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aimljan24
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import streamlit as st
# from transformers import pipeline
from PIL import Image
import face_recognition
import cv2
import numpy as np
import requests
import os

st.title("AIMLJan24 - Face Recognition")

# create list of encoding of all images in photos folder
# Load images for face recognition
Images = []   # List to store Images
classnames = []  # List to store classnames
directory = "photos"
myList = os.listdir(directory)

for cls in myList:
    if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]:
        img_path = os.path.join(directory, cls)
        curImg = cv2.imread(img_path)
        Images.append(curImg)
        classnames.append(os.path.splitext(cls)[0])

# Load images for face recognition
encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images]

# camera to take photo of user in question
file_name = st.camera_input("Take a picture")  #st.file_uploader("Upload image  ")

if file_name is not None:
    col1, col2 = st.columns(2)

    test_image = Image.open(file_name)
    image = np.asarray(test_image)

    imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25)
    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
    facesCurFrame   = face_recognition.face_locations(imgS)
    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)

    name = "Unknown"  # Default name for unknown faces
    match_found = False  # Flag to track if a match is found

    # Checking if faces are detected
    if len(encodesCurFrame) > 0:
        for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
            # Assuming that encodeListknown is defined and populated in your code
            matches = face_recognition.compare_faces(encodeListknown, encodeFace)
            faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
            matchIndex = np.argmin(faceDis)

            if matches[matchIndex]:
                name = classnames[matchIndex].upper()
                match_found = True  # Set the flag to True
                
            y1, x2, y2, x1 = faceLoc
            y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.rectangle(image, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
            cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
            
            st.image(image, use_column_width=True, output_format="PNG")
    else:
            st.warning("No faces detected in the image. Face recognition failed.")

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

# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")

# st.title("AIMLJan24 First App on Hugging face - Hot Dog? Or Not?")

# file_name = st.file_uploader("Upload the test image to find is this hot dog ! ")

# 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)}%")


# # my first app
# import streamlit as st

# x = st.slider('Select a value')
# st.write(x, 'squared is', x * x)