Update app.py

app.py

@@ -1,82 +1,83 @@
-import streamlit as st
-import os
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-import cv2
-import numpy as np
-
-# Load the model manually using Keras
-model = load_model('Model/keras_model.h5', compile=False)  # Load model without compilation
-
-# Labels directly added to the code
-labels = [
-    "Nothing",
-    "Zip-top cans",
-    "Newspaper",
-    "Old shoes",
-    "Watercolor pen",
-    "Disinfectant",
-    "Battery",
-    "Vegetable leaf",
-    "Apple"
-]
-
-# Classification dictionary (modified to skip image-based overlays)
-classDic = {0: None,
-            1: 0,
-            2: 0,
-            3: 3,
-            4: 3,
-            5: 1,
-            6: 1,
-            7: 2,
-            8: 2}
-
-# Streamlit page configuration
-st.title('Object Detection with Webcam')
-st.write("This application uses your webcam to detect objects in real-time.")
-
-# Start the webcam feed for Streamlit
-cap = cv2.VideoCapture(0)  # Manually set to camera index 0 (or change to 1, 2 if necessary)
-if not cap.isOpened():
-    st.error("Error: No camera found!")
-    exit()
-
-stframe = st.empty()  # Placeholder for the webcam feed
-
-# Add the 'Exit' button outside the loop so it's only displayed once
-exit_button = st.button('Exit', key="exit_button")
-
-# Loop to keep updating webcam feed
-while True:
-    ret, img = cap.read()
-    if not ret or img is None:
-        st.error("Error: Failed to capture image!")
-        continue
-
-    imgResize = cv2.resize(img, (454, 340))  # Resize captured frame to a specific size
-
-    # Perform prediction using the loaded model
-    img_input = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB
-    img_input = cv2.resize(img_input, (224, 224))  # Resize to expected input size for your model
-    img_input = img_input / 255.0  # Normalize the image
-    prediction = model.predict(tf.convert_to_tensor(img_input[None, ...]))  # Make prediction
-    classID = prediction.argmax()  # Get the predicted class ID
-    print(f"Predicted class ID: {classID}")
-
-    # Handle the class ID (can be used to trigger different actions based on class)
-    if 0 < classID <= len(labels):
-        st.write(f"Detected class: {labels[classID - 1]}")
-    else:
-        st.write(f"Invalid classID: {classID}, setting to default.")
-
-    # Display the resized image in Streamlit
-    img_resized = cv2.cvtColor(imgResize, cv2.COLOR_BGR2RGB)  # Convert image to RGB
-    stframe.image(img_resized, channels="RGB", caption="Real-time Detection", use_container_width=True)
-
-    # Exit condition based on button press
-    if exit_button:
-        break
-
-# Release resources and close the window
-cap.release()
+import streamlit as st
+import os
+!pip install tensorflow
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import cv2
+import numpy as np
+
+# Load the model manually using Keras
+model = load_model('Model/keras_model.h5', compile=False)  # Load model without compilation
+
+# Labels directly added to the code
+labels = [
+    "Nothing",
+    "Zip-top cans",
+    "Newspaper",
+    "Old shoes",
+    "Watercolor pen",
+    "Disinfectant",
+    "Battery",
+    "Vegetable leaf",
+    "Apple"
+]
+
+# Classification dictionary (modified to skip image-based overlays)
+classDic = {0: None,
+            1: 0,
+            2: 0,
+            3: 3,
+            4: 3,
+            5: 1,
+            6: 1,
+            7: 2,
+            8: 2}
+
+# Streamlit page configuration
+st.title('Object Detection with Webcam')
+st.write("This application uses your webcam to detect objects in real-time.")
+
+# Start the webcam feed for Streamlit
+cap = cv2.VideoCapture(0)  # Manually set to camera index 0 (or change to 1, 2 if necessary)
+if not cap.isOpened():
+    st.error("Error: No camera found!")
+    exit()
+
+stframe = st.empty()  # Placeholder for the webcam feed
+
+# Add the 'Exit' button outside the loop so it's only displayed once
+exit_button = st.button('Exit', key="exit_button")
+
+# Loop to keep updating webcam feed
+while True:
+    ret, img = cap.read()
+    if not ret or img is None:
+        st.error("Error: Failed to capture image!")
+        continue
+
+    imgResize = cv2.resize(img, (454, 340))  # Resize captured frame to a specific size
+
+    # Perform prediction using the loaded model
+    img_input = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB
+    img_input = cv2.resize(img_input, (224, 224))  # Resize to expected input size for your model
+    img_input = img_input / 255.0  # Normalize the image
+    prediction = model.predict(tf.convert_to_tensor(img_input[None, ...]))  # Make prediction
+    classID = prediction.argmax()  # Get the predicted class ID
+    print(f"Predicted class ID: {classID}")
+
+    # Handle the class ID (can be used to trigger different actions based on class)
+    if 0 < classID <= len(labels):
+        st.write(f"Detected class: {labels[classID - 1]}")
+    else:
+        st.write(f"Invalid classID: {classID}, setting to default.")
+
+    # Display the resized image in Streamlit
+    img_resized = cv2.cvtColor(imgResize, cv2.COLOR_BGR2RGB)  # Convert image to RGB
+    stframe.image(img_resized, channels="RGB", caption="Real-time Detection", use_container_width=True)
+
+    # Exit condition based on button press
+    if exit_button:
+        break
+
+# Release resources and close the window
+cap.release()