change

app.py

@@ -1,13 +1,9 @@
 import gradio as gr
 import re
 import textstat
-from transformers import pipeline
 from langdetect import detect
 
-# Load a summarization model
-summarizer = pipeline("summarization")
-
-def text_analysis_and_summarization(text):
+def text_analysis(text):
     # Analyze text: word count, character count, language detection, and readability
     words = re.findall(r'\w+', text.lower())
     sentences = re.split(r'[.!?]+', text)
@@ -17,24 +13,20 @@ def text_analysis_and_summarization(text):
     reading_ease = textstat.flesch_reading_ease(text)
     language = detect(text)
 
-    # Summarize text using the transformer model
-    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)[0]['summary_text']
-
     # Format the results
     return {
         "Language": language,
         "Sentences": num_sentences,
         "Words": num_words,
         "Characters": num_chars,
-        "Readability (Flesch Reading Ease)": reading_ease,
-        "Summary": summary
+        "Readability (Flesch Reading Ease)": reading_ease
     }
 
-# Define an interface for text analysis and summarization
-text_analysis_and_summarization_interface = gr.Interface(fn=text_analysis_and_summarization, 
-                                                        inputs=gr.Textbox(lines=4, placeholder="Type something here..."), 
-                                                        outputs=gr.JSON(label="Text Analysis and Summarization"))
+# Define an interface for text analysis
+text_analysis_interface = gr.Interface(fn=text_analysis, 
+                                       inputs=gr.Textbox(lines=4, placeholder="Type something here..."), 
+                                       outputs=gr.JSON(label="Text Analysis"))
 
 # Launch the interface
 if __name__ == "__main__":
-    text_analysis_and_summarization_interface.launch()
+    text_analysis_interface.launch()