ddad

app.py

@@ -9,9 +9,13 @@ import torch
 #import transformers 
 from transformers import pipeline
 from PIL import Image
+import datasets 
 
 
 
+metric = datasets.load_metric("rouge")
+
+
 image = Image.open('hb.jpg')
 
 #from transformers import 
@@ -37,7 +41,9 @@ def load_zeroshot_classifier():
     
 def get_summarizer(summarizer, sequence:str, maximum_tokens:int, minimum_tokens:int):
 	output = summarizer(sequence, num_beams=4, max_length=maximum_tokens, min_length=minimum_tokens, do_sample=False)
-	return output[0].get('summary_text')
+	
+    metric.add_batch(predictions=output[0].get('summary_text'), references=sequence)
+    return output[0].get('summary_text')
 
 
 
@@ -82,8 +88,12 @@ output_text = []
 
 if submit_button:
     with st.spinner('On summarizing !...wait a second please..'):
-        output_text.append(get_summarizer(summarizer_1, text_input, 150, 5))
-        output_text.append(get_summarizer(summarizer_2, text_input, 150, 5))
+        
+        get_1 = get_summarizer(summarizer_1, text_input, 150, 5)
+        get_2 = get_summarizer(summarizer_2, text_input, 150, 5)
+        
+        output_text.append(get_1)
+        output_text.append(get_2)
         #output_text.append(get_summarizer(summarizer_3, text_input, 150, 5))
    
     
@@ -92,6 +102,7 @@ if submit_button:
     for i in range(2):
         st.markdown("**"+ plms[i] +"s Output:  **  ")
         st.text(output_text[i])
+        st.text("final ROUGE score: "+ metric.compute())
         st.success(f"{i+1} of 3 are done!")
         
     st.success("Congrats!!! ALL DONE!")

requirements.txt

@@ -1,2 +1,3 @@
 transformers
 torch
+datasets