Update app.py

app.py

@@ -4,54 +4,63 @@ from pptx import Presentation
 import re
 import json
 
-# Create a text classification pipeline
+# Load the classification and summarization pipelines
 classifier = pipeline("text-classification", model="Ahmed235/roberta_classification", tokenizer="Ahmed235/roberta_classification")
 summarizer = pipeline("summarization", model="Falconsai/text_summarization")
 
+# Cache for model weights
+classification_model_loaded = False
+summarization_model_loaded = False
+
+def load_models():
+    global classifier, summarizer, classification_model_loaded, summarization_model_loaded
+    if not classification_model_loaded:
+        classifier = pipeline("text-classification", model="Ahmed235/roberta_classification", tokenizer="Ahmed235/roberta_classification")
+        classification_model_loaded = True
+    if not summarization_model_loaded:
+        summarizer = pipeline("summarization", model="Falconsai/text_summarization")
+        summarization_model_loaded = True
+
+# Extract text from PowerPoint
 def extract_text_from_pptx(file_path):
-    presentation = Presentation(file_path)
-    text = []
-    for slide_number, slide in enumerate(presentation.slides, start=1):
-        for shape in slide.shapes:
-            if hasattr(shape, "text"):
-                text.append(shape.text)
-    return "\n".join(text)
+    try:
+        presentation = Presentation(file_path)
+        text = []
+        for slide in presentation.slides:
+            for shape in slide.shapes:
+                if hasattr(shape, "text"):
+                    text.append(shape.text)
+        return "\n".join(text)
+    except Exception as e:
+        print(f"Error extracting text from PowerPoint: {e}")
+        return ""
 
+# Limit text length
 def limit_text_length(text, max_length=512):
-    # Truncate or limit the text length
     return text[:max_length]
 
+# Predict content from PowerPoint
 def predict_pptx_content(file_path):
     try:
+        load_models()  # Load models if not loaded already
         extracted_text = extract_text_from_pptx(file_path)
         cleaned_text = re.sub(r'\s+', ' ', extracted_text)
-        
-        
         limited_text = limit_text_length(cleaned_text)
-
-    
         result = classifier(limited_text)
-
         predicted_label = result[0]['label']
         predicted_probability = result[0]['score']
         summary = summarizer(cleaned_text, max_length=1000, min_length=30, do_sample=False)[0]['summary_text']
-        
         output = {
             "predicted_label": predicted_label,
             "evaluation": predicted_probability,
             "summary": summary
         }
-        
-        output_dict = json.dumps(output, indent = 3)
-
-        return output_dict 
-
+        return json.dumps(output, indent=3)
     except Exception as e:
-        # Log the error details
-        print(f"Error in predict_pptx_content: {e}")
+        print(f"Error predicting content from PowerPoint: {e}")
         return {"error": str(e)}
 
-
+# Gradio interface
 iface = gr.Interface(
     fn=predict_pptx_content,
     inputs=gr.File(type="filepath", label="Upload PowerPoint (.pptx) file"),