Update app.py

app.py

@@ -1,68 +1,68 @@
-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")
-
-# Cache the model and label encoder
-@st.cache_resource
-def load_model():
-    try:
-        model_path = r"C:\Users\hi\Desktop\INNOMATICS\CNN\cv2_model.keras"
-        label_encoder_path = r"C:\Users\hi\Desktop\INNOMATICS\CNN\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:
+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")
+
+# Cache the model and label encoder
+@st.cache_resource
+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)