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| import streamlit as st | |
| import keras | |
| import cv2 | |
| import numpy as np | |
| import pickle | |
| from PIL import Image | |
| st.set_page_config(page_title="CV2 Image Detection", layout="centered") | |
| st.title("🖼️ A Minor Project on Image Detection using CNN and CV2 (A Simplified Clone of CNN Explainer)") | |
| # Cache the model and label encoder | |
| def load_model(): | |
| try: | |
| model_path = r"cv2_model.keras" | |
| label_encoder_path = r"label_encoder.pkl" | |
| model = keras.models.load_model(model_path) | |
| with open(label_encoder_path, 'rb') as file: | |
| label_encoder = pickle.load(file) | |
| return model, label_encoder | |
| except Exception as e: | |
| st.error(f"Error loading model: {e}") | |
| return None, None | |
| # File uploader for image input | |
| user_input_img = st.file_uploader("📤 Upload an Image", type=["jpg", "jpeg", "png"]) | |
| # Initialize session state for prediction | |
| if "prediction" not in st.session_state: | |
| st.session_state["prediction"] = None | |
| st.session_state["uploaded_img"] = None | |
| if user_input_img is not None: | |
| # Load and preprocess image | |
| img = Image.open(user_input_img) | |
| img_array = np.array(img) | |
| img_resized = cv2.resize(img_array, (64, 64)) | |
| img_resized = np.expand_dims(img_resized, axis=0) # Another way to batch dimension instead of np.newaxis | |
| # Store image in session state | |
| st.session_state["uploaded_img"] = img | |
| # Button for Prediction | |
| if st.button("🔍 Predict"): | |
| if st.session_state['uploaded_img'] is None: | |
| st.warning("⚠️ Please upload an image before predicting!") | |
| else: | |
| model, encoder = load_model() | |
| if model and encoder: | |
| # Make prediction | |
| predicted_label = encoder.inverse_transform(np.argmax(model.predict(img_resized), axis = 1)) | |
| # Store prediction in session state | |
| st.session_state["prediction"] = predicted_label | |
| # Display result in two columns if prediction exists | |
| if st.session_state["prediction"]: | |
| col1, col2 = st.columns([1, 1]) | |
| with col1: | |
| st.success(f"**Prediction:** {st.session_state['prediction']}") | |
| with col2: | |
| st.image(st.session_state["uploaded_img"], caption="Uploaded Image", use_container_width =True) |