face detection model added

app.py

@@ -1,22 +1,28 @@
 import streamlit as st
-from transformers import pipeline
 from PIL import Image
+import face_recognition
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+st.title("Face Detection")
 
-st.title("Hot Dog? Or Not?")
+# Load the jpg file into a numpy array
+file_name = st.file_uploader("Upload a candidate image")
+image = face_recognition.load_image_file(file_name)
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+# Find all the faces in the image using the default HOG-based model.
+# This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated.
+# See also: find_faces_in_picture_cnn.py
+face_locations = face_recognition.face_locations(image)
 
-if file_name is not None:
-    col1, col2 = st.columns(2)
+st.write("I found {} face(s) in this photograph.".format(len(face_locations)))
 
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
+for face_location in face_locations:
 
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
 
+    # Print the location of each face in this image
+    top, right, bottom, left = face_location
+    st.write("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))
 
+    # You can access the actual face itself like this:
+    face_image = image[top:bottom, left:right]
+    pil_image = Image.fromarray(face_image)
+    st.write(pil_image)