Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 from transformers import pipeline, AutoTokenizer
 import nltk
 from nltk.tokenize import sent_tokenize
+import time
 
 # Download NLTK data
 nltk.download('punkt')
@@ -23,8 +24,9 @@ summarization_models = {
 # Initialize tokenizer
 tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
 
-# Initialize summarization pipelines
-summarizers = {name: pipeline("summarization", model=model) for name, model in summarization_models.items()}
+# Helper function to initialize summarization pipeline
+def get_summarizer(model_name):
+    return pipeline("summarization", model=model_name)
 
 # Initialize translation pipeline
 def get_translator(language):
@@ -60,11 +62,12 @@ def summarize_text(text, model_name):
     if len(text) < 200:  # Adjust the threshold as needed
         print("Input text is too short for summarization. Please provide longer text.")
         return ""
+    summarizer = get_summarizer(model_name)
     chunks = split_text(text)
     summaries = []
     for chunk in chunks:
         try:
-            summary = summarizers[model_name](chunk, max_length=150, min_length=20, do_sample=False)[0]['summary_text']
+            summary = summarizer(chunk, max_length=150, min_length=20, do_sample=False)[0]['summary_text']
             summaries.append(summary)
         except Exception as e:
             print(f"Error summarizing chunk: {chunk}\nError: {e}")
@@ -83,6 +86,7 @@ def translate_text(text, language):
     return text
 
 def process_text(input_text, model, language):
+    start_time = time.time()
     print(f"Input text: {input_text[:500]}...")  # Show only the first 500 characters for brevity
     summary = summarize_text(input_text, model)
     if not summary:
@@ -96,6 +100,8 @@ def process_text(input_text, model, language):
     print(f"Bullet Points: {bullet_points}")
     translated_text = translate_text(bullet_points, language)
     print(f"Translated Text: {translated_text}")
+    end_time = time.time()
+    print(f"Processing time: {end_time - start_time} seconds")
     return bullet_points, translated_text
 
 def generate_bullet_points(summary):
@@ -148,4 +154,5 @@ iface.launch()
 
 
 
+