this is a commit

app.py

@@ -5,6 +5,10 @@
 import glob
 import re
 import numpy as np
+import PIL.Image
+import PIL.ImageOps as ImageOps
+import matplotlib.pyplot as plt
+from skimage.transform import resize
 
 # Keras
 from keras.applications.imagenet_utils import preprocess_input, decode_predictions
@@ -15,16 +19,21 @@
 from flask import Flask, redirect, url_for, request, render_template
 from werkzeug.utils import secure_filename
 from gevent.pywsgi import WSGIServer
+from keras.models import model_from_json
+json_file = open(r'C:\Users\rishi\deployemotion\depemotion\Deployment-Deep-Learning-Model\models\fer.json', 'r')
+loaded_model_json = json_file.read()
+model = model_from_json(loaded_model_json)
+model.load_weights(r'C:\Users\rishi\deployemotion\depemotion\Deployment-Deep-Learning-Model\models\fer.h5')
 
 # Define a flask app
 app = Flask(__name__)
 
 # Model saved with Keras model.save()
-MODEL_PATH = 'models/model_resnet.h5'
+
 
 # Load your trained model
-model = load_model(MODEL_PATH)
-model._make_predict_function()          # Necessary
+
+          # Necessary
 # print('Model loaded. Start serving...')
 
 # You can also use pretrained model from Keras
@@ -36,18 +45,27 @@
 
 
 def model_predict(img_path, model):
-    img = image.load_img(img_path, target_size=(224, 224))
-
-    # Preprocessing the image
-    x = image.img_to_array(img)
-    # x = np.true_divide(x, 255)
-    x = np.expand_dims(x, axis=0)
-
-    # Be careful how your trained model deals with the input
-    # otherwise, it won't make correct prediction!
-    x = preprocess_input(x, mode='caffe')
-
-    preds = model.predict(x)
+    img=plt.imread(img_path)
+    img2=resize(img,(48,48))
+    rgb_weights = [0.2989, 0.5870, 0.1140]
+
+    imgf = np.dot(img2[...,:3], rgb_weights)
+    imgf=imgf.reshape(1,48,48,1)
+
+    # img = image.load_img(img_path, target_size=(48,48))
+    # img=ImageOps.grayscale(img)
+
+    # # Preprocessing the image
+    # x = image.img_to_array(img)
+    # # x = np.true_divide(x, 255)
+    # x = np.expand_dims(x, axis=0)
+    # # print(x.shape())
+
+    # # Be careful how your trained model deals with the input
+    # # otherwise, it won't make correct prediction!
+    # x = preprocess_input(x, mode='caffe')
+
+    preds = model.predict(imgf)
     return preds
 
 
@@ -74,9 +92,18 @@ def upload():
 
         # Process your result for human
         # pred_class = preds.argmax(axis=-1)            # Simple argmax
-        pred_class = decode_predictions(preds, top=1)   # ImageNet Decode
-        result = str(pred_class[0][0][1])               # Convert to string
-        return result
+
+        emotions=['Angry', 'Disgust', 'Fear', 'Happy', 'Sad','Surprise','Neutral']
+        preds=list(preds)
+
+               # ImageNet Decode
+        maxelem=max(preds[0])
+        i=0
+        for x in preds[0]:
+            if maxelem==x:
+                break
+            i+=1   
+        return str(emotions[i])
     return None
 
 

models/fer.json

@@ -0,0 +1 @@
+{"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": [null, 48, 48, 1], "dtype": "float32", "sparse": false, "ragged": false, "name": "conv2d_input"}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "batch_input_shape": [null, 48, 48, 1], "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_4", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Conv2D", "config": {"name": "conv2d_5", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": [3, 3], "strides": [1, 1], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "dtype": "float32", "pool_size": [2, 2], "padding": "valid", "strides": [2, 2], "data_format": "channels_last"}}, {"class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1024, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_2", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 1024, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dropout", "config": {"name": "dropout_3", "trainable": true, "dtype": "float32", "rate": 0.2, "noise_shape": null, "seed": null}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 7, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "keras_version": "2.4.0", "backend": "tensorflow"}