Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import torch
+from torchvision import transforms
+from transformers import pipeline
+
+# Load the face detection model
+face_detector = pipeline('face-detection', model='facebook/facemask-plugin-fasterrcnn')
+
+# Function to detect faces in an image
+def detect_faces(image):
+    # Convert image to PyTorch tensor
+    image_tensor = transforms.ToTensor()(image).unsqueeze(0)
+
+    # Detect faces
+    faces = face_detector(image_tensor)
+
+    return faces
+
+# Streamlit app
+def main():
+    st.title("Multiple Face Detection using Hugging Face")
+
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Read the image
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Detect faces
+        faces = detect_faces(image)
+
+        # Display the number of faces detected
+        num_faces = len(faces)
+        st.write(f"Number of faces detected: {num_faces}")
+
+        # Display bounding boxes around detected faces
+        for face in faces:
+            xmin, ymin, xmax, ymax = face['box']
+            image_with_box = image.copy()
+            draw = ImageDraw.Draw(image_with_box)
+            draw.rectangle([xmin, ymin, xmax, ymax], outline="red", width=3)
+            st.image(image_with_box, caption="Face Detection", use_column_width=True)
+
+if __name__ == "__main__":
+    main()