Update app.py

app.py

@@ -307,8 +307,7 @@ class GradioInference:
     def from_article(self, article, progress=gr.Progress()):
         """
         Call the Gradio Inference python class.
-        Uses it directly the Whisper model to perform Automatic Speech Recognition (i.e Speech-to-Text).
-        Once the function has the transcription of the video it proccess it to obtain:
+        Acepts the user's text imput, then it performs: 
             - Summary: using Facebook's BART transformer.
             - KeyWords: using VoiceLabT5 keyword extractor.
             - Sentiment Analysis: using Hugging Face's default sentiment classifier
@@ -320,14 +319,14 @@ class GradioInference:
         
         # Perform summarization on the transcription
         transcription_summary = self.bart_summarizer(
-            results["text"], max_length=150, min_length=30, do_sample=False, truncation=True
+            article, max_length=150, min_length=30, do_sample=False, truncation=True
         )
         
         #### Resumen multilingue
         WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
         
         input_ids_sum = self.mt5_tokenizer(
-            [WHITESPACE_HANDLER(results["text"])],
+            [WHITESPACE_HANDLER(article)],
             return_tensors="pt",
             padding="max_length",
             truncation=True,
@@ -352,7 +351,7 @@ class GradioInference:
         
         # Extract keywords using VoiceLabT5
         task_prefix = "Keywords: "
-        input_sequence = task_prefix + results["text"]
+        input_sequence = task_prefix + article
         
         input_ids = self.keyword_tokenizer(
             input_sequence, 
@@ -387,26 +386,16 @@ class GradioInference:
         progress(0.90, desc="Generating Wordcloud")
         # WordCloud object
         wordcloud = WordCloud(colormap = "Oranges").generate(
-            results["text"]
+            article
         )
         wordcloud_image = wordcloud.to_image()
 
-        if lang == "english" or lang == "none":
-            return (
-                results["text"],
-                transcription_summary[0]["summary_text"],
-                formatted_keywords,
-                formatted_sentiment,
-                wordcloud_image,
-            )
-        else:
-            return (
-                results["text"],
-                summary,
-                formatted_keywords,
-                formatted_sentiment,
-                wordcloud_image,
-            )
+        return (
+            transcription_summary[0]["summary_text"],
+            formatted_keywords,
+            formatted_sentiment,
+            wordcloud_image,
+        )
 
 
 gio = GradioInference()
@@ -428,7 +417,7 @@ with block as demo:
         </div>
         """
     )
-    with gr.Group(spacing_size="md", radius_size="md"):
+    with gr.Group():
         with gr.Tab("From YouTube 📹"):
             with gr.Box():