app fixes

.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,53 @@
+import tensorflow.keras.backend as K
+from tensorflow.keras.layers import LSTM  
+from pickle import load
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+
+model_V2 = 'ByteLevelLM.h5'
+
+K.clear_session()
+tf.keras.backend.clear_session()
+np.random.seed(42)
+tf.random.set_seed(42)
+HeNormal = tf.keras.initializers.he_normal()
+daily_V2 = tf.keras.models.load_model(model_V2, 
+                                      custom_objects={'HeNormal': HeNormal},compile=False)
+#Tokenizer
+def tokenize():
+    import json
+    with open('Tokenizer.json', encoding='utf-8') as f:
+        data = json.load(f)
+        tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(data)
+    with open('index2char.json', encoding='utf-8') as f:
+        index2char = json.load(f)
+    char2index = dict((int(v),int(k)) for k,v in index2char.items())
+    tokenizer.word_index = char2index
+    return tokenizer
+    
+def model2_preds(news_headline_input):  
+    headline = news_headline_input
+    headline = '<s>' + headline + '<\s'
+
+    tokenizer = tokenize()
+    
+    sample_2 = headline.encode('utf-8') 
+    sample_2 = tokenizer.texts_to_sequences([sample_2])
+
+    predict_v2 = daily_V2.predict(sample_2, verbose = 0)[0,0]
+    # app_type = ui_display(title = "Model 2 Predictions (256 Bits Embeddings)")
+    return "Probability of Buy Signal from News Headline/s: %f" % predict_v2   
+
+# Create an instance of the Gradio Interface application function with the appropriate parameters. 
+app = gr.Interface(fn=model2_preds, 
+                   title="Event Driven Trading (Byte Level Language Modelling)",
+                   description='News headlines from OverNight concatenated for next day Buy/Sell Probability/Signal',
+                   inputs = gr.Textbox(label="News Headline/s", info='Separate several news headlines by a space'),
+                   outputs=gr.Textbox(show_label = True,label="Prediction", info='This is the probability to buy at market close today and sell market close tomorrow'),
+                   submit_btn = 'Predict')
+
+# Launch the app
+if __name__ == '__main__':
+    app.launch(share=True)
+

app.py

@@ -1,62 +1,53 @@
-from tensorflow.keras.models import load_model
+import tensorflow.keras.backend as K
+from tensorflow.keras.layers import LSTM  
 from pickle import load
 import numpy as np
 import tensorflow as tf
 import gradio as gr
 
+model_V2 = 'ByteLevelLM.h5'
 
-# Set the model to the saved trained 300 epoch model. 
-model = load_model('four_chapters_moby_dick_model_300_FIRAS.keras')
-# Set the tokenizer to the trained tokenizer from the model. 
-tokenizer = load(open('four_chapters_moby_dick_tokenizer_300_FIRAS', 'rb'))
+K.clear_session()
+tf.keras.backend.clear_session()
+np.random.seed(42)
+tf.random.set_seed(42)
+HeNormal = tf.keras.initializers.he_normal()
+daily_V2 = tf.keras.models.load_model(model_V2, 
+                                      custom_objects={'HeNormal': HeNormal},compile=False)
+#Tokenizer
+def tokenize():
+    import json
+    with open('Tokenizer.json', encoding='utf-8') as f:
+        data = json.load(f)
+        tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(data)
+    with open('index2char.json', encoding='utf-8') as f:
+        index2char = json.load(f)
+    char2index = dict((int(v),int(k)) for k,v in index2char.items())
+    tokenizer.word_index = char2index
+    return tokenizer
+    
+def model2_preds(news_headline_input):  
+    headline = news_headline_input
+    headline = '<s>' + headline + '<\s'
 
-def preprocess(texts):
-    X = np.array(tokenizer.texts_to_sequences([texts])) -1
-    return X
-  
-def next_word(text, num_gen_words=0, 
-              randome_sampling = False,
-              temperature=1):  
-    '''
-    Author : Firas Obeid
-    Randome_Sampling : Using a categorical distribution to predict the character returned by the model
-    Low temperatures results in more predictable text.
-    Higher temperatures results in more surprising text.
-    Experiment to find the best setting.
-    '''
-    input_text = text  
-    output_text = []  
-      
-    for i in range(num_gen_words):  
-        X_new = preprocess(input_text)  
-        
-        if randome_sampling:
-            y_proba = model.predict(X_new, verbose = 0)[0, -1:, :]#first sentence, last token
-            rescaled_logits = tf.math.log(y_proba) / temperature
-            pred_word_ind = tf.random.categorical(rescaled_logits, num_samples=1) + 1
-            pred_word = tokenizer.sequences_to_texts(pred_word_ind.numpy())[0]
-        else:
-            y_proba = model.predict(X_new, verbose=0)[0]  #first sentence
-            pred_word_ind = np.argmax(y_proba, axis = -1) +1 
-            pred_word = tokenizer.index_word[pred_word_ind[-1]]   
-             
-        
-        input_text += ' ' + pred_word  
-        output_text.append(pred_word)  
-      
-    return ' '.join(output_text)  
-  
-def generate_text(text, num_gen_words=25, temperature=1, randome_sampling=False):  
-    return next_word(text, num_gen_words, randome_sampling, temperature)  
+    tokenizer = tokenize()
+    
+    sample_2 = headline.encode('utf-8') 
+    sample_2 = tokenizer.texts_to_sequences([sample_2])
+
+    predict_v2 = daily_V2.predict(sample_2, verbose = 0)[0,0]
+    # app_type = ui_display(title = "Model 2 Predictions (256 Bits Embeddings)")
+    return "Probability of Buy Signal from News Headline/s: %f" % predict_v2   
 
 # Create an instance of the Gradio Interface application function with the appropriate parameters. 
-max_output = gr.Number(value=150)
-app = gr.Interface(fn=generate_text, 
-                   inputs=["text", 
-                           gr.Slider(1, 50, value=1, step=1, label="Minimum number of Shakespearean words to generate", info="Choose between 1 and 50"),
-                           gr.Slider(0.1, 5, value=0.1, step=0.1, label="Temprature", info="Choose between 0.1 and 5"),
-                          "checkbox"],
-                   outputs="text")
+app = gr.Interface(fn=model2_preds, 
+                   title="Event Driven Trading (Byte Level Language Modelling)",
+                   description='News headlines from OverNight concatenated for next day Buy/Sell Probability/Signal',
+                   inputs = gr.Textbox(label="News Headline/s", info='Separate several news headlines by a space'),
+                   outputs=gr.Textbox(show_label = True,label="Prediction", info='This is the probability to buy at market close today and sell market close tomorrow'),
+                   submit_btn = 'Predict')
+
 # Launch the app
 if __name__ == '__main__':
-    app.launch(share=True)
+    app.launch(share=True)
+