Update app.py

app.py

@@ -1,3 +1,8 @@
+# Initialize the space
+summarizeryt = pipeline("summarization", model="facebook/bart-large-cnn")
+summarizerbg = pipeline("summarization")
+summarizertx = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
+
 # How to use: YTVideoToText("https://www.youtube.com/watch?v=jQL0ZeHtXFc")
 def YTVideoToText(video_link):
     # installing & importing libraries
@@ -13,9 +18,7 @@ def YTVideoToText(video_link):
     for i in transcript:
         result += ' ' + i['text']
 
-    # summarize text
-    summarizerfb = pipeline("summarization", model="facebook/bart-large-cnn")
-    
+    # summarize text    
     num_iters = int(len(result)/1000)
     summarized_text = []
     summarized_text2 = []
@@ -23,7 +26,7 @@ def YTVideoToText(video_link):
         start = 0
         start = i * 1000
         end = (i + 1) * 1000
-        out = summarizerfb(result[start:end], max_length=130, min_length=30, do_sample=False)
+        out = summarizeryt(result[start:end], max_new_tokens=130, min_length=30, do_sample=False)
         out = out[0]
         out = out['summary_text']
         summarized_text.append(out)
@@ -40,9 +43,6 @@ def postSummaryWithBart(blog_link):
     from bs4 import BeautifulSoup
     import requests
 
-    # loading summarization pipeline
-    summarizer = pipeline("summarization")
-
     # getting our blog post
     URL = blog_link
     r = requests.get(URL)
@@ -76,7 +76,7 @@ def postSummaryWithBart(blog_link):
         chunks[chunk_id] = ' '.join(chunks[chunk_id])
 
     # summarizing text
-    res = summarizer(chunks, max_length=70, min_length=30, do_sample=False)
+    res = summarizerbg(chunks, max_new_tokens=1024, min_length=30, do_sample=False)
     text = ''.join([summ['summary_text'] for summ in res])
 
     # returning summary
@@ -88,15 +88,14 @@ def abstractiveSummaryWithPegasus(words):
     # importing & loading model
     from transformers import PegasusForConditionalGeneration, PegasusTokenizer
     tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
-    model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
 
     # perform summarization
     tokens = tokenizer(words, truncation=True, padding="longest", return_tensors="pt")
-    summary = model.generate(**tokens)
+    summary = summarizertx.generate(**tokens)
     actual_summ = tokenizer.decode(summary[0])
 
     # returning summary
-    print(actual_summ)
+    return actual_summ
 
 
 # Main logic of the program
@@ -122,29 +121,38 @@ with gr.Blocks() as ui:
                 label="URI à résumer",
                 max_lines=1,
                 placeholder="https://youtube|website.ext",
+                api_name="uri"
             )
             TRANSCRIPT = gr.Textbox(
-                label="URI à résumer",
+                label="Transcript à résumer",
                 lines=10,
                 placeholder="https://youtube|website.ext",
+                api_name="transcript"
             )
             RESUME = gr.Textbox(
-                label="URI à résumer",
+                label="Résumé",
                 lines=10,
                 interactive=False,
                 placeholder="https://youtube|website.ext",
+                api_name="resume"
             )
         with gr.Column():
-            MODE = gr.Radio(choices=["Youtube", "Blog", "Text"])
+            MODE = gr.Radio(
+                label="Mode pour URI", 
+                choices=["Youtube", "Blog"],
+                api_name="mode"
+            )
             gr.Button("Process URI").click(
                 fn=process,
                 inputs=[URI, MODE],
-                outputs=[TRANSCRIPT, RESUME]
+                outputs=[TRANSCRIPT, RESUME],
+                api_name="process_uri"
             )
             gr.Button("Process TEXT").click(
                 fn=abstractiveSummaryWithPegasus,
                 inputs=[TRANSCRIPT],
-                outputs=[RESUME]
+                outputs=[RESUME],
+                api_name="process_text"
             )
     
 #translator_fr = gr.Interface.load("huggingface/Helsinki-NLP/opus-mt-fr-en")