Update app.py

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 from transformers import pipeline
 from PIL import Image
 import base64
-import cv2
 
 pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
@@ -12,30 +11,11 @@ st.markdown('<h1 style="color:darkgreen;">R3SELL</h1>', unsafe_allow_html=True)
 # Create a file input option for uploading an image
 file_name = st.file_uploader("Upload an image file (JPEG, PNG, etc.)")
 
-# Create an option to access the camera/webcam
-if st.button("Take an image from camera"):
-    cap = cv2.VideoCapture(0)
-    ret, frame = cap.read()
-    if ret:
-        # Encode the webcam image as a Base64 string
-        img_encoded = base64.b64encode(cv2.imencode('.jpg', frame)[1]).decode('utf-8')
+# Create a camera input widget to capture images from the webcam
+image = st.camera_input("Capture an image from your webcam")
 
-        # Pass the Base64 encoded image to the pipeline function
-        predictions = pipeline(Image.open('data:image/jpeg;base64,' + img_encoded))
-
-        # Replace file_name with the encoded image
-        file_name = 'webcam_image.jpg'
-
-# Add a text bar to add a title
-image_title = st.text_input("Image Title", value="Specificity is nice!")
-
-# Add a text bar to add a description
-image_description = st.text_input("Image Description", value="(Optional)")
-
-if file_name is not None:
-    col1, col2 = st.columns(2)
-
-    # Check if the file is a webcam image
+if file_name is not None or image is not None:
+    # Check if the image is a webcam image
     if file_name == 'webcam_image.jpg':
         # Use the Base64 encoded image
         image = Image.open('data:image/jpeg;base64,' + img_encoded)
@@ -43,9 +23,19 @@ if file_name is not None:
         # Open the uploaded image
         image = Image.open(file_name)
 
-    col1.image(image, use_column_width=True)
+    # Pass the captured image to the pipeline function
     predictions = pipeline(image)
 
+    col1, col2 = st.columns(2)
+
+    # Add a text bar to add a title
+    image_title = st.text_input("Image Title", value="Specificity is nice!")
+
+    # Add a text bar to add a description
+    image_description = st.text_input("Image Description", value="(Optional)")
+
+    col1.image(image, use_column_width=True)
+
     col2.header("Probabilities")
     for p in predictions:
         col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")