Update app.py

app.py

@@ -1,15 +1,12 @@
-
 import gradio as gr
 from transformers import pipeline
 from transformers import AutoModelForSeq2SeqLM
 from transformers import AutoTokenizer
 from transformers import GenerationConfig
 import re
-
 model_name='google/flan-t5-base'
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
-
 Examples_to_teach_model="""
 Text: I hate apples
 Sentiment analysis:
@@ -24,43 +21,39 @@ Sentiment analysis:
 Sentiments: Negative
 PPrint Key words: tired, long process
 """
-
 def make_prompt(sentence):
     prompt = Examples_to_teach_model+ "Text: " + sentence + "Sentiment analysis:"
-
     return prompt
+def split_conj(text):
+    return re.sub('(but|yet|although|however|nevertheless|on the other hand|still|though)', "|", text).split('|')
 
 def get_sentiment_from_llm(review_text):
-    segments= re.sub('(but|yet|although|however|nevertheless|on the other hand|still|though|.)', "|", review_text.lower()).split('|')
+    sentences = review_text.lower().split(".")
     segments=[]
-    
-    ls_outputs=[]
-
-    for seg in segments:
+    for sen in sentences:
         if len(sen)>=3:
-            input = make_prompt(seg)
-            inputs = tokenizer(input, return_tensors='pt')
-            output = tokenizer.decode(
-            model.generate(
-                inputs["input_ids"],
-                max_new_tokens=100,
-            )[0],
-              skip_special_tokens=True)
-            ls_outputs.append("\n".join(output.split('PPrint ')))
+            segments=segments+split_conj(sen)
+    ls_outputs=[]
     
+    for seg in segments:
+        input = make_prompt(seg)
+        inputs = tokenizer(input, return_tensors='pt')
+        output = tokenizer.decode(
+        model.generate(
+            inputs["input_ids"],
+            max_new_tokens=100,
+        )[0],
+          skip_special_tokens=True)
+        ls_outputs.append("\n".join(output.split('PPrint ')))
     return "\n".join(ls_outputs)
-
 demo = gr.Blocks()
-
 sentiment_extr = gr.Interface(
     fn=get_sentiment_from_llm,
     inputs=gr.Textbox(label="Text input", type="text"),
     outputs=gr.Textbox(label="Sentiments", type="text"),
     title="Sentiments analysis",
-    description="Sentiment analysis and keywords extraction. Powered by prompt tuned flan-t5-base from Google.<br> As the model is run on small CPU, please allow 2-3 minutes for long inputs.",
+    description="Sentiment analysis and keywords extraction. Powered by prompt tuned flan-t5 from Google. <br> The model is run on small CPU. Please allow 2-3 minutes for longer inputs.",
 )
-
 with demo:
     gr.TabbedInterface([sentiment_extr], ["Sentiment text analysis"])
-
-demo.launch()
+demo.launch()