Update app.py

app.py

@@ -263,18 +263,18 @@ print(summarized_text)
 # the aim of this section of code is to get a summary of just one sentence by summarizing the summary all while the summary is longer than one sentence.
 # unfortunately, I tried many many models and none of them actually summarize the text to as short as one sentence.
 #I had searched for ways to fine tune the summarization model to specify that the summarization should be done in just one sentence but did not find a way to implement it
-from transformers import pipeline
-summarized_text_list_list=summarized_text_list['summary_text']
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+#from transformers import pipeline
+#summarized_text_list_list=summarized_text_list['summary_text']
+#summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 #print(summarizer)
-number_of_sentences=summarized_text_list_list.count('.')
-print(number_of_sentences)
-while(number_of_sentences)>1:
-    print(number_of_sentences)
-    summarized_text_list_list=summarizer(summarized_text_list_list)[0]['summary_text']
-    number_of_sentences-=1
-    print(summarized_text_list_list)
-    print(number_of_sentences)
+#number_of_sentences=summarized_text_list_list.count('.')
+#print(number_of_sentences)
+#while(number_of_sentences)>1:
+ #   print(number_of_sentences)
+  #  summarized_text_list_list=summarizer(summarized_text_list_list)[0]['summary_text']
+   # number_of_sentences-=1
+    #print(summarized_text_list_list)
+    #print(number_of_sentences)
 
 
 #text to speech
@@ -288,10 +288,11 @@ from transformers import pipeline
 from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
 processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
 model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
-#text = "The future belongs to those who believe in the beauty of their dreams."
+text = "The future belongs to those who believe in the beauty of their dreams."
 #text = (summarized_text_list_list)
 
-inputs = processor(text=summarized_text_list_list, return_tensors="pt")
+#inputs = processor(text=summarized_text_list_list, return_tensors="pt")
+inputs = processor(text, return_tensors="pt")
 from datasets import load_dataset
 embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")