores for all classes
+    if st.checkbox("Show confidence scores for all classes"):
+        class_names = label_encoder.classes_
+        confidence_scores = {class_names[i]: float(prediction[0][i]) for i in range(len(class_names))}
+        st.bar_chart(confidence_scores)
+
+else:
+    st.info("Please upload an image to get started.")
+
+# Footer
+st.markdown("---")
+st.markdown("Created with ❤️ using Streamlit | Hosted on [Hugging Face Spaces](https://huggingface.co/spaces)")