app.py

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")

# Set the title and text color to dark green
st.title('R3SELL', color='darkgreen')

# 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)

    image = Image.open(file_name)
    col1.image(image, use_column_width=True)
    predictions = pipeline(image)

    col2.header("Probabilities")
    for p in predictions:
        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")