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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

Model/keras_metadata.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f207a1e2c0f42e257b372b2830feaddb4e8ba4c47ddb86237439c1ed266f46b2
+size 16505

Model/saved_model.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7d3645eb160aafdb2e193c1fc02939ab79f0793de83097898bb545ae8d2a6a4
+size 146234

Model/variables/variables.data-00000-of-00001

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2393a841d5bd4d584110ef69a6b0a7d36289e90100b714f2669d6ee98783502d
+size 1278565

cv_func.py

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+import cv2
+import numpy as np
+import tensorflow as tf
+
+
+
+def predict(image):
+    model = tf.keras.models.load_model("Model")
+    img = cv2.imread(image)
+    
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    haar_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+    faces_rect = haar_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=9)
+    men_count = 0
+    women_count = 0
+    for (x, y, w, h) in faces_rect:
+        cropped_img = img[y:y+h, x:x+w]
+
+        resized_img = cv2.resize(cropped_img,(64,64))
+        image_array = np.expand_dims(resized_img,axis=0)
+
+        prediction = model.predict(image_array)
+
+
+        if prediction[0][0] > 0.5:
+            ans = "Men"
+            men_count+=1
+        else:
+            ans = "Women"
+            women_count+=1
+    
+
+        cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), thickness=2)
+        cv2.putText(img, ans, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+        rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+    return rgb_img,men_count,women_count
+
+ 

deploy_app.py

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+import cv2
+import time
+import tempfile
+import numpy as tf
+import streamlit as st
+import tensorflow as tf
+from cv_func import predict
+
+st.title("Gender Detectior using OpenCv and Tensorflow")
+
+uploader = st.file_uploader(label="Upload the png here: ",type=["jpg","png"])
+
+if uploader is None:    
+     st.warning("⚠️| Hey there! Ready to unveil the mysteries? Upload an image and let's predict some genders!")
+
+
+if uploader is not None:
+    print("log update: file uploaded!")
+    st.write("⏳ | Brace yourselves! Our top-notch AI detectives are on the case....")
+    time.sleep(3)
+    st.write("⏳ | Analyzing the pixels to uncover the hidden secrets of gender in your image.....")
+    time.sleep(3)
+    st.write("⌛ | This could be the moment we crack the code or just end up with some hilariously unexpected results")
+    time.sleep(3)
+    st.write("✅ | Done!")
+    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+            temp_file.write(uploader.read())
+            image_filename = temp_file.name
+    
+    ans,men,women = predict(image_filename)    
+    
+    
+    st.header("Output:")
+    col1, col2 = st.columns(2,)
+
+    
+    with col1:
+        st.header("Men:")
+        st.metric("Count", men)
+    with col2:
+        st.header("Women:")
+        st.metric("Count", women)
+    
+    st.image(ans)
+
+
+
+
+
+

requirements.txt

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+python-opencv
+tensorflow
+numpy
+streamlit