Update app.py

app.py

@@ -1,11 +1,16 @@
 import gradio as gr
 import spacy
+from spacy.cli import download
 from transformers import T5Tokenizer, AutoModelForSeq2SeqLM, pipeline
 
-# Load spaCy for answer extraction
-nlp_spacy = spacy.load("en_core_web_sm")
+# 🛠 Ensure spaCy model is available
+try:
+    nlp_spacy = spacy.load("en_core_web_sm")
+except OSError:
+    download("en_core_web_sm")
+    nlp_spacy = spacy.load("en_core_web_sm")
 
-# Load the T5 model
+# Load T5 model for question generation
 model_name = "valhalla/t5-base-e2e-qg"
 tokenizer = T5Tokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
@@ -13,11 +18,10 @@ qg_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
 
 def extract_answers(context):
     doc = nlp_spacy(context)
-    answers = list(set([ent.text for ent in doc.ents]))  # Named entities
+    answers = list(set([ent.text for ent in doc.ents]))
     if not answers:
-        # Fallback to noun chunks if no named entities
         answers = list(set([chunk.text for chunk in doc.noun_chunks if len(chunk.text.split()) > 1]))
-    return answers[:5]  # Limit to 5 for now
+    return answers[:5]
 
 def generate_questions_answers(context):
     answers = extract_answers(context)
@@ -31,14 +35,13 @@ def generate_questions_answers(context):
     
     return "\n\n".join(qa_pairs) if qa_pairs else "Could not generate any QA pairs."
 
-# Gradio UI
 iface = gr.Interface(
     fn=generate_questions_answers,
     inputs=gr.Textbox(lines=6, label="Paste Paragraph"),
     outputs="textbox",
-    title="Question & Answer Generator",
-    description="Extract answers and generate questions from any paragraph using spaCy + T5 model."
+    title="Q&A Generator from Paragraph",
+    description="Get both questions and answers using spaCy + T5!"
 )
 
-iface.queue()  # Enables /api/predict endpoint
+iface.queue()
 iface.launch()