Update app.py

app.py

@@ -1,17 +1,9 @@
-from flask import Flask,render_template
-from flask_socketio import SocketIO,emit
-import base64
-
 from keras.models import load_model
 from PIL import Image
 import numpy as np
 import cv2 
 
 
-app = Flask(__name__)
-app.config['SECRET_KEY'] = 'secret!'
-socket = SocketIO(app,async_mode="eventlet")
-
 
 #the following are to do with this interactive notebook code
 from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks
@@ -28,45 +20,54 @@ age_ranges = ['1-2', '3-9', '10-20', '21-27', '28-45', '46-65', '66-116']
 gender_ranges = ['male', 'female']
 emotion_ranges= ['positive','negative','neutral']
 
-def base64_to_image(base64_string):
-    # Extract the base64 encoded binary data from the input string
-    base64_data = base64_string.split(",")[1]
-    # Decode the base64 data to bytes
-    image_bytes = base64.b64decode(base64_data)
-    # Convert the bytes to numpy array
-    image_array = np.frombuffer(image_bytes, dtype=np.uint8)
-    # Decode the numpy array as an image using OpenCV
-    image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
-    return image
+import streamlit as st
+st.write("""
+         # Customer  Age , Gender  and  Emotion Prediction
+         """
+         )
+st.write("This is a simple  web app to predict age , gender and emotion of customer.")
+file = st.file_uploader("Please upload an image file", type=["jpg", "png","jpeg"])
+######
+if file is None:
+    st.text("Please upload an image file")
+else:
+    test_image = Image.open(file)
+    st.image(test_image, use_column_width=True)
+    st.write(type(test_image))
+    test_image = np.asarray(test_image)
+    gray = cv2.cvtColor(test_image,cv2.COLOR_BGR2GRAY)
+    face_cascade = cv2.CascadeClassifier('Copy of haarcascade_frontalface_default.xml')
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+    i = 0
+
+    for (x,y,w,h) in faces:
+            i = i+1
+            cv2.rectangle(test_image,(x,y),(x+w,y+h),(203,12,255),2)
+
+            img_gray=gray[y:y+h,x:x+w]
 
-@socket.on("connect")
-def test_connect():
-    print("Connected")
-    emit("my response", {"data": "Connected"})
+            emotion_img = cv2.resize(img_gray, (48, 48), interpolation = cv2.INTER_AREA)
+            emotion_image_array = np.array(emotion_img)
+            emotion_input = np.expand_dims(emotion_image_array, axis=0)
+            output_emotion= emotion_ranges[np.argmax(emotion_model.predict(emotion_input))]
+            
+            gender_img = cv2.resize(img_gray, (100, 100), interpolation = cv2.INTER_AREA)
+            gender_image_array = np.array(gender_img)
+            gender_input = np.expand_dims(gender_image_array, axis=0)
+            output_gender=gender_ranges[np.argmax(gender_model.predict(gender_input))]
 
-@socket.on("image")
-def receive_image(image):
-    # Decode the base64-encoded image data
-    image = base64_to_image(image)
-    image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
-    # emit("processed_image", image)
-    # Make the image a numpy array and reshape it to the models input shape.
-    image = np.asarray(image, dtype=np.float32).reshape(1, 224, 224, 3)
-    image = (image / 127.5) - 1
-    # Predicts the model
-    # prediction1 = age_model.predict(image)
-    prediction2 = gender_model.predict(image)
-    # prediction3 = emotion_model.predict(image)
+            age_image=cv2.resize(img_gray, (200, 200), interpolation = cv2.INTER_AREA)
+            age_input = age_image.reshape(-1, 200, 200, 1)
+            output_age = age_ranges[np.argmax(age_model.predict(age_input))]
 
-    index = np.argmax(prediction2)
-    gender_ranges = gender_ranges[index]
-    age = prediction1[0][index]
-    emit("result",{"gender":str(gender_ranges)})
 
-@app.route("/")
-def home():
-    return render_template("index.html")
+            output_str = str(i) + ": "+  output_gender + ', '+ output_age + ', '+ output_emotion
+            st.write(output_str)
+            
+            col = (0,255,0)
 
-if __name__ == '__main__':
-    # app.run(debug=True)
-    socket.run(app,host="0.0.0.0", port=7860)
+            cv2.putText(test_image, str(i),(x,y),cv2.FONT_HERSHEY_SIMPLEX,1,col,2)
+    st.image(cv2.cvtColor(test_image, cv2.COLOR_BGR2RGB))
+  
+     #st.image(test_image, use_column_width=True)