Update q_generator1.py

q_generator1.py

@@ -1,34 +1,34 @@
-from transformers import T5Tokenizer, AutoModelForSeq2SeqLM, pipeline
-
-class QGenerator:
-    def __init__(self):
-        tokenizer = T5Tokenizer.from_pretrained("valhalla/t5-small-qg-hl", use_fast=False)
-        model = AutoModelForSeq2SeqLM.from_pretrained("valhalla/t5-small-qg-hl")
-        self.qg = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
-
-    def split_sentences(self, text):
-        # Simple sentence splitting (for better results, use nltk or spacy)
-        return [s.strip() for s in text.split('.') if s.strip()]
-
-    def chunk_text(self, text, chunk_size=512):
-        return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
-
-    def generate(self, text, max_questions=5):
-        questions = []
-        sentences = self.split_sentences(text)
-
-        for sentence in sentences:
-            if len(questions) >= max_questions:
-                break
-
-            input_text = f"generate question: {sentence} </s>"
-            try:
-                result = self.qg(input_text, max_length=64, num_return_sequences=1)[0]
-                question = result["generated_text"]
-                if question and question not in questions:
-                    questions.append(question)
-            except Exception as e:
-                print("Error generating question:", e)
-                continue
-
-        return questions
+from transformers import T5Tokenizer, AutoModelForSeq2SeqLM, pipeline
+
+class QGenerator:
+    def __init__(self):
+        tokenizer = T5Tokenizer.from_pretrained("valhalla/t5-small-qg-hl", use_fast=False)
+        model = AutoModelForSeq2SeqLM.from_pretrained("valhalla/t5-small-qg-hl")
+        self.qg = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
+
+    def split_sentences(self, text):
+        # Simple sentence splitting (for better results, use nltk or spacy)
+        return [s.strip() for s in text.split('.') if s.strip()]
+
+    def chunk_text(self, text, chunk_size=512):
+        return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
+
+    def generate(self, text, max_questions=1):
+        questions = []
+        sentences = self.split_sentences(text)
+
+        for sentence in sentences:
+            if len(questions) >= max_questions:
+                break
+
+            input_text = f"generate question: {sentence} </s>"
+            try:
+                result = self.qg(input_text, max_length=64, num_return_sequences=1)[0]
+                question = result["generated_text"]
+                if question and question not in questions:
+                    questions.append(question)
+            except Exception as e:
+                print("Error generating question:", e)
+                continue
+
+        return questions