summarize

aiSum.py

@@ -0,0 +1,37 @@
+from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration
+
+# Let's load the model and the tokenizer
+model_name = "human-centered-summarization/financial-summarization-pegasus"
+tokenizer = PegasusTokenizer.from_pretrained(model_name)
+model = PegasusForConditionalGeneration.from_pretrained(model_name) # If you want to use the Tensorflow model
+                                                                    # just replace with TFPegasusForConditionalGeneration
+
+
+# Some text to summarize here
+text_to_summarize = "Customer service was terrible. Called the number for accounts and forced to listen to advertisements from their partners with no escape. When it was finally over it just went to a loop with a number to call for more promotional offers. Called a different number and got transferred from a human back to their answering service-- which hung up on me."
+
+class Sum():
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def summarize(text_to_summarize):
+        # Tokenize our text
+        # If you want to run the code in Tensorflow, please remember to return the particular tensors as simply as using return_tensors = 'tf'
+        input_ids = tokenizer(text_to_summarize, return_tensors="pt").input_ids
+
+        # Generate the output (Here, we use beam search but you can also use any other strategy you like)
+        output = model.generate(
+            input_ids,
+            max_length=32,
+            num_beams=5,
+            early_stopping=True
+        )
+
+        # Finally, we can print the generated summary
+        #print(tokenizer.decode(output[0], skip_special_tokens=True))
+        return tokenizer.decode(output[0], skip_special_tokens=True)
+        # Generated Output: Saudi bank to pay a 3.5% premium to Samba share price. Gulf region’s third-largest lender will have total assets of $220 billion
+
+if __name__ == "__main__":
+    print(Sum().summarize(text_to_summarize))

app.py

@@ -11,7 +11,12 @@ https://hits.seeyoufarm.com/
 PORTFOLIO OPTIMIZATION
 '''
 from aiOpt import Asset
+from aiSum import Sum
 import numpy as np
+
+def summarize(text):
+  return Sum().summarize(text)
+
 def optimize(cost, prob, its):
   s = Asset(np.asfarray(cost.split()),
             np.asfarray(prob.split()))
@@ -220,6 +225,16 @@ With no need for jargon, SSDS delivers tangible value to our fintech operations.
     tuning the distance for use case
       """)
 
+  with gr.Tab("Generative AI Summarization"):
+    in_sum = gr.Textbox(placeholder="Customer service was terrible. Called the number for accounts and forced to listen to advertisements from their partners with no escape. When it was finally over it just went to a loop with a number to call for more promotional offers. Called a different number and got transferred from a human back to their answering service-- which hung up on me.",
+                            label="Long Text",
+                            info="Summarization"
+                            )
+    out_sum = gr.JSON(label="Summarized Verbatim")
+
+    btn_sum = gr.Button("Find Similar Verbatim")
+    btn_sum.click(fn=summarize, inputs=in_sum, outputs=out_sum)
+
   with gr.Tab("Explainable AI"):
     df=pd.read_csv("./xgb/re.csv")
 

requirements.txt

@@ -5,3 +5,4 @@ pandas==2.1.3
 #yfinance==0.2.31
 #scikit-learn
 plotly
+transformers