Frontend

app.py

@@ -1,12 +1,29 @@
 import streamlit as st
 from transformers import pipeline
 from PIL import Image
+import cv2
 
 pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-st.title("Hot Dog? Or Not?")
+# Set the title and text color to dark green
+st.title('R3SELL', color='darkgreen')
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+# 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:
+        cv2.imwrite('webcam_image.jpg', frame)
+        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)
@@ -17,4 +34,5 @@ if file_name is not None:
 
     col2.header("Probabilities")
     for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+