app.py

import cv2
import numpy as np
import torch
import streamlit as st
from transformers import AutoModelForImageClassification, AutoFeatureExtractor

# Load AI Model from Hugging Face
model_name = "microsoft/resnet-50"
model = AutoModelForImageClassification.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)

def process_frame(frame):
    # Convert frame to tensor
    inputs = feature_extractor(images=[frame], return_tensors="pt")
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    return predictions

def main():
    st.title("Smart Mirror AI")
    run = st.checkbox("Run Camera")
    cap = cv2.VideoCapture(0)
    frame_placeholder = st.empty()
    
    while run:
        success, frame = cap.read()
        if not success:
            st.write("Failed to capture video")
            break
        else:
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            predictions = process_frame(frame)
            frame_placeholder.image(frame, channels="RGB")
    cap.release()
    
if __name__ == "__main__":
    main()