Upload app.py

app.py

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+import tensorflow as tf
+
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import numpy as np
+
+# Default files
+path_to_file = 'amy-winehouse.txt'
+path_to_model = './amy_winehouse.h5'
+
+# Open model and dataset 
+model = tf.keras.models.load_model(path_to_model) 
+ 
+#Get name of file 
+name = path_to_file.split('.')[0]
+#print(name)
+data= open(path_to_file).read()
+corpus = data.lower().split('\n')
+
+#Tokenize data
+tokenizer = Tokenizer()
+tokenizer.fit_on_texts(corpus)
+total_words = len(tokenizer.word_index) + 1
+word_index = tokenizer.word_index 
+index_word = {index:word for word, index in tokenizer.word_index.items()}
+
+n_gram_sequences = []
+for line in corpus:
+  token_list = tokenizer.texts_to_sequences([line])[0]
+  for i in range(1, len(token_list)):
+    n_gram_sequences.append(token_list[:i+1])
+#print(np.shape(n_gram_sequences))
+
+#for seq in n_gram_sequences:
+#  print ([index_word[w] for w in seq])
+
+# pad sequences
+max_len = max([len(seq) for seq in n_gram_sequences])
+
+#print(max_sequence_len, total_words)
+n_gram_sequences = np.array(pad_sequences(n_gram_sequences, padding='pre', maxlen=max_len))
+
+
+# Generate next words with an initial prompt
+def predict_n_words(prompt, n_words):
+
+  for _ in range(n_words):
+    token_list = tokenizer.texts_to_sequences([prompt])[0]
+    token_list = pad_sequences([token_list], padding='pre', maxlen=max_len-1,)
+    predicted = np.argmax( model.predict(token_list), axis = 1)
+    prompt += " " + index_word[predicted[0]]
+  return prompt
+
+
+import gradio as gr
+
+demo = gr.Interface(
+    fn=predict_n_words,
+    inputs=[gr.Textbox(lines=2, placeholder="Prompt text here..."), gr.Slider(0, 500)], 
+    outputs="text",
+    examples=[
+        ["the girl from ipanema", 200],
+        ["A walk through Cagliari", 200],
+    ],
+    title="Amy Winehouse RNN",
+    description="Simple word-based RNN text generator trained on Amy Winehouse's songs",
+)
+
+demo.launch()