Update app.py

app.py

@@ -1,17 +1,8 @@
+import gradio as gr
 from pptx import Presentation
 import re
-import gradio as gr
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
-import torch.nn.functional as F
 from transformers import pipeline
 
-# Load the pre-trained model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("Ahmed235/roberta_classification")
-model = AutoModelForSequenceClassification.from_pretrained("Ahmed235/roberta_classification")
-device = torch.device("cpu")
-model = model.to(device)  # Move the model to the CPU
-
 # Create a summarization pipeline
 summarizer = pipeline("summarization", model="Falconsai/text_summarization")
 
@@ -26,30 +17,13 @@ def extract_text_from_pptx(file_path):
 
 def predict_pptx_content(file_path):
     try:
-        str(extracted_text) = extract_text_from_pptx(file_path)
-        str(cleaned_text) = re.sub(r'\s+', ' ', extracted_text)
-
-        # Tokenize and encode the cleaned text
-        input_encoding = tokenizer(cleaned_text, truncation=True, padding=True, return_tensors="pt")
-        input_encoding = {key: val.to(device) for key, val in input_encoding.items()}  # Move input tensor to CPU
-
-        # Perform inference
-        with torch.no_grad():
-            outputs = model(**input_encoding)
-            logits = outputs.logits
-
-        probabilities = F.softmax(logits, dim=1)
-
-        predicted_label_id = torch.argmax(logits, dim=1).item()
-        predicted_label = model.config.id2label[predicted_label_id]
-        predicted_probability = probabilities[0][predicted_label_id].item()
+        extracted_text = extract_text_from_pptx(file_path)
+        cleaned_text = re.sub(r'\s+', ' ', extracted_text)
 
         # Summarize the cleaned text
         summary = summarizer(cleaned_text, max_length=80, min_length=30, do_sample=False)[0]['summary_text']
 
         prediction = {
-            "Predicted Label": predicted_label,
-            "Evaluation": f"Evaluate the topic according to {predicted_label} is: {predicted_probability}",
             "Summary": summary
         }
 
@@ -64,10 +38,10 @@ 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",  # Only output the summary
     live=False,  # Change to True for one-time analysis
     title="<h1 style='color: lightgreen; text-align: center;'>HackTalk Analyzer</h1>",
 )
 
 # Deploy the Gradio interface
-iface.launch(share=True)
+iface.launch(share=True)