Update app.py

app.py

@@ -2,9 +2,11 @@ import gradio as gr
 from transformers import pipeline
 from pptx import Presentation  # Import the Presentation class
 import re
+
 # Create a text classification pipeline
 classifier = pipeline("text-classification", model="Ahmed235/roberta_classification", tokenizer="Ahmed235/roberta_classification")
 summarizer = pipeline("summarization", model="Falconsai/text_summarization")
+
 def extract_text_from_pptx(file_path):
     presentation = Presentation(file_path)
     text = []
@@ -25,8 +27,9 @@ def predict_pptx_content(file_path):
         predicted_probability = result[0]['score']
         summary = summarizer(extracted_text, max_length=80, min_length=30, do_sample=False)[0]['summary_text']
         prediction = {
+            "Summary": summary,
             "Evaluation": f"Evaluate the topic according to {predicted_label} is: {predicted_probability}",
-            "Summary": summary
+            "Predicted Label": predicted_label,
         }
 
         return prediction
@@ -40,7 +43,7 @@ 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"],  # Predicted Label, Evaluation
+    outputs=[gr.Textbox("Summary"), gr.Textbox("Evaluation"), gr.Textbox("Predicted Label")],
     live=False,  # Change to True for one-time analysis
     title="<h1 style='color: lightgreen; text-align: center;'>HackTalk Analyzer</h1>",
 )