Update app.py

app.py

@@ -25,34 +25,40 @@ 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)
+    try:
+        extracted_text = extract_text_from_pptx(file_path)
+        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
+        # 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
+        # Perform inference
+        with torch.no_grad():
+            outputs = model(**input_encoding)
+            logits = outputs.logits
 
-    probabilities = F.softmax(logits, dim=1)
+        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()
+        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()
 
-    # Summarize the cleaned text
-    summary = summarizer(cleaned_text, max_length=80, min_length=30, do_sample=False)[0]['summary_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
-    }
+        prediction = {
+            "Predicted Label": predicted_label,
+            "Evaluation": f"Evaluate the topic according to {predicted_label} is: {predicted_probability}",
+            "Summary": summary
+        }
 
-    return prediction
+        return prediction
+
+    except Exception as e:
+        # Log the error details
+        print(f"Error in predict_pptx_content: {e}")
+        return {"error": str(e)}
 
 # Define the Gradio interface
 iface = gr.Interface(