Create app.py

app.py

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+# 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:
+            predictions = process_frame(frame)
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame_placeholder.image(frame, channels="RGB")
+    cap.release()
+    
+if __name__ == "__main__":
+    main()
+
+# requirements.txt
+opencv-python
+numpy
+torch
+transformers
+streamlit