Update app.py

app.py

@@ -16,8 +16,29 @@ def extract_text_from_pptx(file_path):
     return "\n".join(text)
 
 def predict_pptx_content(file_path):
-    extracted_text = extract_text_from_pptx(file_path)
-    cleaned_text = re.sub(r'\s+', ' ', extracted_text)
+    print(f"File path received: {file_path}")
+    try:
+        extracted_text = extract_text_from_pptx(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")
+
+        result = classifier(cleaned_text)[0]
+        predicted_label = result['label']
+        predicted_probability = result['score']
+
+        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)
+        }
+
+        return prediction
+    except Exception as e:
+        print(f"Error processing file: {e}")
+        return {"error": str(e)}
+
 
     classifier = pipeline("text-classification", model="Ahmed235/roberta_classification")
     #summarizer = pipeline("summarization", model="Falconsai/text_summarization")