test

LSTM/cultural_nepali_50.h5

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+size 43815912

LSTM/lstm_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea3983c78d36d3aed41962954ac55d2ff4c508f1fd2b1ed8c03af3144d241558
+size 42890524

app.py

@@ -1,7 +1,183 @@
+# from flask import Flask, request, jsonify
+import cv2
+import numpy as np
+from keras.applications import ResNet152
+from keras.optimizers import Adam
+from keras.models import Sequential, Model,load_model
+from keras.layers import Input
+from keras.layers import Dense
+from keras.layers import LSTM
+from keras.layers import Embedding
+from keras.layers import Dropout
+from keras.layers import add
+from keras.utils import to_categorical
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+from keras.preprocessing import image, sequence
+import cv2
+from keras_preprocessing.sequence import pad_sequences
+from tqdm import tqdm
+import pickle
+import tensorflow as tf
+# from keras.applications.Resnet50 import preprocess_input
+from flask_cors import CORS
+
+from keras.applications import ResNet50
+# 
+# # Transformer 
+# from library.prediction import evaluate_single_image
+# from  library.transformer import Transformer
+# from library.customSchedule import learning_rate
+
+# top_k = 25000
+# num_layer = 4
+# d_model = 512
+# dff = 2048
+# num_heads = 8
+# row_size = 8
+# col_size = 8
+# target_vocab_size = top_k + 1
+# dropout_rate = 0.1
+
+
+# loaded_transformer = Transformer(num_layer, d_model, num_heads, dff, row_size, col_size,
+#                                  target_vocab_size, max_pos_encoding=target_vocab_size,
+#                                  rate=dropout_rate)
+
+# # Load the weights into the model
+# loaded_transformer.load_weights('models/Transformer/model')
+# # Use the loaded custom objects
+# loaded_transformer.compile(optimizer=tf.keras.optimizers.Adam(learning_rate))
+# print("Trasformer model loaded successfully")
+# # loaded_transformer.compile(optimizer=tf.keras.optimizers.Adam(learning_rate), loss=train_loss.result(), metrics=[train_accuracy])
+# global tokenizer
+# with open('pickle_files/transformer/tokenizer.pickle', 'rb') as handle:
+#     tokenizer = pickle.load(handle)
+#     tokenizer.word_index['<pad>'] = 0
+#     tokenizer.index_word[0] = '<pad>'
+
+
+# print("Tokenizer  loaded successfully")
+
+# 
+
+
+incept_model = ResNet152(weights='imagenet', include_top=False,input_shape=(224, 224, 3))
+last = incept_model.layers[-2].output
+ResNet152Model= Model(inputs = incept_model.input,outputs = last)
+
+with open("pickle_files/lstm/words_dict.pkl","rb") as f:
+    words_dict=pickle.load(f)
+
+
+vocab_size = len(words_dict)+1
+MAX_LEN = 192
+inv_dict = {v:k for k, v in words_dict.items()}
+
+
+model = tf.keras.models.load_model('LSTM/lstm_model.h5')
+
+# inputs1 = Input(shape=(2048,))
+# fe1 = Dropout(0.5)(inputs1)
+# fe2 = Dense(256, activation='relu')(fe1)
+
+# # language sequence model
+# inputs2 = Input(shape=(MAX_LEN,))
+# se1 = Embedding(vocab_size, MAX_LEN, mask_zero=True)(inputs2)
+# se2 = Dropout(0.4)(se1)
+# se3 = LSTM(256)(se2)
+
+# # decoder model
+# decoder1 = add([fe2, se3])
+# decoder2 = Dense(256, activation='relu')(decoder1)
+# outputs = Dense(vocab_size, activation='softmax')(decoder2)
+
+# # tie it together [image, seq] [word]
+# model = Model(inputs=[inputs1, inputs2], outputs=outputs)
+# # compile model
+# model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy'])
+# model.load_model("models/LSTM/cultural_nepali_50.h5")
+# print("LSTM model  loaded successfully")
+
+
+# app = Flask(__name__)
+# app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 1
+# cors = CORS(app, resources={r"/*": {"origins": "*"}})
+# @app.route('/')
+# def index(): 
+#     return render_template('index.html')
+
+
+# @app.route('/tranformer',methods=['POST'])
+# def tranformer():
+#     if 'file' not in request.files:
+#         return 'No file part'
+
+#     file = request.files['file']
+
+#     if file.filename == '':
+#         return 'No selected file'
+
+#     # Save the file
+   
+#     file.save('static/file.jpg')
+#     caption=evaluate_single_image("static/file.jpg",tokenizer,loaded_transformer)
+#     print(caption)
+#     return jsonify({'caption': caption})
+
+
+# @app.route('/lstm', methods=['POST'])
+def after(image):
+
+    # if 'file' not in request.files:
+    #     return 'No file part'
+
+    # file = request.files['file']
+
+    # if file.filename == '':
+    #     return 'No selected file'
+
+    # # Save the file
+   
+    # file.save('static/file.jpg')
+
+    # Read the saved file
+    img = cv2.imread(image)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = cv2.resize(img, (224,224))
+    img = img.reshape(1,224,224,3)
+    test_img_resized=ResNet152Model.predict(img).reshape(2048,) 
+
+
+    text_inp = ['startofseq']
+    count = 0
+    caption = ''
+    while count < MAX_LEN:
+        count += 1
+        encoded = []
+        encoded = [words_dict.get(word, len(words_dict) - 1) for word in text_inp]  # Convert words to indices, using index for '<end>' for unknown words
+        encoded = pad_sequences([encoded], padding='post', truncating='post', maxlen=MAX_LEN)[0]  # Pad sequences
+
+        data_list = [test_img_resized.reshape(1, -1), encoded.reshape(1, -1)]  # Reshape encoded
+        prediction = np.argmax(model.predict(data_list))
+        prediction = np.argmax(model.predict(data_list))
+        sampled_word = inv_dict[prediction]
+        caption = caption + ' ' + sampled_word
+
+        if sampled_word == 'endofseq':
+            break
+        text_inp.append(sampled_word)
+
+    caption= caption.replace('endofseq','')
+    print(caption.replace(' .','.'))
+
+    # return jsonify({'caption': caption.replace(' .','.')})
+    return caption.replace(' .','.')
+
+
+
+iface = gr.Interface(fn=after, inputs="image", outputs="text")
+iface.launch()
+# if __name__ == "__main__":
+#     app.run(debug=True) 

pickle_files/lstm/words_dict.pkl

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+size 140742

pickle_files/transformer/tokenizer.pickle

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