Update app.py

app.py

@@ -1,28 +1,23 @@
 import gradio as gr
 from transformers import pipeline
 
-# Use a different language model: GPT-Neo (125M)
-generator = pipeline('text-generation', model='EleutherAI/gpt-neo-125M')
+# Load a question-answering model instead of a text generator
+qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
 
-def generate_text(prompt):
-    # Adjust parameters to reduce repetition and shorten output
-    generated = generator(
-        prompt,
-        max_length=30,           # Limit the output length
-        do_sample=True,          # Enable sampling for varied responses
-        temperature=0.2,         # Lower temperature for less randomness
-        repetition_penalty=1.5,  # Penalize repetitive tokens
-        no_repeat_ngram_size=2   # Prevent repeating 2-word sequences
-    )
-    return generated[0]['generated_text']
+def get_answer(question):
+    context = """
+    London is the capital of the United Kingdom. The UK consists of England, Scotland, Wales, and Northern Ireland.
+    """
+    answer = qa_pipeline(question=question, context=context)
+    return answer["answer"]
 
-# Create the Gradio interface
+# Create Gradio Interface
 iface = gr.Interface(
-    fn=generate_text,
+    fn=get_answer,
     inputs="text",
     outputs="text",
-    title="Simple LLM with GPT-Neo & Gradio",
-    description="Enter a prompt and get a concise response using GPT-Neo."
+    title="Ask Any Question",
+    description="Ask factual questions and get precise answers."
 )
 
 iface.launch()