Create app.py

app.py

@@ -0,0 +1,38 @@
+# inference.py
+
+from pptx import Presentation
+import re
+from transformers import pipeline
+
+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)
+
+def main():
+    file_path = "path/to/your/powerpoint.pptx"  # Specify the path to your PowerPoint file
+
+    extracted_text = extract_text_from_pptx(file_path)
+    cleaned_text = re.sub(r'\s+', ' ', extracted_text)
+
+    print(cleaned_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']
+
+    print("Predicted Label:", predicted_label)
+    print(f"Evaluate the topic according to {predicted_label} is: {predicted_probability}")
+    print(summarizer(cleaned_text, max_length=80, min_length=30, do_sample=False))
+
+if __name__ == "__main__":
+    main()