Update app.py

app.py

@@ -20,7 +20,7 @@ def predict_pptx_content(file_path):
     cleaned_text = re.sub(r'\s+', ' ', extracted_text)
 
     classifier = pipeline("text-classification", model="Ahmed235/roberta_classification")
-    summarizer = pipeline("summarization", model="Falconsai/text_summarization")
+    #summarizer = pipeline("summarization", model="Falconsai/text_summarization")
 
     result = classifier(cleaned_text)[0]
     predicted_label = result['label']
@@ -28,8 +28,8 @@ def predict_pptx_content(file_path):
 
     prediction = {
         "Predicted Label": predicted_label,
-        "Evaluation": f"Evaluate the topic according to {predicted_label} is: {predicted_probability}",
-        "Summary": summarizer(cleaned_text, max_length=80, min_length=30, do_sample=False)
+        "Evaluation": f"Evaluate the topic according to {predicted_label} is: {predicted_probability}"
+        #"Summary": summarizer(cleaned_text, max_length=80, min_length=30, do_sample=False)
     }
 
     return prediction
@@ -38,7 +38,7 @@ def predict_pptx_content(file_path):
 iface = gr.Interface(
     fn=predict_pptx_content,
     inputs=gr.File(type="filepath", label="Upload PowerPoint (.pptx) file"),
-    outputs=["text", "text", "text"],  # Predicted Label, Evaluation, Summary
+    outputs=["text", "text"],  # Predicted Label, Evaluation, Summary
     live=False,  # Change to False for one-time analysis
     title="<h1 style='color: lightgreen; text-align: center;'>PPTX Analyzer</h1>",
 )